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988 results for “Averaging”
Daily average pCO2, dissolved oxygen, and water quality in Elson Lagoon: 2019, 2021, 2022
pCO2 (partial pressure of carbon dioxide) and other physicochemical parameters were measured during the open water season (July-August) in three years (2019, 2021, 2022) at five sites in Elson Lagoon, a coastal ecosystem near Utqiagvik, AK, as a supplement to the Beaufort Lagoons Ecosystems LTER core sampling program. Locations include three shallow sites (<1 m water depth) at a depth of 0.5 m and two deep sites (up 2 m depth) at a depth of ~1.7m. Data includes daily averages of sensor data for each site of pCO2 (uatm), water temperature (°C), absolute pressure (kPa), salinity (ppt), dissolved oxygen (DO, mg/L), light extinction (Kd; m-1), and water level difference from mean tidal level (m). Discrete laboratory data for dissolved organic carbon (C; umol/L) and turbidity (NTU) are also presented.
Column-Averaged Atmospheric Carbon and Water at Harvard Forest since 2018
Ground-based total-atmospheric column measurements of CO2, CH4, CO, and H2O began at Harvard Forest in May 2018 using a solar-viewing Fourier Transform Spectrometer (Bruker EM27/Sun). Observations are made from a platform near the Fisher Meteorological Tower throughout the year on sunny days. Total column observations are sensitive to regional carbon fluxes, and are less impacted by changes in the boundary layer height than tower based in-situ observations. The Harvard Forest observations are complemented by an identical instrument operated on the Harvard campus in Cambridge MA. The two sensors are used to support studies of urban emissions, regional transport, and forest-atmosphere exchange. In addition, the total-column measurements provide key validation data for satellite measurements of greenhouse gases.
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
Single-cycle, 643-mW average power THz source based on tilted pulse front in lithium niobate
<p>This data set is associated with the aforementioned paper.</p> <p>The data and the Jupyter notebooks (Python) to reproduce the figures in this paper can be downloaded below. To run a Jupyter notebook as a beginner, it is easiest to download and install anaconda, a Python environment that comes with many packages preinstalled and also offers Jupyter lab/notebook. It is available at <a href="https://www.anaconda.com/download" target="_blank" rel="noopener">https://www.anaconda.com/download</a>.</p> <h2>Fig.01</h2> <p><strong>Fig.01_literature_lithium_niobate_sources.csv</strong> contains a summary table of the last decades of published THz power values obtained with lithium niobate in the tilted pulse front geometry. The accompanying jupyter notebook allows to reproduce the figure that was used in the paper.</p> <h2>Fig.03</h2> <p>Each individual data frame (df), which is saved as an HDF file in the .zip file, contains a "power curve" measurement (i.e. measured THz power as a function of the applied pump power). Whenever a parameter is changed, all positions, angles, THz power and cryostat parameters are saved.</p> <ul> <li><strong>x1 </strong>is the position of the last mirror before the transmission grating (parallel to the pump beam direction before the crystal) in [mm]</li> <li><strong>x2 </strong>is the position of the first imaging lens in direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x3 </strong>is the position of the second imaging lens in the direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x4 </strong>is the position of the cryostat in the direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>y0 </strong>is the position of the cryostat in the perpendicular direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>α0 </strong>is the angle of the lambda/2 waveplate that allows the pump power to be varied at the crystal in [°]</li> <li><strong>α1 </strong>is the angle of the last mirror before the grating in [°]</li> <li><strong>α2 </strong>is the angle of the transmission grating in [°]</li> <li><strong>thz_power_W </strong>is the obtained power obtained from the Ophir 3A-P-THz power meter in [W]</li> <li><strong>temperature_setpoint_K </strong>is the LakeShore cryostat controller setpoint in [K]</li> <li><strong>temperature_K </strong>is the temperature read from the sensor on the cooling finger (above the crystal) in [K]</li> <li><strong>heater_output </strong>is the amount of power in [%] delivered to the resistive heating element inside the cryostat. 100% corresponds to about 50 W. Its value is controlled by an internal PID loop of the cryostat controller, which tries to stabilize <strong>temperature_K </strong>to <strong>temperature_setpoint_K</strong></li> <li><strong>pump_power </strong>is the average laser power reaching the crystal in [W]. It was calibrated before obtaining the data set by characterizing the lambda/2 waveplate angle <strong>α0</strong> to the value of an NIR power meter just before the cryostat.</li> <li><strong>repetition_rate</strong> is the repetition rate of the laser in [Hz]</li> </ul> <p>As an example, below is one line (for one pump power) of such a data frame:</p> <table> <tbody> <tr> <td> </td> <th>x1</th> <th>x2</th> <th>x3</th> <th>x4</th> <th>y0</th> <th>α0</th> <th>α1</th> <th>α2</th> <th>thz_power_W</th> <th>temperature_setpoint_K</th> <th>temperature_K</th> <th>heater_output</th> <th>pump_power</th> <th>repetition_rate</th> </tr> <tr> <td>0</td> <td>-12.000005</td> <td>2.500039</td> <td>9.100015</td> <td>-5.0</td> <td>-2.0</td> <td>35.905660</td> <td>25.68</td> <td>-23.3</td> <td>0.006000</td> <td>80.0</td> <td>79.883</td> <td>4.4</td> <td>20.0</td> <td>40000.0</td> </tr> </tbody> </table> <p>10 of such power curves were obtained at 100 kHz and 40 kHz and can be found in the respective zip-file.</p> <p> </p> <p><strong>Literature_Power_Efficiency.zip</strong> contains digitzed power and efficiency values from the following references:</p> <ol> <li>X. Wu, D. Kong, S. Hao, et al., "Generation of 13.9-mJ Terahertz Radiation from Lithium Niobate Materials," Advanced Materials 35, 2208947 (2023).</li> <li> <p>P. L. Kramer, M. K. R. Windeler, K. Mecseki, et al., "Enabling high repetition rate nonlinear THz science with a kilowatt-class sub-100 fs laser source," Opt. Express 28, 16951 (2020).</p> </li> <li> <p>T. Kroh, T. Rohwer, D. Zhang, et al., "Parameter sensitivities in tilted-pulse-front based terahertz setups and their implications for high-energy terahertz source design and optimization," Opt. Express, OE 30, 24186–24206 (2022).</p> </li> <li> <p>B. Zhang, Z. Ma, J. Ma, et al., "1.4-mJ High Energy Terahertz Radiation from Lithium Niobates," Laser & Photonics Reviews 15, 2000295 (2021).</p> </li> </ol> <p> </p> <h2>Fig.04</h2> <p><strong>EOS_dfs.p</strong> is a pickle file, contain electro-optic sampling traces, which are already averaged for various pump powers at 40 kHz repetition rate.</p>
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Daily weather averages for Palmer Station, Antarctica (1989-2024)
Daily averages of weather data collected at Palmer Station, Antarctica were generated from averages of manual observations (1989-2003) and automatic weather station measurements (2003-present). Weather data was originally acquired by manual observers 4 times per day. An automated weather data system was installed in Nov 2001. Measurements began shifting from manual to automated observations in June 2003 until the manual observations were ended on December 12, 2003, except for snow stake depth, sea ice coverage, and sea water temperature which remain as manual observations. Data are collected, compiled, and distributed by the U.S. Antarctic Program’s polar contractor. Data is archived by the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin-Madison, and made available online at https://amrdcdata.ssec.wisc.edu/group/palmer-station/ This dataset contains a compiled version of the daily averaged data provided by the archive.
Average monthly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1978 - June 2024.
Monthly sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including (1) monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines); and (2) monthly sea-ice concentration (%) extracted for small PAL subregions, including the nominal penguin foraging areas (~200km by ~200km) southwest of King George Island (KGI), Anvers, Avian and Charcot islands, as well as for Marguerite Bay (~140km by ~140km) (inland of the area defined for Avian).
Average yearly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1979 - 2023.
Annual sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including: monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines).
SBC LTER: Daily averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)
From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
Average daily alcohol consumption
<p>Average daily alcohol consumption by sex and age group. Average and standard deviation. Population aged 15 years old and over that consumes alcoholic beverages more than once per month</p>
ERA-5 reanalysis results interpolated onto the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>ERA-5 fields at 1-hour temporal and grid size of 0.25° x 0.25° (0.5° x 0.5° for wave variables) have been downloaded from <a href="https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels">https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels</a>.</p> <p>The data are interpolated using two methods:</p> <p>'nearest': the value of the nearest ERA-5 grid cell is use;</p> <p>'linear': the values from the nearest grid cells in space and time are linearly interpolated to the [date_time, latitude, longitude] coordinate of the ship</p> <p>providing a number of atmospheric, land and oceanic climate variables interpolated along the five-minute cruise track.</p> <p>The data repository can be checked out at: <a href="https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track">https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track</a></p> <p><strong>Dataset contents</strong></p> <ul> <li>era5-on-cruise-track-5min-legs0-4-linear.csv, data file, comma-separated values</li> <li>era5-on-cruise-track-5min-legs0-4-nearest.csv, data file, comma-separated values</li> <li>interpolate-to-shiptrack.py, processing script, text/x-python</li> <li>download-ecmwf.ipynb, processing script, application/x-ipynb+json</li> <li>ecwmf_interpolate.zip, processing scripts, zip file</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This interpolation of the ERA-5 reanalysis output to the five-minute averaged cruise track and velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Distance to the nearest land/coastline (including small subantarctic islands) for the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset is derived from:<br> - The GPS track of the R/V-Akademik Tryoshnikov (10.5281/zenodo.3772377)<br> - The shapefiles of the continents from NaturalEarth physical (@50m), version 4.1.0,<br> downloadable at [https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/physical/ne_50m_land.zip](https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/physical/ne_50m_land.zip)<br> - A manual entry of the smaller islands which may not be mapped on the NaturalEarth resource, namely:<br> - "Peter I": [-68.8282, -90.6157]<br> - "Scott": [-67.3783, -179.9117]<br> - "Young": [-66.2833, 162.4167]<br> - "Buckle": [-66.65, 163.05]<br> - "Sturge": [-67.416667, 164.733333]<br> - "Siple": [-73.65, -125]<br> - "Bouvetoya": [-54.4208, 3.3464]</p> <p>The calculation of the actual distance to land has been done in qGIS 3.2.3-Bonn https://qgis.org/downloads/, using the NNJoin plugin version 3.1.2 https://plugins.qgis.org/plugins/NNJoin/. After the point-to-closest polygon distance calculation, the python script in src/add_distance_to_small_islands.py replaces the distance calculation to the centerpoint of the islands (as reported above) if the boat is closer to the island centerpoint than any other coast.</p> <p>Data file: dist_to_land_incl_small_islands.csv</p> <p>The data repository can be checked out at: https://renkulab.io/gitlab/ACE-ASAID/cruise-track-distance-to-land<br> <br> <strong>Dataset contents</strong></p> <p>- dist_to_land_incl_small_islands.csv, data file, comma-separated values<br> - data_file_header, metadata, text format<br> - README.txt, metadata, text format<br> - add_distance_to_small_islands.py, python script, text format</p> <p><strong>Dataset license</strong></p> <p>This output to the five-minute averaged distance to land is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Two-minute averaged net community production in seawater estimated along the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>During the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017, seawater O2/Ar ratios were measured underway from the ship's flow-through seawater system. High-resolution net community production (NCP) in units of mmol O2 m-2 day-1 was derived from delta O2/Ar and National Centers for Environmental Prediction (NCEP) reanalysis winds followed by a linear correction for sea-ice.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_ncp_linear_correction.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data reprocessed using different version of sea ice.</p> <ul> <li>data file reprocessed</li> <li>README updated to reflect change in processing</li> <li>change log added to dataset</li> </ul> <p>v1.0 - initial release of dataset</p>
Time-averaged borehole temperatures at AM01–AM06 on the Amery Ice Shelf
<p>These are supplementary materials for the paper:</p> <p>Wang, Y., Zhao, C., Gladstone, R., Galton-Fenzi, B., and Warner, R.: Thermal structure of the Amery Ice Shelf from borehole observations and simulations, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-248, in review, 2021.</p> <p>Full description is given in the paper.</p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
December 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Doboy Sound transect
Two hydrographic surveys were performed December 10, 2002, along an east-to-west transect through Doboy Sound near Sapelo Island, Georgia (Doboy Sound Transect, GCE-DB). Vertical CTD profiles were collected at approximately 2km intervals from -2km to 12km along the transect during low tide and high tide conditions. Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
December 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Intracoastal Waterway transect
Three hydrographic surveys were performed from December 10 to December 11, 2002, along the Intracoastal Waterway between the Altamaha River south of Wolf Island and Sapelo Sound (Intracoastal Waterway Transect, GCE-IC). Vertical CTD profiles were collected at various intervals from -15km to 29km along the transect during various tidal regimes. Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
December 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Altamaha River transect
Four hydrographic surveys were performed on December 11, 2002, along an east-to-west transect up the Altamaha River in Georgia (Altamaha River Transect, GCE-AL). Vertical CTD profiles were collected at 1-2km intervals from the -2km to 28km upriver along the transect during low and high tidal regimes. Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
December 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Inner Marsh transect
One hydrographic survey was performed on December 11, 2002, along the Darien River, North River, and north channel of the Altamaha River (Inner Marsh Transect, GCE-IM). Vertical CTD profiles were collected at various nominal stations along the transect under a low water tidal regime (late ebb to early flood tide). Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.
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.