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zenodo48/100

Survey Data on Apple Farming in China: Agronomic Management, Advisory Channels, and Profitability

<p>The Survey results and original data are stored in a directory structured as the table:</p> <table style="width: 100%; height: 223.938px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"><strong>Type</strong></td> <td style="width: 21.7597%; height: 19.5938px;"><strong>File Name</strong></td> <td style="width: 59.4134%; height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 47.5938px;"> <td style="width: 18.8269%; height: 47.5938px;"> <p>Raw_Data_Spearate_Source</p> </td> <td style="width: 21.7597%; height: 47.5938px;">raw_data_english_telephone.xlsx</td> <td style="width: 59.4134%; height: 47.5938px;">Translated data in English corresponding to the Chinese telephone interview data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">&nbsp;</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_wechat.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Translated data in English corresponding to the Chinese Wechat Mini Program data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Raw_Data_Total</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_total.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Combined data from raw_data_english_telephone.xlsx and raw_data_english_wechat.xlsx</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 18.8269%; height: 39.1875px;">Apple_Statistical_Data</td> <td style="width: 21.7597%; height: 39.1875px;">apple_2022_statistical_data.xlsx</td> <td style="width: 59.4134%; height: 39.1875px;">Contains data on apple planting area, production, and yield sourced from the China Statistics Bureau, along with the number of survey questionnaires collected from various provinces</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">&nbsp;</td> <td style="width: 21.7597%; height: 19.5938px;">province_eng.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Contains the English version of the provinces' names</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Map_Boundary_line</td> <td style="width: 21.7597%; height: 19.5938px;">national_boundary_line.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The country boundaires of China</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">&nbsp;</td> <td style="width: 21.7597%; height: 19.5938px;">province_boundary.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The province boundaries of China</td> </tr> </tbody> </table> <p>For privacy reasons, personally identifiable information such as respondents&rsquo; names, telephone numbers, and specific addresses has been anonymized in the dataset. The file <em>raw_data_english_total.xlsx</em> contains 96 columns, each corresponding to a question in the questionnaire.</p>

opencc-by-4.0Aug 2024View details →
zenodo48/100

Datasets and R source code of manuscript "Parasites make hosts more profitable but less available to predators"

<p>Data about experimentations of DIV-1 (virus) infection on Daphnia magna.</p> <p>Linked article:&nbsp;Parasites make hosts more profitable but less available to predators</p>

opencc-by-4.0Feb 2022View details →
zenodo44/100

Dataset for the article "Frequency regulation with storage: On losses and profits"

<p>This dataset complements the article <em>"Frequency regulation with storage: On losses and profits"</em> by Dirk Lauinger, Fran&ccedil;ois Vuille, and Daniel Kuhn, available at <a href="https://doi.org/10.1016/j.ejor.2024.03.022">https://doi.org/10.1016/j.ejor.2024.03.022</a> and at <a href="https://arxiv.org/pdf/2306.02987v2.pdf">https://arxiv.org/pdf/2306.02987v2.pdf</a>.&nbsp;</p> <p>The dataset contains the following files:</p> <p>1. <strong>Case_study.ipynb</strong>, which relies on the datafiles <strong>delta_10s.h5</strong>, <strong>pa.h5</strong>, <strong>pb.h5</strong>, <strong>pd.h5</strong>, and on the excel files <strong>conso_mix_RTE_2019.xls</strong> and <strong>ReserveAjustement_2019.xlsx</strong> to construct all figures in the article. The jupyter notebook is also available at <a href="https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage">https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage</a>.</p> <p>2. <strong>delta_10s.h5</strong>, which contains normalized frequency deviations with a 10s resolution from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw frequency measurement data in <strong>build_delta_10s.rar</strong>. The frequency measurements are taken from the website of the French transmission system operator RTE: <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&amp;type=network_frequencies">https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&amp;type=network_frequencies</a> (link live as of 7 June 2023).</p> <p>3. <strong>pa.h5</strong>, which contains availability prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The availability price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&amp;category=market&amp;type=balancing_capacity&amp;subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&amp;category=market&amp;type=balancing_capacity&amp;subType=procured_reserves</a> (link live as of 7 June 2023).</p> <p>4. <strong>pd.h5</strong>, which contains delivery prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The delivery price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&amp;category=market&amp;type=balancing_capacity&amp;subType=actived_offers">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&amp;category=market&amp;type=balancing_capacity&amp;subType=actived_offers</a> (link live as of 7 June 2023).</p> <p>5. <strong>pb.h5</strong>, which contains utility prices for a subscribed apparent power of 9kVA from the state regulated <em>"tarif bleu"</em> of EDF, the largest electricity provider in France. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. Current price data (including taxes and transportation fees) is available at <a href="https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf">https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf</a>. The French Energy Department publishes the electricity prices in the official French Government journal. The corresponding legal texts are accessible under the following links:<br>01/11/2014 - 31/07/2015: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637</a><br>01/08/2015 - 31/07/2016: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456</a><br>01/08/2016 - 31/07/2017: <a href="https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf">https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf</a><br>01/08/2017 - 31/01/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675</a><br>01/02/2018 - 31/07/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814</a><br>01/08/2018 - 31/05/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170</a><br>01/06/2019 - 01/08/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381</a><br>01/08/2019 - 31/12/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867</a><br>These prices exclude taxes and transportation fees. The <em>"Option Base"</em> offers a flat price throughout the day. The <em>"Option Heures Creuses"</em> has a higher price for peak (6:00-22:00) than off-peak (22:00-6:00) hours. EDF refers to peak hours as <em>"Heures Pleines (HP)"</em> and to off-peak hours as <em>"Heures Creuses (HC)"</em>.&nbsp; The prices of these options only change, when EDF changes its electricity tariffs, which is up to three times per year. Conversely, the <em>"Option Tempo"</em> is a pricing scheme in which each day is either a high-price (<em>"Rouge"</em>), medium-price (<em>"Blanc"</em>) or low-price (<em>"Bleu"</em>) day. The price level of each day is announced by 10:30 am on the previous day. RTE, the French transmission system operator, keeps track of the daily price levels (<a href="https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html">https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html</a>). The price data can be downloaded from RTE's eco2mix platform (<a href="https://www.rte-france.com/eco2mix/telecharger-les-indicateurs">https://www.rte-france.com/eco2mix/telecharger-les-indicateurs</a>).</p> <p>6. <strong>conso_mix_RTE_2019.xls</strong>, which contains total French electricity demand throughout the year 2019 with 15 minute resolution. This data is provided by RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&amp;type=short_term">https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&amp;type=short_term</a> (link live as of 27 March 2024).</p> <p>7. <strong>ReserveAjustement_2019.xlsx</strong>, which contains the quantities and availability price for four reserve products: "r&eacute;serve primaire" (primary frequency regulation, which we use in this article), "r&eacute;serve secondaire", "r&eacute;serve rapide", and "r&eacute;serve compl&eacute;mentaire", with 30 minute resolution throughout the year 2019. This data is provided RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&amp;type=balancing_capacity&amp;subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&amp;type=balancing_capacity&amp;subType=procured_reserves</a> (link live as of 27 March 2024).</p>

opennosl3.0Jun 2023View details →
zenodo44/100

Profitability and investment risk of Texan power system winterization

<p><strong>Profitability and investment risk of Texan power system winterization</strong></p> <p>This data repository contains interim and final results of the <a href="https://www.nature.com/articles/s41560-022-00994-y">paper </a>&ldquo;Profitability and investment risk of Texan power system winterization&rdquo; published in Nature Energy. Code used to generate these results can be found at <a href="https://github.com/inwe-boku/texas-power-outages">github</a></p> <p><strong>Abstract</strong></p> <p>A lack of winterization of power system infrastructure resulted in significant rolling blackouts in Texas in 2021 though debate about the cost of winterization continues. Here, we assess if incentives for winterization on the energy only market are sufficient. We combine power demand estimates with estimates of power plant outages to derive power deficits and scarcity prices. Expected profits from winterization of a large share of existing capacity are positive. However, investment risk is high due to the low frequency of freeze events, potentially explaining under-investment, as do high discount rates and uncertainty about power generation failure under cold temperatures. As the social cost of power deficits is one to two orders of magnitude higher than winterization cost, regulatory enforcement of winterization is welfare enhancing. Current legislation can be improved by emphasizing winterization of gas power plants and infrastructure.</p> <p><strong>Date and time format</strong></p> <p>Please observe that we omit the date column from the description of columns below for all datasets. The ERA5 data in <strong>input/</strong> is in UTC, all other input datasets are in local Texas time (GMT-6). In <strong>interim</strong>, <em>temperatures/temppop/</em>, <em>temperatures/temp_gas_powerplant.csv</em>, <em>temperatures/temp_gas_outages.csv</em>, <em>temperatures/temp_coal_powerplant.csv</em>, <em>temperatures/temp_coal_outages.csv</em> and the wind power simulation output (<em>windpower/</em>) is in UTC. All other datasets are in local Texas time.</p> <p><strong>Data</strong></p> <p><strong>cache/</strong></p> <p>Data cache used by the scripts analyzing the extreme events: extreme temperatures, loss of load, their return periods, durations, maxima/minima (the cached files are not included, but can be generated with scripts/R/events.R)</p> <p><strong>figures/</strong></p> <p>Figures shown in the manuscript</p> <ul> <li><strong>raw_data</strong>: includes raw data for reproducing the figures in the main part of the manuscript</li> <li><strong>outage_model</strong>: figures representing the outage function as derived with our model</li> </ul> <p><strong>input/</strong></p> <p>Input data from external sources (with exception of orcd not included due to licensing issues)</p> <ul> <li><strong>ERA5_windspeeds_USA</strong>: available from the <a href="https://cds.climate.copernicus.eu/#!/home">CDS</a>. Download with scripts/download_era5_USA.py</li> <li><strong>gas_production</strong>: available from the Texas Railroad Commission in PDF format <a href="https://www.rrc.state.tx.us/media/qcpp3bau/2020-12-monthly-production-county-gas.pdf">here</a>. We extracted the data manually.</li> <li><strong>Load</strong>: available from ERCOT <a href="https://www.ercot.com/gridinfo/load">here</a></li> <li><strong>orcd</strong>: Scarcity prices as regulated by ERCOT. Manually extracted from <a href="https://doi.org/10.1016/j.enpol.2019.111143.334">J. Zarnikau et al.</a></li> <li><strong>outages</strong>: Outage Events from ERCOT with geo locations provided by Edgar Virguez <a href="https://bit.ly/EGOVADatabase">here</a> resulting from unit outage data provided by <a href="http://www.ercot.com/content/wcm/lists/226521/Unit_Outage_Data_20210312.xlsx">Ercot</a></li> <li><strong>population</strong>: population density data provided by arcgis <a href="https://www.arcgis.com/home/item.html?id=28bcaee42e2c4ace9fcb7c8b9ca524e7">here</a></li> <li><strong>powerplants</strong>: locations of power plants in Texas provided by the Energy Information Administration <a href="https://www.eia.gov/maps/layer_info-m.php">here</a></li> <li><strong>shp</strong>: shapefile of Texas state boundaries provided by arcgis <a href="https://gis-txdot.opendata.arcgis.com/datasets/texas-state-boundary-detailed">here</a></li> <li><strong>temperatures</strong>: available from the CDS <a href="https://cds.climate.copernicus.eu/#!/home">here</a>. Can be downloaded with script scripts/download_era5_TX_temp.py</li> <li><strong>USWTDB</strong>: US wind turbine data base provided by the US Geological Service <a href="https://eerscmap.usgs.gov/uswtdb/">here</a>. We used version: uswtdb_v3_3_20210114</li> <li><strong>GWA2</strong>: Global Wind Atlas Version 2.1 accessible <a href="https://silo1.sciencedata.dk/shared/cf5a3255eb87ca25b79aedd8afcaf570?path=%2FGWA2.1">here</a></li> </ul> <p><strong>interim/</strong></p> <p>Intermediary files from the analysis</p> <ul> <li><strong>bootstrap_year.csv</strong>: 30 randomly selected years between 1950 and 2021, 10,000 times used for bootstrapping<br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li><strong>bootstrap_year2020.csv</strong>: 30 randomly selected years between 1950 and 2020, 10,000 times used for bootstrapping without 2021 event<br> Generated by outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb</li> <li><strong>turbine_data.csv</strong>: turbine data for Texan wind turbines<br> Generated by scripts/prepare_TX_turbines.py<br> Columns: <ul> <li>capacity: turbine capacity (kW)</li> <li>height: turbine height (m)</li> <li>lon: longitude coordinate (&deg;)</li> <li>lat: latitude coordinate (&deg;)</li> <li>sp: specific power (W/m&sup2;)</li> <li>ind: running index</li> </ul> </li> </ul> <p><strong>interim/load/</strong></p> <p>Temperature dependent estimates of electricity load for Texas.</p> <ul> <li><strong>load_est70_LR24_temptrend_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load_est: load estimated for the period 1950-2021 assuming an average load level as in 2021 (MWh)</li> <li>temp: population weighted temperature (&deg;C)</li> </ul> </li> <li><strong>load_est10_LR24_temptrend_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load in period 2012-2021 as published by ERCOT (MWh)</li> <li>load_est: load estimated for period 2012-2021 considering time trend, i.e. this is a replication of the observed load without outages with our model for validation purposes (MWh)</li> </ul> </li> <li><strong>load_est9_LR24temptrend2021_Hook-8.csv</strong><br> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load in period 2004-2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for the years 2012-2020 for cross validation of load model. For training, the years 2012-2021 (2021/02 forecast) were used, except the predicted year, i.e. this is a replication of the observed load with our model for validation purposes. (MWh)</li> </ul> </li> <li><strong>load_est17_crossvalidation_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load 2004 - 2021/01 and load forecast 2021/02 as published by ERCOT (MWh)</li> <li>load_est: load estimated for cross validation for years 2004-2021, training years 2012-2020, trained with each year in traning period except modelled year with variable load level, i.e. this is a replication of the observed load with our model for validation purposes(MWh)</li> </ul> </li> <li><strong>load_est_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/load_estimation_LR24_temptrend_Hook-8.ipynb<br> Columns: <ul> <li>load: observed load 2004 - 2021 as published by ERCOT (MWh)</li> <li>load_est: years 2004-2021 predicted with a model which was trained for the years 2012-2020 considering time trend, i.e. this is a replication of the observed load without outages with our temperature dependent model with our model for validation purposes (MWh)</li> </ul> </li> </ul> <p><strong>interim/outages</strong></p> <ul> <li><strong>outages.feather</strong> Outage by minute of all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time.<br> Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section <strong>input/</strong>)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this minute (MW)</li> <li>cap_available: available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li><strong>outages-hourly.feather</strong> Hourly outages at all generation units in Texas in February 2021. Created by scripts/R/create-ercot-outage-timeseries.R In Texas local time.<br> Columns: <ul> <li>station: name of power plant</li> <li>unit: name of generation unit</li> <li>fullname: concatenated string of station and name</li> <li>dataset: ercot or edgar. ercot refers to the raw dataset provided by ERCOT, Edgar to the dataset provided by Edgar Virguez (for details see above in section <strong>input/</strong>)</li> <li>Longitude: Longitude of location of power plant</li> <li>Latitude: Latitude of location of power plant</li> <li>reduction: hourly reduction of capacity due to outage in this time step (MW)</li> <li>cap_available: hourly available capacity in this minute (MW)</li> <li>cap_max: maximum capacity of unit (MW)</li> </ul> </li> <li><strong>outages_reduction.csv</strong> Hourly outages per fuel (MW). We use these outages for COAL and GAS only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns: <ul> <li>NG: natural gas power plants</li> <li>WIND: wind power plants</li> <li>SOLAR: solar power plants</li> <li>ESR: energy storage resource</li> <li>HYDRO: hydropower plants</li> <li>NUCLEAR: nuclear power plants</li> </ul> </li> <li><strong>outages_reductionNorth.csv</strong> Hourly outages for the Northern part of Texas (latitude &gt; 30) (MW). We use these outages for WIND only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns as above.</li> <li><strong>outages_reductionSouth.csv</strong> Hourly outages for the Southern part area of Texas (latitude &lt;= 30) (MW). We use these outages for WIND only in the analysis.<br> Generated by notebooks/prepare_outages_NSsplit.ipynb<br> Columns as above.</li> </ul> <p><strong>interim/temperatures</strong></p> <ul> <li><strong>temppop</strong><br> Generated by scripts/calc_temppopC.py <ul> <li>contains population weighted temperatures for Texas, one file for each year (&deg;C).</li> </ul> </li> <li><strong>temp_coal_outage.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by coal power plants experiencing outages in February 2021 (&deg;C)</li> </ul> </li> <li><strong>temp_coal_powerplant.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all coal power plants (&deg;C)</li> </ul> </li> <li><strong>temp_gas_outage.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by gaspower plants experiencing outages in February 2021 (&deg;C)</li> </ul> </li> <li><strong>temp_gas_powerplant.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all gas power plants (&deg;C)</li> </ul> </li> <li><strong>temp_gasfields.csv</strong><br> Generated by notebooks/temperatures_NSsplit.ipynb<br> Columns: <ul> <li>t2m: temperature weighted by all gasfields (&deg;C)</li> </ul> </li> <li><strong>tempWP_NSsplit.csv</strong><br> Generated by notebooks/wp_temp_NSsplit.ipynb<br> Columns: <ul> <li>t2mSouth: temperatures weighted by all wind power plants in the South (&deg;C)</li> <li>t2mNorth: temperatures weighted by all wind power plants in the North (&deg;C)</li> </ul> </li> </ul> <p><strong>interim/thresholds</strong></p> <ul> <li><strong>thresh_total63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>total available capacity of gas, coal and wind considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li><strong>thresh_totalPP63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>total available capacity of gas, coal and wind, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> <li><strong>threshold_coal.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of coal power plants based on coal power plant temperatures (GW)</li> </ul> </li> <li><strong>threshold_gas.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of gas power plants based on gasfield temperatures (GW)</li> </ul> </li> <li><strong>threshold_gasPP.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>outages of gas power plants based on gas power plant temperatures (GW)</li> </ul> </li> <li><strong>threshold_gas_coal63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gasfield temperatures for gas outages (GW)</li> </ul> </li> <li><strong>threshold_gas_coalPP63.5GW.csv</strong><br> Generated by notebooks/outages_thresholds_gasPP_vs_gasfield_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>available capacity of gas and coal, considering outages, assuming gas power plant temperatures for gas outages (GW)</li> </ul> </li> </ul> <p><strong>interim/windpower</strong></p> <ul> <li><strong>cfTXh.csv</strong><br> Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb<br> Columns: <ul> <li>Capacity factors of simulated Texan wind power (dimensionless)</li> </ul> </li> <li><strong>wpTXh.csv</strong><br> Generated by notebooks/windpower_ERA5_GWA2_const_cap.ipynb<br> Columns:</li> <li>simulated Texan wind power generation (kWh)</li> </ul> <p><strong>output/</strong></p> <ul> <li><strong>marginal_revenue_coal_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization for coal (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_coal_LR24temptrend_Hook-8.csv</strong><br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_coal2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of coal (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_gas_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_gas_LR24temptrend_Hook-8.csv</strong><br> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_gas2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of gas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_wind_north_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_north_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_north2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Northern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>marginal_revenue_wind_south_10_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_10_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 10% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_south_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2021</li> </ul> </li> <li><strong>marginal_revenue_wind_south2020_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_reduced_bootstrap_2020_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>Marginal revenues from winterization of Southern Wind in Texas (bn$/GW winterized) with discount rate of 5% and deficit events up to 2020</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_all_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>delta_thresh_temp: change in outage temperature thresholds for all technologies (&deg;C)</li> <li>delta_rec_temp: change in recovery temperature thresholds for all technologies (&deg;C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_coal_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_coal_temp: outage temperature thresholds for coal (&deg;C)</li> <li>rec_coal_temp: recovery temperature thresholds for coal (&deg;C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_gas_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_gas_temp: outage temperature thresholds for gas (&deg;C)</li> <li>rec_gas_temp: recovery temperature thresholds for gas (&deg;C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_wind_north_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_windn_temp: outage temperature thresholds for wind north (&deg;C)</li> <li>rec_windn_temp:recovery temperature thresholds for wind north (&deg;C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> <li><strong>sensitivity_analysis_temperature_thresholds_wind_south_LR24temptrend_Hook-8.csv</strong> Generated by notebooks/outages_thresholds_sensitivity_LR24temptrend_Hook-8.ipynb<br> Columns: <ul> <li>thresh_winds_temp: outage temperature thresholds for wind south (&deg;C)</li> <li>rec_winds_temp:recovery temperature thresholds for wind south (&deg;C)</li> <li>number_of_events: estimated number of events within 7 decades</li> <li>after2004: years of events after 2004</li> <li>total_loss_TWh: total loss of load (TWh)</li> <li>max_loss_GW: maximum loss of load (GW)</li> <li>mean_loss_GW: average loss og load (GW)</li> <li>mean_revenue_mioUSD: average revenue (M$)</li> <li>rank_2021_event: rank of 2021 event in terms of loss of load</li> </ul> </li> </ul> <p>&nbsp;</p>

opencc-by-4.0Jan 2022View details →
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Data Extracted for the Systematic Literature Review on Non-profit Open data Intermediaries and their effects on Open data Usability Barriers

<p>The dataset contains the data extracted from the literature for the Systematic Literature Review and is referenced or used in the extended abstract titled&nbsp;"How do Non-profit Open data Intermediaries enhance Open data Usability? A Systematic Literature Review", submitted to the 18th International Symposium on Open Collaboration (Companion), September 6&ndash;10, 2022, Madrid, Spain.&nbsp;<a href="https://doi.org/10.1145/3555051.3555061" target="_blank" rel="noopener">https://doi.org/10.1145/3555051.3555061</a> &nbsp;</p>

opencc-by-4.0Dec 2021View details →
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Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design

<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanm&auml;ki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability&nbsp;maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map.&nbsp;</p> <p>Additional details can be found from the published article:&nbsp;<a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>

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Fig. 1 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract

Fig. 1: Map of the study area. The darker regions highlighted correspond to Spanish coastal Autonomous Communities.

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Fig. 4 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract

Fig. 4: Economic indicator by: A) the main fishing modalities: spearfishing, shore-fishing and boat-fishing and B) spearfishing diving approach.

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Fig. 3 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract

Fig. 3: Daily expenses. A) Spearfishing by diving approach and B) Boat fishing (angling) by type of vessel. The percentage of responses by modality in brackets.

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Production costs and their effect on the profitability of the SMES in Latin America

<p><span>The micro, small and medium-sized enterprises (SMEs) play a pivotal role in the Latin America economy, creating jobs, boosting local economic activity and contribute to the socio-economic development of the region. The purpose is to reflect on the current costs management prices, the common challenges faced by SMEs in the region, and effective strategies to improve the profitability and sustainability of these companies.</span></p>

opencc-by-4.0Jul 2024View details →
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Are there good ethical reasons why for profit publishers should no longer exist under the conditions of digital infrastructures? And what does this have to do with ethics as a reflexive discipline?

<p>Talk at the <a href="https://www.digital-philosophy.org/">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop (08/09/2022).</p>

opencc-by-4.0Sep 2022View details →
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Textiles: Polenka (2), "A Profitable Position"

The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Polina Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.26667.11043 Please contact us if you require seamless textures. Source: Objaverse 1.0 / Sketchfab

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Textiles: Kukushkina, "A Profitable Position"

The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Felisata Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. Please contact us if you require seamless textures. The cloth 3D model by atomov is available at Turbosquid. DOI: 10.13140/RG.2.2.35055.71848 Source: Objaverse 1.0 / Sketchfab

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Data from: Will trees or grasses profit from changing rainfall regimes in savannas?

<div> <p>• Increasing rainfall variability is widely expected under future climate change scenar- ios. How will savanna trees and grasses be affected by growing season dry spells and altered seasonality and how tightly coupled are tree-grass phenologies with rainfall?</p> <p>• We measured tree and grass responses to growing season dry spells and dry season rainfall. We also tested if the phenologies of 17 deciduous woody species and the Soil Adjusted Vegetation Index of grasses were related to rainfall between 2019 and 2023.</p> <p>• Tree and grass growth was significantly reduced during growing season dry spells. Tree growth was strongly related to growing season soil water potentials and limited to the wet season. Grasses can rapidly recover after growing season dry spells and grass evapotranspiration was significantly related to soil water potentials in both the wet and dry seasons. Tree leaf flushing commenced before the rainfall onset date with little subsequent leaf flushing. Grasses grew when moisture became available regardless of season.</p> </div> <p>• Our findings suggest that increased dry spell length and frequency in the growing season may slow down tree growth in some savannas, which together with longer growing seasons may allow grasses an advantage over C<sub>3</sub> plants that are advantaged by rising CO<sub>2</sub> levels.</p>

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Agronomic response and profitability of applying fractional doses of NPK fertilizer on cassava (Manihot esculenta Crantz) in Beni and Lubero territory, Democratic Republic of the Congo

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2024View details →
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CS6 - Sea urchin enhancement Norway - profitability model

<p>The dataset contains estimation on all costs&rsquo; parameters relevant to produce enhanced sea urchins in Norway. Sales price was estimated and calculations on profitability for three production scales and two different market situations are presented.</p>

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CS9 - Mussel offshore farm technology Sweden - profitability model 2022

<p>The dataset contains a standard profitability analysis of a mussel farm in Sweden utilising a novel farming method of tube and net technology with potential to be used in exposed and offshore areas. The profitability of the farm is analysed by estimating net present value and sensitivity analysis to calculate variations in financial returns under different conditions. Data on costs and productivity was classified as confidential for industrial property protection and will not uploaded in any public repository</p>

opencc-by-4.0Sep 2022View details →
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Textiles: Yulenka, "A Profitable Position"

The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Yulenka Kukushkina in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.28344.83202 Please contact us if you require seamless textures. Source: Objaverse 1.0 / Sketchfab

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Textiles: Zhadov, "A Profitable Position"

The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Vasily Zhadov in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. This work was supported by the Russian Historical Society and the History of the Fatherland Foundation under project titled "NashOstrovsky". DOI: 10.13140/RG.2.2.13245.33762 Please contact us if you require seamless textures. Source: Objaverse 1.0 / Sketchfab

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Textiles: Zhadov, "A Profitable Position"

The 3D model presents a digital reconstruction of historical textile materials for a theatrical costume for Vasily Zhadov in the play "A Profitable Position" (1856) by A.N. Ostrovsky. The authors used 2D scanning to capture the look of the surfaces, post processed the images in PixPlant, generated texture maps in Photoshop and put those on the 3D models in SubstancePainter. The authors of the 3D model are Aleksei Moskvin and Mariia Moskvina (Saint Petersburg State University of Industrial Technologies and Design). The authors thank prof. Victor Kuzmichev (Ivanovo State Polytechnic University) for providing data required for digitization of textiles. The actual costume that can be seen in the photo was made by faculty members and students of Ivanovo State Polytechnic University. Please contact us if you require seamless textures. The cloth 3D model by atomov is available at Turbosquid. DOI: 10.13140/RG.2.2.13245.33762 Source: Objaverse 1.0 / Sketchfab

opencc-byAug 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record