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2,113 results for “High resolution”
Front Parlor Mantel - High Resolution
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Plaque Gift to Mrs. Stanford - High Resolution
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Retrieval of dominant methane (CH4) emission sources, the first high resolution (1-2m) dataset of storage tanks of China in 2000-2021
<p>We presented a storage tank inventory of 92 typical cities with high volatile organic compounds, in response to the Three-Year Action Plan for Winning the Blue Sky Defense War, proposed by the State Council of China. It comprises of 14461 storage tanks, which are extracted based on high spatail resolution images of GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan satellites in year of 2021. It is the first inventory covering detailed distribution locations and boundaries of storage tanks. The inventory we aim to share provides a basic distribution of storage tanks in different sizes and will contribute to support environmentally friendly regulations proposal for more effective pollution control and energy resource management.</p>
NUUR-Texture2953: A Diverse Dataset of High Resolution Homogeneous Textures
<p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Zhiwei Xu: zhiweixu2050@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>"NUUR-Texture2953" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis, and is build upon our previously established dataset "NUUR-Texture500". The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Zhiwei Xu, Gaurav Sharma, Thrasyvoulos N. Pappas, "Texture Representation via Analysis and Synthesis with Generative Adversarial Networks", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (2023), https://doi.org/10.1016/j.prime.2023.100286.</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures", Zenodo, https://doi.org/10.5281/zenodo.7127079</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture2953 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p>
A wideband, high-resolution vector spectrum analyzer for integrated photonics
<p>This data contains the raw data of the experiment and the code and corresponding data of the figures in the artical.</p>
High-Resolution Vegetation Distribution (HRVD) dataset
<p><strong>High-Resolution Vegetation Distribution (HRVD) dataset</strong> was developed by integrating multiple datasets, including the China multi-period land use/cover change remote sensing monitoring dataset (CNLUCC), MODIS MCD12Q1 land cover product, and Vegetation Atlas of China (1:1,000,000). The HRVD dataset provided vegetation distribution data in China in 2020 with a horizontal resolution of 1 km. <br><em>The HRVD dataset includes tree, shrub, herb, and crop, corresponding to the value of 1, 2, 3, and 4 in the attribute table, respectively.</em></p>
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
Supplementary Movie 1 from: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution
<p>This record contains<strong> Supplementary Movie 1</strong> from:</p> <p><strong>A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution</strong></p> <p>Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal</p> <p>Biozentrum, University of Basel, 4056 Basel, Switzerland</p> <p>Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch</p> <p> </p>
High-resolution global gridded population between 1870 and 2100
<p>history_pop.zip contains historical gridded population data from 1870 to 2010 at a 10-year interval</p> <p>SSP1.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP1</p> <p>SSP2.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP2</p> <p>SSP3.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP3</p> <p>SSP4.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP4</p> <p>SSP5.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP5</p>
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
Simulation of Southern Ocean cloud processes using the high-resolution regional UK Met Office Unified Model with interactive aerosols
<p>Code and model data used to create the figures for Price et al., 2024, JGR (in prep.).</p>
High-spatial-resolution (0.0083° × 0.0083°) and long-term (1982 to 2010) monthly gridded wetland CH4 flux product for the Southeastern United States
<p>This dataset presents monthly gridded methane emissions from subtropical freshwater wetlands across the Southeastern United States spanning from 1982 to 2010, measured in nmol m-2 s-1. Each grid cell's methane flux prediction was adjusted based on the fractional wetland extent within that cell, utilizing data from the National Wetland Inventory (NWI) and the Wetland Area and Dynamics for Methane Modeling (WAD2M) product. The dataset includes mean monthly fluxes with no adjustment for wetland area (i.e., fluxes assuming hypothetical 100% wetland cover), as well as mean monthly fluxes adjusted for wetland area based on NWI or WAD2M, along with their respective standard deviations of the daily emissions for each month. Data are provided in NetCDF4 format.</p> <p>Lat: 25-40°N</p> <p>Lon: 95-75°W</p> <p>Period: 198201-201012 (for "CH4_Monthly_SEUS_unweighted.nc" and "CH4_Monthly_SEUS_NWI.nc") and 200001-201012 (for "CH4_Monthly_SEUS_WAD2M.nc")</p> <p>Temporal resolution: Monthly</p> <p>Spatial resolution: 0.0083° × 0.0083° (~1 km × 1 km)</p> <p>Unit: nmol m-2 s-1</p> <p>Fill value: -9999</p>
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
<p>Codes and data made available as part of the data paper "HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)" publication. This work presents the HighResClimNevada database, a climatic database for Sierra Nevada (southern Spain) based on data modeled with the Weather Research and Forecasting model. The data used as a reference for the evaluation of HighResClimNevada are freely available online at the websites of the different institutions that develop it, so they are not available here.</p> <p>This research was financially supported by the project "Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)" funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan - NextGenerationEU and by the Regional Government of Andalucia, the project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE; and LifeWatch-2019-10-UGR-01 co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE) LifeWatch-ERIC action line; and P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades.</p>
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
<p>This dataset provides annual paddy rice maps of Northeast and Southeast Asia from 2017 to 2019.</p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: Northeast and Southeast Asia</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** CropType: Paddy rice.</p> <p>*** Year: Values from 2017 to 2019</p> <p>*** The file name consists of year (Y), latitude range (N/S), and longitude range (E). For example, the file name '2019Y_40_45N_140_145E' means the map of paddy rice in the range of 40-50 degrees North and 140-145 degrees East for 2019.</p>
High resolution dynamic mapping of the C. elegans intestinal brush border
<p>The intestinal brush border is made of an array of microvilli that increases the membrane surface area for nutrient processing, absorption, and host defense. Studies on mammalian cultured epithelial cells uncovered some of the molecular players and physical constrains required to establish this apical specialized membrane. However, the building and maintenance of a brush border <em>in vivo</em> has not been investigated in detail yet. Here, we combined super-resolution imaging, transmission electron microscopy and genome editing in the developing nematode <em>C. elegans</em> to build a high-resolution and dynamic localization map of known and new brush border markers. Notably, we show that microvilli components are dynamically enriched at the apical membrane during microvilli outgrowth and maturation but become highly stable once microvilli are built<em>.</em> This new toolbox will be instrumental to understand the molecular processes of microvilli growth and maintenance <em>in vivo</em> as well as the effect of genetic perturbations, notably in the context of disorders affecting brush border integrity.</p>
Figure 11. Ultra high resolution X in Cranial anatomy of Paleocene and Eocene Labidolemur kayi (Mammalia: Apatotheria), and the relationships of the Apatemyidae to other mammals
Figure 11. Ultra high resolution X-ray computed tomography (uhrCT) slices of USNM 530208. See caption for Figure 8. (A) slice no. 210; the solid arrow indicates the postglenoid foramen; the dashed arrow indicates the subsquamosal foramen. (B) slice no. 194; the white arrow indicates the opening of the postglenoid foramen into the lateral neurocranium. (C) slice no. 187; the white arrow indicates the canal for the greater petrosal nerve running through the petrosal. (D) slice no. 72; the white arrow indicates the posterior semicircular canal. Note also the extensive pneumatization of the petrosal (in the mastoid region) and exoccipital.
Figure 9. Ultra high resolution X in Cranial anatomy of Paleocene and Eocene Labidolemur kayi (Mammalia: Apatotheria), and the relationships of the Apatemyidae to other mammals
Figure 9. Ultra high resolution X-ray computed tomography (uhrCT) slices of USNM 530208, and enlarged view of the left auditory region. See caption for Figure 8. In all images the white arrow indicates a fragment of bone, identified as a piece of the basisphenoid, which has been displaced rostrally along the basisphenoid process. In all four uhrCT images the portion of the basisphenoid medial to the fragment in question is clearly damaged. This can be seen in its irregular outline, and in the lack of symmetry with the less damaged right side. Panel (D) also shows some fragments of bone ventrally, which underscore the damage that has occurred in this region. The enlarged view of the left auditory region has been tipped medially so that the medial wall of the tympanic cavity is visible. Note the step fractures along the fragment of basisphenoid, indicated with the black arrow, indicating damage and probably displacement.
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations
<p><strong>Data for publication:</strong></p> <p><em><strong>Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations.</strong></em></p> <p>Mail:</p> <p>sato@riam.kyushu-u.ac.jp </p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></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.