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10,016 results for “Observation”
Intentional Observer Effects on Quantum Randomness
<p>The research that created this data is described in:</p> <ul> <li>Maier, M. A., Dechamps, M. C., & Pflitsch, M. (2018). Intentional Observer Effects on Quantum Randomness: A Bayesian Analysis Reveals Evidence Against Micro-Psychokinesis. Frontiers in psychology, 9, 379. doi:10.3389/fpsyg.2018.00379</li> </ul> <p>Two files are included:</p> <ul> <li>exp_data_12571.csv - Raw data (experiments)</li> <li>sim_data_12571.csv - Raw data (simulation)</li> </ul>
Neutral Composition and Temperature Response to the January 2021 SSW in the F-Region Observed by ICON
<p>'Omni' is the geomagnetic indices for Figure 2.</p> <p><a href="../api/records/11216124/draft/files/MERRA2%201979-2023.mat/content" target="_blank" rel="noopener">MERRA2 1979-2023</a> is zonal mean temperature at 10 hPa (~30 km) at 90 N and zonal mean zonal wind at 10 hPa at 60 N for Figure 1.</p> <p>ICON Limb FUV is the original data from ICON limb observed neutral composition and temperature profiles.</p> <p>pICON, pTIEGCM and pMSIS are the composition and temperature data transformed to the constant pressure levels from ICON observations, TIEGCM simulations and NRLMSISE-00 model for other Figures.</p> <p> </p>
Results Discharge Observation Uncertainty and Model Performance Pub
<p><br>Dataset for Publication:<br>On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance</p> <p>Authors and Affiliations:<br>- Jerom P. M. Aerts<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Jannis M. Hoch<br> Department of Physical Geography, Utrecht University, Utrecht, the Netherlands<br> Fathom, Bristol, United Kingdom<br>- Gemma Coxon<br> Geographical Sciences, University of Bristol, Bristol, United Kingdom<br>- Nick C. van de Giesen<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands<br>- Rolf W. Hut<br> Department of Water Management, Civil Engineering and Geoscience, Delft University of Technology, Delft, the Netherlands</p> <p>Correspondence:<br>For inquiries about the dataset or publication, contact:<br>Jerom P. M. Aerts<br>Email: j.p.m.aerts@tudelft.nl</p> <p>---</p> <p>Overview:<br>This dataset supports the research article, "On the Importance of Discharge Observation Uncertainty When Interpreting Hydrological Model Performance."<br>It includes outputs and analyses from hydrological models and observational data across multiple flow categories and performance metrics. The dataset enables replication of the analyses and insights into the impacts of discharge observation uncertainty on model evaluation.</p> <p>---</p> <p>Dataset Structure:</p> <p>Folder: Model_Results<br>Contains results from hydrological model runs and associated analyses.</p> <p>Subfolders:</p> <p>1. MARRMoT_Models<br> - Contains results for six hydrological models implemented using the MARRMoT framework.</p> <p>2. PCR-GLOBWB<br> - Includes results for the PCR-GLOBWB hydrological model.</p> <p>3. wflow_sbm<br> - Contains results for the wflow_sbm hydrological model.</p> <p>Each Subfolder Contains:</p> <p>- Flow Categories<br> - Results categorized by flow regime:<br> - Low_Flow<br> - Average_Flow<br> - High_Flow</p> <p>- Gumboot Temporal Sampling Analysis<br> - Results of temporal sampling analyses conducted using the Gumboot method.</p> <p>- Objective Functions<br> - Files containing model performance metrics based on different objective functions.</p> <p>- Observations<br> - Observed discharge time series data used for model calibration and validation.</p> <p>- Simulation Time Series<br> - Simulated discharge time series generated by the models.</p> <p>---</p> <p>Data Description:</p> <p>- File Formats<br> - All data files are in CSV format with UTF-8 encoding.</p> <p>- Variable Descriptions<br> - Date: YYYY-MM-DD<br> - Discharge Values: Cubic meters per second (m³/s).<br> - Flow Categories: Indicates if data corresponds to low, average, or high flow conditions.</p> <p>- Performance Metrics:<br> - Includes common hydrological metrics (e.g., Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency) computed for each model and flow category.</p> <p>---</p> <p>Usage Guidelines:</p> <p>Citation:<br>If you use this dataset in your research, please cite the publication as follows:<br>> Aerts, J. P. M., Hoch, J. M., Coxon, G., van de Giesen, N. C., & Hut, R. W. (2024). On the importance of discharge observation uncertainty when interpreting hydrological model performance. [Journal Name, Volume, Pages]. DOI: [Insert DOI].</p> <p>Licensing:<br>This dataset is made available under the [Insert License, e.g., Creative Commons Attribution 4.0 International License (CC BY 4.0)].</p> <p>---</p> <p>Contact:<br>For further questions, feel free to contact:<br>Jerom P. M. Aerts<br>j.p.m.aerts@tudelft.nl</p>
Dataset for Widespread occurrence of lava lakes on Io observed from Juno by A. Mura et al.
<div>This database provides supplementary information for the Article:</div> <div>Widespread occurrence of lava lakes on Io observed from Juno</div> <div>by Alessandro Mura, Federico Tosi, Francesca Zambon, Rosaly M. C. Lopes, Pete J. Mouginis-Mark, Jani Radebaugh, Alberto Adriani, Scott Bolton, Julie Rathbun, Andrea Cicchetti, Davide Grassi, Raffaella Noschese, Giuseppe Piccioni, Christina Plainaki, Roberto Sordini, Giuseppe Sindoni</div> <div> </div> <div>The data can be read with MATLAB. Each file name has the following convention:</div> <div> </div> <div>[Name of patera] _ [BAND] _ [ORBIT] _ [FILE]</div> <div>The data is a structure with the following fields:</div> <div> </div> <div> DATA.dat= calibrated data frame</div> <div> DATA.SCLK_SCI = SCLK time</div> <div> DATA.SCI_GeometryEpoch = ASCII time, not for geometry calibration</div> <div> DATA.File_Name_ACQ = PDS name</div> <div> DATA.File_Name_EGSE= EGSE name</div> <div> DATA.Name_cube= Cube name</div> <div> </div> <div>Each file may contain more than one frame. Each frame is 256 lines x 432 columns, and contains the calibrated band radiance in the L (lines 1 to 128) and M (lines 129 to 256) bands). Geometry can be obtained via the SPICE software by using the proper SCET time.</div> <div>The SCET can be obtained with cspice_scs2e from the provided SCLK_SCI field.</div> <div> </div> <div>The names of the Patera are the same as in the article with the following exceptions:</div> <div> </div> <div>Kibero is Unknown 03</div> <div>Dingir P. is Unknown 04</div> <div>JR055 is Unknown 02</div> <div>JR230 is Unknown 05 </div> <div>JR229 is Unknown 06 </div> <div>Hatchawa P. is Unknown 17</div> <div>JR207 is Unknown 16 </div> <div> </div> <div>Along with the data, we provide images of the frame (radiance in log scale). Data from Orbit 60 and 62 has been obtained from a test session occurred after a problem with the dark current electronics of JIRAM. Because the background level was not available, it has been obtained from images where Io was not in the field of view, and subtracted from the others. </div>
China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2024
<p>The <strong>30m seamless annual leaf-on Landsat composites from 1985 to 2024</strong> were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.</p> <p>The band naming convention follows Landsat TM standards, with bands designated as <strong>Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7)</strong>. Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit <strong>Google Earth Engine</strong> at <a href="https://code.earthengine.google.com/6d1ea26ff4463277840eaf6a2662763c">this link</a>, and <strong>Earth Engine App (<a target="_blank">Landsat Yearly Composite Viewer</a>)</strong> at <a href="https://ee-caiyt33-catcd.projects.earthengine.app/view/landsat-yearly-composite-viewer">this link</a>.</p> <p><strong><a target="_blank">The dataset has now been updated to include data up to 2024.</a></strong></p> <p><a target="_blank"><strong>Data citation:</strong> Cai, Y., Li, X., Zhu, P., Nie, S., Wang, C., Liu, X., & Chen, Y. (2025). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. <em>Journal of Remote Sensing</em>. </a><a href="https://doi.org/10.34133/remotesensing.0698">DOI: 10.34133/remotesensing.0698</a></p> <p>For data-related inquiries, please contact Dr. Yaotong Cai at <a href="mailto:caiyt33@mail2.sysu.edu.cn">caiyt33@mail2.sysu.edu.cn</a>.</p>
Observable and ionizing properties of star-forming galaxies with very massive stars and different IMFs
<p>Predicted, observable and ionizing properties of star-forming galaxies with very massive stars and different IMFs.</p> <p>The dataset includes the results from the models described in Table 1 of Schaerer+2024 (https://ui.adsabs.harvard.edu/abs/2024arXiv240712122S/abstract) and additional models.</p>
First observations of continuum emission in dayside aurora
<p>This dataset contains all data used in the related article, which are not published elsewhere. It includes all-sky images and an animation from Sony camera, data from meridian imaging spectrograph (MISS), data from Fabry-Perot interferometer (FPI) and High-Throughput Imaging Echelle Spectrograph (HiTIES) all operated at Kjell Henriksen Observatory on Svalbard at 78 degrees latitude (see Herlingshaw et al. 2024, <a href="https://doi.org/10.1139/as-2024-0009">https://doi.org/10.1139/as-2024-0009</a>). The FPI data is in plain text format with their own headers. The MISS data are in the raw format, and GitHub links for the plotting routines are given in the code availability section of the manuscript.</p>
Shedding light on the devastating floods in June 1897 in Romania: early instrumental observations and synoptic analysis
<p>Daily and monthly precipitation totals at different meteorological stations, in Romania, for the year 1897 used for different figures in Ionita and Nagavciuc, 2024.</p> <p>Ionita, M., and V. Nagavciuc, 2024: Shedding Light on the Devastating Floods in June 1897 in Romania: Early Instrumental Observations and Synoptic Analysis. <em>J. Hydrometeor.</em>, <strong>25</strong>, 1729–1745, <a href="https://doi.org/10.1175/JHM-D-23-0230.1" target="_blank" rel="noopener">https://doi.org/10.1175/JHM-D-23-0230.1</a></p>
Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs
<p>This dataset containts the data for the article <strong>Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model </strong>(https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69<em>° </em>18’ N, 16<em>°</em> 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. </p> <p><strong>Abstract:</strong></p> <p><em>Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m<sup>-2</sup>. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.</em></p> <p>The dataset contains three subsets:</p> <p>i) <strong>aerosol_data:</strong> Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements</p> <p>ii) <strong>INP_trajectories: </strong>Back trajectories at the time of INP measurements</p> <p>iii) <strong>model_data: </strong>Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic</p> <p>Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder <strong>scripts. </strong>The data folders and scripts folder should be in the same repository when running the scripts.</p> <p>Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. </p> <p>The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at <a href="https://search.earthdata.nasa.gov/" rel="nofollow">https://search.earthdata.nasa.gov/</a>. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at <a href="https://www.pangaea.de/" rel="nofollow">https://www.pangaea.de/</a>. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. </p> <p>References:</p> <ul> <li>Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8289058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8289058</a></li> <li>Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. <em>Nat Commun</em> <strong>11</strong>, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7</li> <li>Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). <a href="https://doi.org/10.1038/s43247-024-01524-2" rel="nofollow">https://doi.org/10.1038/s43247-024-01524-2</a></li> <li>Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, <a href="https://doi.org/10.5194/gmd-13-6165-2020" rel="nofollow">https://doi.org/10.5194/gmd-13-6165-2020</a>, 2020.</li> <li>Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. <a href="https://doi.org/10.1029/2021GL096191" rel="nofollow">https://doi.org/10.1029/2021GL096191</a></li> </ul>
Observation of 1/3 fractional quantum Hall physics in balanced large angle twisted bilayer graphene
<p>Raw data of Main figures (.dat file) and Monte Carlo calculation results at various filling factor (.xlsx file).</p> <p>In the mangetotransport file name, "Q mV" refers to the DMM sensitivity. The resistance is calcuated as "(Voltage drop x (Q) x 0.1) / (1E-7)".</p> <p> </p>
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Observation of Electroluminescence and Energy Transfer of Hyperbolic Polaritons
<p>Includes all codes and data related to the article titled "Electroluminescence and Energy Transfer Mediated by Hyperbolic Polaritons" for the main text, extended data and supplementary information. Version 3.</p>
EEAR-Clim: A high density observational dataset of daily precipitation and air temperature for the Extended European Alpine Region
<p>Data, metadata and code for paper published in Earth System Science Data:</p> <p>A high density observational dataset of daily precipitation and air temperature for the Extended Alpine Region</p> <p> </p> <p><strong>Code </strong>(working copy all written in R statistical software): scripts.zip</p> <ul> <li>to read and process data in from different sources</li> <li>to perform intra and inter-stations quality control</li> <li>to perform break detection and homogenization</li> <li>to read results of quality control and homogenization</li> </ul> <p><strong>Data</strong>:</p> <ul> <li>Daily time series of air temperature (mean, minimum and maximum) and precipitation as .zip files, grouped by data provider.</li> <li>Information on column content is provided in separate files "data_readme.txt"</li> <li>about 10000 stations from Italy, France, Switzerland, Austria, Germany, Slovenia, Croatia, Bosnia-Herzegovina, Czech Republic, Slovakia and Hungary</li> <li>Meta data (code, name, longitude, latitude, elevation, measurements availability for each variable, starting date, ending date) in "metadata.zip", including a file for each data provider</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "License.pdf"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p><strong>Version history:</strong></p> <p>v1.0: initial upload</p> <p>v2.0: update of data policies; addition of France and Croatia time series</p>
Global and Sequential Imaging Observation of the Earth's Plasmasphere by PHOENIX Onboard EQUULEUS
<p>A dataset used in "<span>Global and Sequential Imaging Observation of the Earth’s Plasmasphere by PHOENIX Onboard EQUULEUS</span>"</p>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Ka, W and G-band radar observations of clouds and light precipitation during the EPCAPE campaign in March and April 2023
<p>The files contained in the data sets include Ka, W and G-band radar observations of clouds and light precipitation for several days from March 23 to April 27, 2023, during the EPCAPE campaign in La Jolla, CA, USA. <br>The YYYYMMDD_HHMMSS file naming convention corresponds to the starting measurement time of the data set in UTC.<br>The CloudCube_EPCAPE_Gband_Spectra.zip folder contains G-band radar Doppler spectra in the form of calibrated reflectivity as a function of Doppler velocity and range, where the noise has been masked out. The CloudCube_EPCAPE_Gband_Spectra_Noise.zip folder contains G-band radar Doppler spectra, including noise and SNR. The CloudCube_EPCAPE_Gband_Moments.zip folder contains G-band radar Doppler spectra moments, i.e. calibrated reflectivity, mean Doppler velocity and Doppler spectrum width. The CloudCube_EPCAPE_Multifrequency.zip folder includes Ka, W and G-band calibrated reflectivity and dual-frequency reflectivity ratios. <br>These data were obtained from CloudCube, a Ka, W and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals.<br>For more details about the data processing and description, please refer to: Socuellamos, J. M., Rodriguez Monje, R., Lebsock, M. D., Cooper, K. B., Beauchamp, R. M., and Umeyama, A.: Multifrequency radar observations of marine clouds during the EPCAPE campaign, Earth Syst. Sci. Data. https://doi.org/10.5194/essd-2023-454, 2024. </p>
Supporting datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor"
<p>This archive contains datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor."</p>
Data for "Observation of Generalized t-J Spin Dynamics with Tunable Dipolar Interactions"
<p>Calculated dipole parameters, experimental and theoretical data and code needed to reproduce all plots of the paper, and separate csv files with data points of main figures. </p> <p> </p>
Global gridded NOx emissions using TROPOMI observations
<p>This dataset provides top-down global gridded emissions of NOx for the year 2022. It is constructed from retrievals of tropospheric vertical column densities of NO2 by the TROPOMI spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of the flux-divergence method. The emissions are provided with a spatial resolution of 0.0625°×0.0625° and deliver a detailed overview of the distribution of emissions. <br>This dataset also provides two lists of point and diffuse sources, defined as clusters of at least 3 contiguous pixels above the value of 2 Pmolecules/cm²/h for annual averaged NOx emissions, calculated with the given monthly gridded maps. Point sources are defined as clusters comprising 3 to 9 pixels while diffuse sources are clusters with more than 10 pixels. Corrections have been applied from the previous version of the dataset; changes are generally minor, except for emissions at very high latitudes.</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.