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514 results for “meteorological data”
Solar and meteorological data collected from the Radio Telescope Bras D'Eau station (Mauritius) by the ENERGY-Lab at the University of La Reunion between November 2015 and March 2023
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Bras Panon Moreau station (La Réunion) by the ENERGY-lab at the University of La Reunion between November 2010 and September 2014
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Saint Paul Le Carat station (La Réunion) by the ENERGY-Lab at the University of La Reunion between October 2022 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Saint Joseph Marie station (La Réunion) by the ENERGY-lab at the University of La Reunion between September 2013 and June 2015
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Daily meteorological data from January 2005 to February 18, 2022 from Viladrau Meteorological Station (Catalonia, Spain)
<p>Daily meteorological Data from Viladrau WS meteorological Station (Catalonia, Spain) from January 1, 2005 to February 18, 2022. Data includes the following variables: Date, Average, Maximal and Minimal daily temperatures, Maximal and Average of Relative humidity, Average of Atmospheric Pressure, Accumulated rain and maximal speed of wind. <strong>Surviving on the edge: present and future effects of climate warming on the common frog (Rana temporaria) population in the Montseny massif (NE Iberia).</strong></p>
Meteorlogial Data from Viladrau WS Meteorological Station. https://meteo.cat
<p>Meteorological Data from Viladrau WS Meteorological Station (Catalonia, Spain) for the 28 days previous of onset spawning of Rana temoraria. <strong>Surviving on the edge: present and future effects of climate warming on the common frog (Rana temporaria) population in the Montseny massif (NE Iberia). </strong></p>
Stationary comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer.</p>
Stationary comparison data and analysis between a new low-cost meteorological device and the MaRTy device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to the established high-end MaRTy device developed by Arizona State University's Sensable Heatscapes and Digital Environments (SHaDE) lab. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost HC2S3 Rotronic HygroClip2 for air temperature and humidity. However, the main purpose of the analysis was a comparison of the ability to predict Mean Radiant Temperature (MRT) as an essential component of thermal comfort. MRT for the low-cost device was calculated using the RayMan Pro software, while MRT for the MaRTy device is an output calculated directly by the device. </p>
Mobile comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides mobile comparison data from Tharandt and Dresden, Germany, which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer. The ability of each device to predict the Universal Thermal Climate Index (UTCI), calculated using the software RayMan Pro, was also compared.</p>
Mobile Meteorological Data from Dresden city center (June-August, 2022)
<p>This file contains meteorological data collected with built-for-purpose low-cost meteorological device for mobile thermal comfort mapping. Data was collected in the city center of Dresden, Germany, around a 3.1km route for the 19th of June, 23rd of June, 19th of July, 25th of July, 16th of August, and 17th of August, 2022 in the morning (06:00), midday (12:00), afternoon (15:30), and evening (20:00) of each of the 6 monitoring days. Each data file also contains thermal indices calculated using the software RayMan Pro.</p> <p>Folders are labeled by date e.g. 19th of June is written as 1906. Subfolders are labeled to correspond with the starting time of each measurement where A corresponds to 06:00, B is 12:00, C is 15:30, and D is 20:00. For each of these time periods (A, B, C, D) 2 laps of the 3.1km route were made. </p>
Fig.11 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.11. Ranking of meteorological factors by the quantity of significant positive or negative correlations with the number of sun-basking Emys orbicularis in the interval 8d"Nsbd"21.
Fig.3 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.3. Basic forms of sun-basking activity of Emys Fig.4. Basic forms of sun-basking activity of Emys orbicularis registered in the study: lying in the orbicularis registered in the study: heating under shadow. the sun in the shoal.
A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017
<p>The LSTC dataset contains land surface temperature data in China (about 9.6 million square kilometers of land) during the period of 2003-2017, in monthly temporal and 5600 m spatial resolution. It combines MODIS daily data, monthly data and meteorological station data to reconstruct the true LST under cloud coverage, and then the data performance is further improved by establishing a regression analysis model. The accuracy analysis shows that the reconstruction result is closely correlated with the in-situ measurements, with an average RMSE is 1.39 °C, an average MAE of 1.30 ° C and an R<sup>2</sup> of 0.97. The dataset can be used for the spatiotemporal evaluation of LST and will be useful for high temperature and drought studies and food security.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): Meteorological Measurement Data
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. Six automatic weather stations and 41 unventilated temperature sensors were distributed within the valley to gain insight into the spatial structure of the cold-air pool in Seefeld. The study site as well as locations and instrumentation of each station are described in Rudolph (2022) and Rauchöcker et al. (2024d). This upload contains meteorological measurement data associated with SEECAP. WRF simulations were performed for two nights, representing an ideal evolution of the cold-air pool (January 12 and January 13 2020) and a disrupted evolution (January 16 and January 17 2020), respectively. The output data of simulations with snow cover for the night between January 16 and January 17 2020 are published in Rauchöcker et al. (2024a) and in Rauchöcker et al. (2024c) for the night between January 12 and January 13 2020. Simulation output without snow cover is available for the night between January 16 and January 17 2020 in Rauchöcker et al. (2024b).</p> <h3><strong>Automatic Weather Stations</strong></h3> <p>Measurement data of the six automatic weather stations can be found in <em>momaa.zip</em>. The folder includes one file for each station (<em>MOMAA02.dat, MOMAA03.dat, MOMAA04.dat, MOMAA07.dat, MOMAA08.dat</em> and <em>MOMAA10.dat</em>). These stations measured temperature, pressure, humidity, net radiation, wind speed and wind direction at 1-min intervals. A figure showing the location of the different stations is included as well (<em>Stations.pdf</em>); the station names of the automatic weather stations are abreviated in the legend of that figure (e.g. M04 instead of MOMAA04). Incoming and outgoing longwave and shortwave radiation, latent heat flux and sensible heat flux were measured at MOMAA04 and MOMAA08. The radiation data can be found in <em>MOMAA04_rad.dat</em> and <em>MOMAA08_rad.dat</em> and eddy covariance data in <em>MOMAA04_turb.csv</em> and <em>MOMAA08_turb.csv</em>, respectively.</p> <h3><strong>Temperature Sensors</strong></h3> <p>Data from the unventilated temperature sensors can be found in <em>hobos.zip</em>, which contains a file for each sensor and the file names refer to the naming convention in <em>Stations.pdf</em>. Most sensors were located along the valley floor and along a ski jump on its southeastern slope. At nine locations, temperature sensors were mounted at two heights (1 m and 2 m above the ground). File names reflect that height by adding <em>_1m</em> or <em>_2m</em> to the file name (e.g. <em>A_1m.txt</em> and <em>A_2m</em>.txt). Locations that had only one sensor were named according to the station name (e.g. <em>M.txt</em>). The remaining sensors were used for vertical profiles at 3 different levels of a walk-up tower (<em>TOWER_2m.txt, TOWER_2ndfloor.txt</em> and <em>TOWER_top.txt</em>) and at a bridge (VP; labeled from <em>VP_050.txt</em> at 0.5m above the ground to <em>VP_630.txt</em> at 6.3m). A pseudo-vertical profile for the sensors along the slope of the valley, at the walk-up tower down to the lowest station in the upper basin (top to bottom, M03, S4, S3, S2, S1, M10, G, H, M04) can be found in <em>PseudoProfile_basin.mat</em>. </p>
Meteorological responses of carbon dioxide and methane fluxes in the terrestrial and aquatic ecosystems of a subarctic landscape [Data set]
<p>The data set contains carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes of boreal subarctic landscape and its ecosystems and ecotones, and ancillary meteorological and environmental data, measured at Kaamanen, northern Finland (69°8’ N, 27°16’ E; 155 m a.s.l.), during June 2017 - June 2019. The studied ecosystems and ecotones include: upland pine forest, fen, treed pine bog, sparsely treed pine bog, lakes and string top fen plant community.</p> <p>C_fluxes1b_Heiskanen_et_al_2022.csv includes quality screened, u* filtered and gap-filled eddy covariance ecosystem flux data and modelled pine bog and string top time series utilising eddy covariance and manual flux chamber measurements.</p> <p>C_fluxes2_Heiskanen_et_al_2022.csv includes quality screened daily average lake fluxes from mineral and organic sediment lakes.</p> <p>environmental_data_Heiskanen_et_al_2022.xlsx includes ancillary meteorological and environmental data.</p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
InSight's seismic and meteorological data related to the Martian convective vortices
<p><strong>Overview:</strong></p> <p>This repository includes the catalog related to Martian convective vortices observed by NASA's InSight mission. The detailed description is made in the JGR Planet paper entitled "Systematic catalog of Martian convective vortices observed by InSight" by Onodera et al. When you use the information in the catalog, please refer to the following citation.</p> <ul> <li>Onodera, K. et al. (2023), InSight's seismic and meteorological data related to the Martian convective vortices, Zenodo,<em><strong> </strong></em>doi:10.5281/zenodo.7801343<em><strong>.</strong></em></li> </ul> <p><strong>Files:</strong></p> <p>The first numbers in each file name correspond to the ID number included in the catalog file (InSight_CV_Catalog.pickle). All files are in csv format including time in Local Mean True Time in sol, respective observation records. If a number is missing, that means the corresponding data were not available on that sol (at least with the sampling rate we focused on in our paper).</p> <ul> <li><strong>InSight_CV_Catalog.pickle</strong>: It includes all estimated parameters presented by Onodera et al. (2023).</li> <li> <p><strong>PS.zip</strong>: 20 min long pressure data centered at the maximum pressure drop time (LMST, Pressure).</p> </li> <li> <p><strong>VBB_ACC.zip</strong>: 20 min long acceleration data centered at the maximum pressure drop time (LMST, Z comp., N comp., E comp.).</p> </li> <li> <p><strong>WSpeed_WDir_ATemp_calib.zip</strong>: 20 min long calibrated wind & air temperature data centered at the maximum pressure drop time (LMST, Wind speed, Wind direction, Air temperature).</p> </li> </ul>
HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation
<p>This repository contains <strong>meteorological</strong> <strong>data</strong> from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a <strong>script </strong>is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function <em>createIdaIceInput()</em> at the end of the script <em>createTimeSeries.py</em>. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em>.</p> <p>Some input <strong>data files have already been generated</strong><strong> </strong>and can be directly used for<strong> thermal building performance simulations with IDA ICE</strong>. These files can be found in the folder <em>0.3_Input_Timeseries (Climate) for IDA ICE</em>.</p> <p>The <strong>naming convention</strong> of the final input data files for IDA ICE is as follows:</p> <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em></li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see <em>createTimeSeries.py and READMEs)</em></li> <li>INTERFACE: Information on how single days were joined together (see <em>createTimeSeries.py</em>).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> <p>Example: <em>Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn</em></p> <p><strong>Folder overview:</strong></p> <ul> <li>The ENVI-met raw data is stored in <em>0.1_Input_RawENVImetOutput</em>.</li> <li>The script is stored in <em>0.2_Input_ScriptsToCreateTimeSeries</em>.</li> <li>The final datasets ready for simulation with IDA ICE are stored in <em>0.3_Input_Timeseries(Climate)ForIDAICE</em>. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>
NOAA GML Kettle Ponds Surface Radiation Budget and Near-Surface Meteorology Data for SPLASH
<p>These files contain Surface Energy Balance data at the Kettle Ponds (CKP) site as part of NOAA’s Global Monitoring Laboratory’s deployment in the Sail-SPLASH Campaign between October 2021 through August 2023.</p>
NOAA GML Brush Creek Surface Radiation Budget and Near-Surface Meteorology Data for SPLASH
<p>These files contain Surface Energy Balance data at the Brush Creek (CBC) site as part of NOAA's Global Monitoring Laboratory's deployment in the Sail-SPLASH Campaign between October 2021 through August 2023.</p><p> </p><p><strong>NOTE: Version 2.1 contains one "zip" file containing all daily files for ease of download.</strong></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.