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2,322 results for “precipitations”
Dataset : Predictors of global monthly precipitation
<p>The predictability of global monthly precipitation.</p> <p>Currently, only one site in Beijing is included.</p>
Data from: Composition of a chemical signalling trait varies with phylogeny and precipitation across an Australian lizard radiation
<p>The environment presents challenges to the transmission and detection of animal signalling systems, resulting in selective pressures that can drive signal divergence among populations in disparate environments. For chemical signals, climate is a potentially important selective force because factors such as temperature and moisture influence the persistence and detection of chemicals. We investigated an Australian lizard radiation (<em>Heteronotia</em>) to explore relationships between a sexually dimorphic chemical signalling trait (epidermal pore secretions) and two key climate variables: temperature and precipitation. We reconstructed the phylogeny of <em>Heteronotia</em> with exon capture phylogenomics, estimated phylogenetic signal in among-lineage chemical variation, and assessed how chemical composition relates to temperature and precipitation using multivariate phylogenetic regressions. High estimates of phylogenetic signal indicate that the composition of epidermal pore secretions varies among lineages in a manner consistent with Brownian motion; although there are deviations to this, with stark divergences coinciding with two phylogenetic splits. Accounting for phylogenetic non-independence, we found that among-lineage chemical variation is associated with geographic variation in precipitation but not temperature. This contrasts somewhat with previous lizard studies, which have generally found an association between temperature and chemical composition. Our results suggest that geographic variation in precipitation can affect the evolution of chemical signalling traits, possibly influencing patterns of divergence among lineages and species.</p> <p> </p>
Data for Holdrege et al: Precipitation intensification increases shrub dominance in arid, not mesic, ecosystems
<p>Data and code for the Holdrege et al. (2022) manuscript entitled 'Precipitation intensification increases shrub dominance in arid, not mesic, ecosystems' published in <em>Ecosystems </em>(doi: 10.1007/s10021-022-00778-1). This repository contains the results from simulations of sagebrush dominated plant communities conducted across 200 sites in the western United States. Simulations were run for ambient conditions as well as increased precipitation intensity and warming. Refer to the manuscript for details on the methods. This repository contains the summarized model output (data) and the R code that uses this data to compute the statistics and create the figures presented in the manuscript. See the README.md and data_dictionary.md files for more information on the code and data, respectively. Note, the .git subfolder provided here contains the git (version control history) files, and the users can ignore these. </p>
CMT precipitation dataset for numerical models of the ocean
<p>Total and liquid precipitation datasets created with the method described in <strong>Bias and trend correction of precipitation datasets to force ocean models</strong> (<em>Dussin, JTECH, in revision)</em>.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
A high-resolution GPM IMERG precipitation dataset for China
<p>The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China</p>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
Supplementary files for paper: "Design and fabrication of an electrostatic precipitator for infrared spectroscopy" in Atmospheric Measurement Techniques, 2022.
<ol> <li>File of absorbance spectra and hypothetical thickness for each sample.</li> <li>MATLAB function to perform clean crystal spectrum subtraction and baseline correction (described in the paper).</li> </ol>
Dataset for Smaller_Sensitivity_of_Precipitation_to_Surface_Temperature_under_Massive_Atmosphere
<p>This file is the dataset for "Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmosphere".</p> <p>Uploaded as 5 groups (1-D radiative transfer model, GCM fixsst simulations, GCM aqua planet simulations, GCM present continent simulations, and cloud-resolving simulations), The data is time average of balanced state.</p> <p>For our article, GCM fixsst simulations are designed for group A, H and sensitivity test 1; GCM aqua planet simulations are designed for group B, C, D, and sensitivity test 2; GCM present continent simulations for group E, F, G; and RCE simulations for sensitivity test 4.</p>
Datasets for "A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation"
<p>These three datasets are supplemental material for the Geophysical Research Letters article, A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation.</p> <p> </p> <p>Data Set DS1. Te data plotted in Figure S1. This is the 3-beam averaged Te data described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS2. Te data errors plotted in Figure S1. This is the 3-beam averaged Te data propagated errors described in Text S1. The first row is the heading that, after Time, lists the altitudes in meters of the data in each column. The first column is time in the format: Year-Month-Day/Hour-Minute-Second.</p> <p> </p> <p>Data Set DS3. THEMIS ASI data at the PFISR location used to calculate the heat flux plotted in Figure S1. The first row is the header. Time, the first column, is in the format: Year-Month-Day/Hour-Minute-Second. The following columns are: geographic longitude, geographic latitude, energy flux for a Gaussian distribution, mean energy for a Gaussian distribution, energy flux for a Maxwellian distribution, mean energy for a Maxwellian distribution. Units are included in the header.</p>
Weamyl radar and precipitation data related to quantitative precipitation estimation
<p>This dataset contains radar data and precipitation data provided for research during the Weamyl project (NO Grants 2014-2021, under Project contract no. 26/2020) specifically used for quantitative precipitation estimation research.</p> <p>Radar data contains two radar products:</p> <ul> <li>Reflectivity (R): a base radar product, measured in dBZ, it is the product used for estimating precipitation. R data for the lowest 2 elevation angles. </li> <li>One-hour precipitation (OHP): a derivate radar product, it is the precipitation estimation that the radar makes automatically; to be used as comparison</li> </ul> <p>All radar data are in <a href="https://www.unidata.ucar.edu/software/netcdf/">netcdf </a>format. It is data from a WSR-98D Doppler weather radar situated in central Transylvania, provided in the form of a 557x800 grid with the cell size of ~1x1 km.</p> <p>Precipitation data contains 1-hour precipitation values collected from ground weather station, measured in mm; with the purpose to be used as ground truth. The precipitation data contains a csv file that contains the actual precipitation data, first value mentions the station code, the second the time and date and the third the 1-hour accumulated precipitation value, in mm.</p> <p>The second file provides the lat/long values for the ground weather stations, to be able to match the values of a station to a location on the grids from the radar data.</p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
Visualizations for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions
<p>- Author manuscript version of paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions".</p> <p>- Visualization for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions". This enables the 3D dataset to be visualized using the free software package Paraview (<a href="https://www.paraview.org">https://www.paraview.org</a>). Instructions are provided.</p> <p>- Animation created of 3D visualization (using Aviso)</p> <p>NOTE: The full set of raw data used to create the results in the paper is available at:</p> <pre><a href="https://doi.org/10.5281/zenodo.7108545">https://doi.org/10.5281/zenodo.7108545</a></pre> <p> </p> <p> </p> <p> </p>
Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters. Scripts used for plotting and analysis are also included.</p>
Triple oxygen isotope variability of precipitation in a tropical mountainous region
<p>Triple oxygen isotope data associated with monthly integrated precipitation and discrete (subsurface-sourced) tap water samples collected over one year from sites across Panama.</p>
РИС. 7. НедостаточнаЯ промывка раковин глохидиев после очиЩениЯ в Щелочи (5% КОН). А, С. «Замыленность» пор наружной поверхности створок (Cristaria tuberculata, оЗ. Ханка, Приморский кр.). B. Остаток Щелочи, выпавШий кристаллами на поверхности личинки (Unio dembeae, р. Дуко, ЭфиопиЯ). D. Капли раствора Щелочи (укаЗаны стрелками) на поверхности Шипов крючка (Nodularia douglasiae, р. Гион, о-в Хонсю, ЯпониЯ). МасШтаб 5 мкм (А, С), 2 мкм (B, D). Микроскоп Zeiss MERLIN, напыление углеродом (А, В), хромом (С, D). FIG. 7. Insufficient rinsing of glochidia after cleaning in alkali (5% KOH). A, C. «Blurredness» of the exterior valve pores (Cristaria tuberculata, Khanka Lake, Primorsky Krai). B. Precipitation of alkali crystals on the exterior glochidia surface (Unio dembeae, Duko River, Ethiopia). D. Drops of alkali (indicated by arrows) on the hook spines (Nodularia douglasiae, Gion River, Honshu Island, Japan). Scale bars 5 μm (A, C), 2 μm (B, D). Zeiss MERLIN microscope, sputter coating with carbon (A, B) and chromium (C, D). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 7. НедостаточнаЯ промывка раковин глохидиев после очиЩениЯ в Щелочи (5% КОН). А, С. «Замыленность» пор наружной поверхности створок (Cristaria tuberculata, оЗ. Ханка, Приморский кр.). B. Остаток Щелочи, выпавШий кристаллами на поверхности личинки (Unio dembeae, р. Дуко, ЭфиопиЯ). D. Капли раствора Щелочи (укаЗаны стрелками) на поверхности Шипов крючка (Nodularia douglasiae, р. Гион, о-в Хонсю, ЯпониЯ). МасШтаб 5 мкм (А, С), 2 мкм (B, D). Микроскоп Zeiss MERLIN, напыление углеродом (А, В), хромом (С, D). FIG. 7. Insufficient rinsing of glochidia after cleaning in alkali (5% KOH). A, C. «Blurredness» of the exterior valve pores (Cristaria tuberculata, Khanka Lake, Primorsky Krai). B. Precipitation of alkali crystals on the exterior glochidia surface (Unio dembeae, Duko River, Ethiopia). D. Drops of alkali (indicated by arrows) on the hook spines (Nodularia douglasiae, Gion River, Honshu Island, Japan). Scale bars 5 μm (A, C), 2 μm (B, D). Zeiss MERLIN microscope, sputter coating with carbon (A, B) and chromium (C, D).
Dataset for publication "Operando Observation of (Bi)carbonate Precipitation during Electrochemical CO2 Reduction in Strongly Acidic Electrolytes"
<p>Broad topic: electrochemical reduction of CO2 using Ag/Cu-based gas diffusion electrodes in neutral and acid electrolyte, operando characterisation using synchrotron radiation (wide-angle X-ray scattering), (bi)carbonate precipitation.</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/PowerPoint files are all contained in the subfolders.</p> <p>Synchrotron raw data are linked to the experiment numbers MA5874 and INHC1776 at ESRF</p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p> <p>Synchrotron data are analysized using this python library https://github.com/EmpaEconversion/Twaxs</p>
Risk Assessment of Extreme Precipitation on to Low- and Medium-Voltage Electrical Infrastructure Under the Influence of Climate Change
Open the record for dataset details and reuse information.
Central Italy Complete Daily Precipitation Series
<p>The "Central Italy Complete Daily Precipitation Series" dataset provides a meticulously gap-filled daily precipitation record for 201 stations in Central Italy from 1951 to 2019. Using quantile mapping, spatial interpolation, machine learning, and multi-strategy merging techniques, the dataset ensures serial completeness and high accuracy. It incorporates data from neighboring stations and ERA5 estimates, validated through quality control measures such as Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Bias, and Kling-Gupta Efficiency (KGE). This dataset is ideal for hydroclimatic modeling and projections.</p> <p>The dataset includes a CSV file with essential station information, such as station ID, latitude, longitude, elevation, and station name. The observed, estimated, and final Serially Complete Dataset are available in MAT format, with each column representing a time series of a station in the same order as in the CSV file. Additionally, the Nearby Info file provides the correlation coefficient (CC) and the distance between each target station and its assigned nearby stations, with each column corresponding to a target station in the same order as in the CSV file.</p>
ScienceDex guides
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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.