Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
451
datasets available to search
ShareScore release 0.9.0
Dataset results
451 results for “catchment”
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Geochemical sediment fingerprinting dataset from Oroua river catchment, New Zealand
<p>Geochemical dataset collected to determine key source contributions to overbank sediment deposition for specific particle size fractions as described in "Vale, S., Smith, H., Matthews, A., & Boyte, S. (2020). Determining sediment source contributions to overbank deposits within stopbanks in the Oroua River, New Zealand, using sediment fingerprinting. <i>Journal of Hydrology (New Zealand)</i>, <i>59</i>(2), 147-172." </p>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Reference Mean and Low Streamflow for all Brazilian Catchments Generated Using Machine Learning Models
<p>This dataset provides comprehensive hydrological information for river networks in Brazil, focusing on reference streamflows, specifically long-term mean flows (qm) and low flows exceeded 95% of the time (q95). Covering over 400,000 ungauged river points, the dataset was developed using advanced machine learning models trained on environmental descriptors and validated against data from 1,069 gauging stations spread across the country. The machine learning pipeline evaluated six regression models to achieve high predictive accuracy (R² > 0.8 for qm and > 0.7 for q95). The 62 environmental descriptors - encompassing climate, topography, land cover, lithology, and water storage characteristics - that were used as features for the models are also included.</p> <p>Key features:</p> <ul> <li><strong>Spatial Coverage:</strong> Brazilian territory and the Amazon River basin, based on the <a href="https://metadados.snirh.gov.br/geonetwork/srv/api/records/f7b1fc91-f5bc-4d0d-9f4f-f4e5061e5d8f" target="_blank" rel="noopener">BHO 5k</a> dataset of officially adopted river networks.</li> <li><strong>Outputs:</strong> Predicted qm and q95 values for each river stretch, with 90% and 75% confidence intervals to account for prediction uncertainty.</li> <li><strong>Environmental Descriptors:</strong> Aggregated from upstream catchment area.</li> </ul> <p> </p>
CAMELS-ES: Catchment Attributes and Meteorology for Large-Sample Studies – Spain
<p>CAMELS-ES is a hydrometeorological dataset covering 269 catchments in Spain and the time period from 1991 to 2020. It is a contribution to the Caravan initiative, a global community that collects open hydrometeorological data to support global hydrological modelling. As other datasets in Caravan, CAMELS-ES includes both catchment attributes extracted from HydroATLAS and ERA5-Land, meteorological time series from ERA5-Land and discharge records from the Spanish Ministry of the Environment. In addition, CAMELS-ES includes information from the European Flood Awareness System (EFASv5): catchment attributes extracted from the input static maps used in the hydrological model LISFLOOD, and the simulated discharge from EFASv5 long run.</p>
Data and script: Catchment scale deforestation increases the uniqueness of subtropical stream communities
<p>These are datasets on benthic diatom and insect communities sampled in 100 streams along a gradient of land use intensification, ranging from streams in pristine forests to agricultural catchments in southeast subtropical Brazil. Data also include information on instream and land-use variables.</p> <p>In addition to the datasets, we also provide the R codes used to investigate whether compositional uniqueness (LCBD) and species contribution to beta diversity (SCBD) of stream diatoms and insects can be predicted by instream and land-use characteristics and by species traits and taxonomic relatedness, respectively. </p>
Modelling impacts of tramlines on soil erosion processes at the catchment scale.
<p>The data refers to the following article:</p> <p>Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161</p> <p>The data only contains distributable raw data and R-codes.</p>
Stormwater runoff pollution of an existing catchment consisting essentially of apartment buildings in Braunschweig/Germany.
<p>This dataset includes stormwater runoff concentrations of pollutants (COD, TP, DP, NO<sub>3</sub>, NH<sub>4</sub>, TSS) taken from a sampling point in Braunschweig, Germany. The total catchment size is 5 ha with approximately 1.8 ha total imperviousness consisting essentially of apartment buildings from the 1970s and roads. This catchment was chosen for comparatively clear delimitation to different land uses considering location-independent results. Samples were taken as part of the research project TransMiT (https://www.transmit-zukunftsstadt.de/). More information including sampling and analytical methods are detailed in the corresponding journal paper "Dynamization of Urban Runoff Pollution and Quantity" and supplementary data, submitted to the MDPI-journal Water.</p> <p>Description of fields:</p> <p>- SamplingPoint:<br> - storm sewer: stormwater runoff sampled during individual rain events in a storm water sewer manhole receiving runoff from the entire catchment<br> - SamplingTime: time of sampling during individual rain event (CET)<br> - COD: measured value of chemical oxygen demand (COD)<br> - TP: measured value of total phosphorus (TP)<br> - DP: measured value of dissovled phosphorus (COD)<br> - NO<sub>3</sub>: measured value of nitrate (NO3)<br> - NH<sub>4</sub>: measured value of ammonium (NH4)<br> - TSS: measured value of total suspended solids (TSS)<br> - N/A: data not available<br> - UnitsAbbreviation: all data given in milligram per litre (mg/L)</p> <p>The data file is provided in comma separated format ("TransMiT.csv") and contains concentrations of all samples.</p> <p> </p>
Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment
<p>The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019.</p> <p>Data deposit in preparation for article:</p> <p>Effect of spatial input data quality on the uncertainty of the<br> SWAT model, submitted 2022</p> <p>Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn<br> Uuemaa</p> <p>contact: alexander.kmoch@ut.ee</p>
Rosalia: An experimental research site to study hydrological processes in a forest catchment - data repository
<p>This repository is a supplement to the paper <strong>Fürst, J., et al. (2021). “Rosalia: an experimental research site to study hydrological processes in a forest catchment.” Earth Syst. Sci. Data 13(8): 4019-4034.</strong></p> <p>Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19<sup>th</sup> and early 20<sup>th</sup> century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called “Rosalia” with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the “Rosalia” instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum.</p> <p>This repository contains the datasets collected by a monitoring network of 4 discharge gauging stations, 7 rain-gauges, together with observations of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community.</p>
Data and code for: A conceptual model-based sediment connectivity assessment for patchy agricultural catchments
<p>Authors: Pedro V G Batista, Peter Fiener, Simon Scheper, Christine Alewell</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Abstract</p> <p>The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. In specific, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the WaTEM/SEDEM model using high resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km<sup>2</sup>) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Metainformation</p> <p>This dataset includes:</p> <p>1 - The input data used for running the WaTEM/SEDEM model in the Baldegg catchment.</p> <p>2 - The discharge and sediment concentration data used for producing the sediment rating curves for the tributaries of the Lake Baldegg.</p> <p>3 - The model and sediment rating curve output data.</p> <p>4 - The R scripts for running the WaTEM/SEDEM model in the Baldegg catchment, the code for producing the sediment rating curves, and the code for summarising and analysing the model output data.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The sediment concentration and water discharge data were supplied by Robert Lovas, from the Department of Environment and Energy of the Canton of Lucerne.</p> <p>The model input data were adapted from freely available ©swisstopo geodata products:</p> <p>Swisstopo. SwissALTI3D. Das hoch aufgelöste Terrainmodell der Schweiz, 2014.</p> <p>Swisstopo. Swiss Map Vector 25 Beta, Das digitale Landschaftsmodell der Schweiz. 2018.</p> <p>Swisstopo. SwissTLM3D. Das grossmassstäbliche Topografische Landschaftsmodell der Schweiz, 2020.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further information we refer to our preprint: https://doi.org/10.5194/hess-2021-231</p> <p> </p> <p> </p>
Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment
<p>This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm.</p> <p>The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data.</p> <p>Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69.</p> <p>Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates.</p> <p>A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes.</p> <p>References</p> <p>J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863.</p> <p>S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026.</p> <p>J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.</p>
Detailed sap flow monitoring data at Weierbach catchment, Luxembourg
<p>The current repository contains the dasometric data of monitored trees, hourly sap flow data, and tree surveys carried out at the Weierbach catchment during 2019 and 2020. The monitored trees include the following species:</p> <p>- Beech (<em>Fagus sylvatica </em>L.)</p> <p>- Douglas Fir (<em>Pseudotsuga menziesii</em> (Mirbel) Franco)</p> <p>- Spruce (<em>Picea abies </em>L.)</p> <p>- Oak (hybrids of <em>Quercus petraea</em> (Matt.) Liebl. and <em>Quercus robur </em>L.)</p> <p>The sap flow data for all the trees is presented in cm/hr.</p> <p>The bark data presented in the "dasometry_individual_trees.csv" was obtained from the literature.</p>
Supplementary data: Rain-on-snow events in mountainous catchments under climate change
<p>The file in this record represents supplementary data for the journal paper Hotovy, O., Nedelcev, O., Seibert, J., Jenicek, M. (2024): Rain-on-snow events in mountainous catchments under climate change submitted to Hydrology and Earth System Sciences.<br>The presented files contain daily simulations of the HBV rainfall-runoff model for 93 mountain catchments in Czechia, Germany and Switzerland. The model simulated different water balance components, such as runoff, base flow, snow water equivalent, evapotranspiration, and soil and groundwater storages for the study period 1980-2010 as well as hydrological projections assuming different increases in air temperature and precipitation.</p>
Salt gauging and stage-discharge curve, Avançon de Nant, outlet Vallon de Nant catchment
<p>This data set contains the salt gaugings completed over the period 2016 - 2017 to establish a stage-discharge curve at the <a href="https://s.geo.admin.ch/7834d541d2">outlet of the Vallon de Nant catchment</a>. The data set contains also the latest version of the estimated stage-discharge curve, compared to the theoretical curve estimated from the geometric properties of the weir.</p> <p>The salt gaugings have been obtained independently by two different research groups from</p> <ul> <li>Institute of Earth Surface Dynamics (<a href="https://www.unil.ch/idyst/en/home/menuinst/research-topics/water-and-elemental-cycles/catchment-hydrology.html">IDYST</a>), Faculty of Geosciences and Environement (FGSE), University of Lausanne (UNIL),</li> <li>Stream Biofilm and Ecosystem Research Laboratory (<a href="https://sber.epfl.ch/">SBER</a>), School of Architecture, Environmental and Civil Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland,</li> </ul> <p>The stream gauging station itself was constructed thanks to a joint funding by the Swiss Federal Institute for Forest, Snow and Landscape (WSL), University of Lausanne (chair of Prof. S. Lane) and ETH Zürich (chair of Prof. J. Kirchner).</p> <p>The gauging station is maintained by the <a href="http://www.wsl.ch/en/about-wsl/organization/research-units/mountain-hydrology.html">Mountain Hydrology and Mass Movements research uni</a>t of the Swiss Federal Institute for Forest, Snow and Landscape (WSL) and by the<a href="https://www.unil.ch/idyst/en/home.html"> Institute of Earth Surface Dynamics</a> of University of Lausanne.</p> <p> </p>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</p>
Geochemistry of a weathering profile and natural waters in the upland Mediterranean catchment of Sapine, Mt-Lozère, southern France
<p>This publication reports geochemical data from an instrumented catchment of the Service National d’Observation “Observatoire Hydro-Météorologique Cévennes-Vivarais” (OHMCV) and of the National Research Infrastructure “Observatoires de la Zone Critique: Applications et Recherche” (OZCAR) of the French “Centre National de la Recherche Scientifique” (CNRS).</p> <p>The presented data are originally contained in Kuessner, M.L. (2018). The interplay between chemical weathering and hydrology in the Critical Zone - insight from trace elements and lithium isotopes [Doctoral dissertation, Institut de Physique du Globe de Paris]</p> <p>Contents</p> <p>Table SC1. Analyses of soil samples in Sapine Creek</p> <p>Table SC2. Analyses of rock samples in Sapine Creek</p> <p>Table SC3. Analyses of rain samples in Sapine Creek and Cloutasse catchments</p> <p>Table SC4. Analyses of stream samples in Sapine Creek</p> <p>Table SC5. Analytical limits for quadrupole ICP-MS measurements</p> <p>Table SC6. Analyses of reference materials for isotope measurements</p>
River flow and catchment rainfall records for the River Garry at Invergarry (Inverness-shire), Scotland, 1913-1915
<p>Daily mean flows for the River Garry at Invergarry (Inverness-shire), Gauge A1, spanning the period 1913-01-01 to 1915-12-31.</p> <p>Data collected by Capt. W. N. McClean via his organisation River Flow Records, assisted by local observers.</p> <p>We acknowledge the sponsorship of Scottish Hydro-Electric and the Scottish Environment Protection Agency in suporting the costs of creating digital time series.</p> <p>Subsequent to the colletion of these records, the River Garry was developed by the construction of dams and hydro power stations below Loch Quoich and Loch Garry.</p> <p>The Scottish Environment Protection Agency (SEPA) subsequently opened a river flow gauging station on the River Garry at Craigard in 1997, approximately 3 km upstream of McClean's gauge, operated until May 2011.</p>
Measuring water quality parameters to estimate Nitrate concentration in surface water in Bonet catchment, Sligo, Ireland
<p><span>Time series data of surface water quality (temperature, pH, dissolved oxygen, oxidation-reduction potential and electrical conductivity) collected from May 2024 to September 2024 at 1m intervals. The file contains tabular data with the following columns: Date and time, Battery (%), temperature (ºC), fix Quality in fix code (Fix), Latitude (in deg), Longitude (in deg), pH,<span> </span>electrical conductivity (µS/cm), TDS (in ppm),<span> </span>Salinity in PSU(ppt), Specific Gravity (in SG), Dissolved Oxygen (in mg/L), Oxygen Saturation (in %), ORP (in mV), Altitude (in meters), Ground Speed (in m/s), Horizontal dilution, Satellites in number.</span></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.