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.
1,121
datasets available to search
ShareScore release 0.9.0
Dataset results
1,121 results for “weathering”
Data from: Multiple effects of weather on common waxbill group foraging and social behavior
<p>The weather poses challenges for wildlife. Environmental challenges can be responded to at the group level by social animals, but the influence of weather on group behavior is poorly understood. We investigated how the weather affects behavior in a gregarious species by monitoring common waxbills <em>(Estrilda astrild</em>) in a large mesocosm during five years. We found seasonal patterns on collective foraging, aggressiveness, and the structure of the social network, usually showing two cycles per year: one peaking in Spring and a smaller one in late Summer. Controlling for seasonality, we found behavioral changes related to increased energy demands on colder and/or cloudier days, such as more frequent and larger foraging groups that resulted in less structured social networks. Rain and wind disturb movement, and we found that on rainy days foraging group journeys became briefer and more synchronous, resulting in stronger associations between individuals and less structured networks, and that on windy days foraging groups were less frequent, larger, and with more within-group aggression. The results show that the weather has more varied effects than anticipated on ecologically-relevant group behavior. We discuss how such weather-related effects can improve predictions of how social animals will react to environmental changes.</p>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Exemplary dataset measured by Bresser WIFI Weather Station from 21.August 2023
<p><span>The Bresser WIFI Colour Weather Station is a convenient weather station for citizen science projects. It is equipped with sensors to measure wind speed, wind direction, humidity, temperature, rainfall, UV levels and light intensity. The WIFI feature allows users to share their data on weather platforms such as AWEKAS, Weather Underground or Weather Cloud.</span></p>
Global economic impact of weather variability on the rich and the poor
<p>This repository provides data and code to reproduce the results of the publication "L. Quante, S. N. Willner, C. Otto, and A. Levermann (2024). <a href="https://doi.org/10.1038/s41893-024-01430-7" target="_blank" rel="noopener">Global economic impact of weather variability on the rich and the poor, <em>Nature Sustainability</em></a>".</p> <p><strong>This repository contains:</strong></p> <p>- 01_forcing_marginal_effects: marginal effects data to be combined with climate model data to generate the impacts<br>- 02_impact_data - timeseries of production disruption, generated as described in the methods "Estimation of direct production losses", using the code in 03_impact_downscaling<br>- 04_acclimate_settings: example settings for acclimate runs using the impact files. Due to licensing restrictions, we can not provide the EORA network data used for the simulations piublicly. 04b_income_share_generation provides the income data (from the World Bank) used to disaggregate consumption data in the EORA network to five income quintiles.<br>- 05_pre_processed_simulation_output: the output from the acclimate simulations, pre-procesed for analysis using the code provided in 06_analysis-code<br>- 07_figures_tables: figures and supplementary data.<br>- 08_addtional_data: data used for plotting, i.e. the World Bank classification of country income levels.</p> <p><strong>Dependencies:</strong></p> <ul> <li>a working environment is provided in environment.yml</li> <li>the Acclimate post-processing package can be downloaded from the respective GitHub repostory with <code>git@github.com:acclimate/post-processing.git</code>. Switch to the develop branch with <code>git checkout develop</code> and install the package with <code>conda develop .</code> from within the repository</li> </ul>
Radiation Effects on Satellites during Extreme Space Weather Events (pre-publication dataset)
<p>Data for submitted paper entitled "Radiation Effects on Satellites during Extreme Space Weather Events".</p> <p>Submitted to AGU Space Weather.</p> <p> </p>
He'eia Weather Data
<p>Weather data from Kako'o O'iwi at 'Auwai 1. </p>
2006\London\SWT Weather data
<p>London\ 2006\ SWT</p> <p><strong>Data Type: </strong>Weather station</p> <p><strong>Site information: </strong></p> <p>Latitude: 51.487760 Longitude: -0.091069 Anemometer height: 60 m</p> <p>Owner: Bill Legassick, Southwark Council.</p> <p>Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705 Email: Bill.Legassick@southwark.gov.uk</p> <p><strong>Sensor information </strong> </p> <p>Sensor type Model Date installed<br>Anemometer CDL Windset (EC8) 1999<br>Rain gauge Campbell Scientific ARG-100 1999<br>Temperature probe T107_C 1999<br>Humidity probe HMP45A 1999</p> <p><strong>Files:</strong> Are Zipped by monthFilenames: Weather_Data_2006.CSV</p> <p><strong>Filetype</strong>: comma delimited</p> <p><strong>Header Row</strong>:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly Average Direction,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,Maximum Hourly Rainfall Rate,Time of RainfallData: hourly averages</p>
2010\ London\ SWT Weather data
<p>2010\ London\ SWT Weather data</p> <p>Data Type: Weather station<br> Site information:<br> Latitude: 51.487760 <br> Longitude: -0.091069<br> Anemometer height: 60 m<br> Owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705<br> Email: Bill.Legassick@southwark.gov.uk<br> Sensor information <br> Sensor type Model Date installed<br> Anemometer CDL Windset (EC8) 1999<br> Rain gauge Campbell Scientific ARG-100 1999<br> Temperature probe T107_C 1999<br> Humidity probe HMP45A 1999<br> Files: Are Zipped<br> Filenames: Weather_Data_2008.CSV<br> Filetype: comma delimited<br> Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly Average Direction,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,Maximum Hourly Rainfall Rate,Time of Rainfall<br> Data: hourly averages2010\ London\ SWT Weather data</p>
1999-2017 - London SWT Weather data
<p>1999-2017 - London SWT Weather data</p> <ol> <li><strong>Site:</strong> SWT</li> <li>Country\City\Location: UK\London\ Southwark WoodsfordTower <strong>SWT</strong></li> <li>Latitude: 51.487760; Longitude: -0.091069</li> <li>Anemometer height: 60 m</li> <li>Weather station owner: Bill Legassick, Southwark Council. Contact: Tel: 020 7525 4253 | Fax: 020 7525 5705</li> <li>Email: Bill.Legassick@southwark.gov.uk</li> </ol> <ol> <li><strong>Sensor: </strong></li> <li>Sensor type:---- Model -------------: Date Installed</li> <li>Anemometer : CDL Windset (EC8) ......... : 1999</li> <li>Rain gauge : Campbell Scientific ARG-100 : 1999</li> <li>Temperature: T107_C .....................: 1999</li> <li>Humidity : HMP45A .....................: 1999</li> </ol> <ol> <li><strong>Files:</strong> Are Zipped</li> <li>Filenames: Weather_Data_2008.CSV</li> <li>Filetype: comma delimited</li> <li> <p>Header Row:Date and Time,Battery Voltage,CR10 Temperature,Wind Direction 10 Minutes,Wind Speed 10 Minutes,Wind Gust 10 Minutes,Hourly AverageDirection,Hourly Average Speed,Hourly Maximum Gust,Hourly Gust Time,Hourly Gust Direction,Last Minute Average Temperature,Total Hourly Rain,Average RH over previous minute,Maximum Hourly Air Temperature,Minimum Hourly Air Temperature,MaximumHourly Rainfall Rate,Time of Rainfall</p> </li> <li>Data: hourly averages</li> </ol>
Videos for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground"
<p>Supplementary videos for the paper "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" by S. Scher and G. Messori, Geoscientific Model Development 2019</p>
Simulated corn yield, drainage nitrate load and residual soil nitrate as affected by weather, management and soil-state at seven long-term US Midwest research sites
<p>This data set is the product of a factorial simulation experiment designed to quantify the impact of five environmental factors and five management practices on NO<sub>3</sub> leaching and yield in corn cropping systems. The simulation model used was the Agricultural Production Systems SImulator (APSIM; <a href="https://www.apsim.info/">https://www.apsim.info/</a>). Description of the simulation factors are provided in the metadata.txt file. </p> <p> </p> <p> </p>
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
Figure. The location of the study area (black dot) near the Tisza River in East Hungary. in The first recording of the threatened species, the European weather loach, Misgurnus fossilis (Berg, 1949), in the diet of the pike
Figure. The location of the study area (black dot) near the Tisza River in East Hungary.
Datas for Leveraging ecosystems responses to enhanced rock weathering in mitigation scenarios
<p>This dataset contains the jupyter notebook calibrating the P-cycle emulator based on the ORCHIDEE-CNP outputs produced by Daniel S. Goll. The zip file contains a set of results from the simulations. </p>
Supplementary XRD Material for "Experimental Constraints on Barium Isotope Fractionation during Adsorption-Desorption Reactions: Implications for Weathering and Erosion Tracer Applications"
<p><strong>Repository Overview:</strong> Supplementary powder X-ray diffraction (XRD) data for the study "Experimental Constraints on Barium Isotope Fractionation during Adsorption-Desorption Reactions: Implications for Weathering and Erosion Tracer Applications"</p> <p><strong>Data Provided:</strong> Powder X-ray diffractograms of the minerals used in the laboratory batch experiments. Pow</p> <p><strong>Minerals Analysed:</strong></p> <p><span>Clay Minerals:</span></p> <ul> <li>Kaolinite (KGa-2): purchased from the Clay Minerals Society (CMS).</li> <li>Montmorillonite (SWy-2): purchased from the Clay Minerals Society (CMS).</li> </ul> <p><span>Iron Oxyhydroxide Minerals:</span></p> <ul> <li>Goethite synthesised under alkaline conditions in the laboratories at the Department of Earth Sciences, University of Cambridge, following the method detailed in Schwertmann and Cornell (2008).</li> <li>2-line ferriydrite synthesised in the laboratories at the Department of Earth Sciences, University of Cambridge, following the method detailed in Schwertmann and Cornell (2008) methods.</li> </ul> <p><strong>XRD Methods:</strong></p> <ul> <li>Instrument and Set Up: Theta-Theta D8 Bruker Advance diffractometer with a Mo anode, Lynxeye XE-T PSD detector and 2 mm divergent slit.</li> <li>Sample Preparation: Zero-background monocrystalline silicon plate was used to minimize sample mass. The X-ray diffractogram of the silicon plate was measured prior to each sample measurement.</li> <li>Clay Minerals: 0.02° step size and a 3s step rate.</li> <li>Iron Oxyhydroxide Minerals: 0.03° increments and a 1s step rate.</li> </ul> <p><strong>Purpose of XRD Analyses:</strong></p> <ul> <li>To verify the correct synthesis of iron oxyhydroxide phases and to detect impurities.</li> <li>To monitor any mineralogical changes that occurred during adsorption-desorption reactions with water.</li> </ul> <p><strong>Data Format</strong></p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>twotheta</td> <td>2θ</td> <td>°</td> </tr> <tr> <td>intensity</td> <td>intensity</td> <td>counts per second (cps)</td> </tr> <tr> <td>ID</td> <td>mineral ID</td> <td>unitless</td> </tr> <tr> <td>mineral</td> <td>mineral name</td> <td>unitless</td> </tr> <tr> <td>duration</td> <td>reaction duration</td> <td>minutes</td> </tr> </tbody> </table> <p><strong>Extra Notes</strong></p> <p>A reaction duration of zero minutes corresponds to an unreacted mineral sample.</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
Data from the Weather Station of Campinas, State of São Paulo, Brazil
<p>Daily meteorological data from the the weather station of Campinas, State of São Paulo, Brazil. The weather station belongs to the Agronomic Institute of Campinas (IAC/APTA/SAA-SP).</p>
Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'
<p><span>1. ALK.mat </span></p> <p><a name="OLE_LINK86"></a><a name="OLE_LINK87"></a><span>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m. </span></p> <p><span>Units: meq/m<sup>3</sup></span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1a, b and Fig.2 </span></p> <p><span> </span></p> <p><span>2. pH.mat</span></p> <p><span>Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH. </span></p> <p><span>Units: unitless</span></p> <p><a name="OLE_LINK92"></a><a name="OLE_LINK93"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1c and Fig.3 </span></p> <p><span> </span></p> <p><span>3. total_DIC_ALK.mat</span></p> <p><a name="OLE_LINK88"></a><a name="OLE_LINK89"></a><span>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory.</span></p> <p><span>Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d)</span></p> <p><span>Data in this file is used in Fig. 1d and Fig. 4.</span></p> <p><span> </span></p> <p><span>4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc</span></p> <p><span>Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><span>Coordinates: DIC: longitude * latitude* depth* time</span></p> <p><span>Data in this file is used in Fig. 5</span></p> <p><span> </span></p> <p><span>5. pCO2.mat</span></p> <p><span>Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2.</span></p> <p><span>Unit: ppmv</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig.6</span></p> <p><span> </span></p> <p><span>6. FG_CO2.mat</span></p> <p><a name="OLE_LINK94"></a><a name="OLE_LINK95"></a><span>Description: This file includes three structs, </span><a name="OLE_LINK90"></a><a name="OLE_LINK91"></a><span>FG_</span><span>CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK96"></a><a name="OLE_LINK97"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 7</span></p> <p><span> </span></p> <p><span>7. NPP.mat</span></p> <p><a name="OLE_LINK98"></a><a name="OLE_LINK99"></a><span>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK104"></a><a name="OLE_LINK105"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 8</span></p> <p><span> </span></p> <p><span>8. spC.mat, diatC.mat, diazC.mat</span></p> <p><span>Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude, mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><a name="OLE_LINK108"></a><a name="OLE_LINK109"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK110"></a><a name="OLE_LINK111"></a><span>Data in these files are used in Fig. 9</span></p> <p><span> </span></p> <p><span>9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat</span></p> <p><span>Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux, para_a10: 10-years average of CaCO3 flux. </span></p> <p><span>Unit: nmol/cm<sup>2</sup>/s (need to convert unit to mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK112"></a><a name="OLE_LINK113"></a><span>Data in these files are used in Fig. 10</span></p> <p><span> </span></p> <p><span>10. POC.mat</span></p> <p><span>Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in these files are used in Fig. 11</span></p> <p><span> </span></p> <p><span>11. atmoCO2.mat</span></p> <p><span>Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO<sub>2</sub> variation with the unit ppm. </span></p> <p><span>Data in this file is used in Fig.1f</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </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.