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601 results for “global changes”
Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change
<p><strong>This repository contains the dataset linked to the following publication:</strong></p> <p><strong>Article title:</strong> Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change</p> <p><strong>Journal title: </strong>Water Research</p> <p><strong>Article Number: </strong>WR_118721</p> <p><strong>doi: </strong>https://doi.org/10.1016/j.watres.2022.118721</p> <p><strong>Abstract: </strong>Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015–2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.<br> </p> <p><strong>Data description</strong><br> by Xiangzhen Kong (xiangzhen.kong@ufz.de; xzkong@niglas.ac.cn)<br> 2022-06-20</p> <p>1. Discharge in the streams from 2010 to 2021 at YRZ site, and from 2010 to 2020 at YHZ_Q site.</p> <ul> <li>File name: dat_discharge_stream_YRZ_YHZ_2010_2021_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> <p>2. Nitrate concentration in the streams from 2011 to 2019 at both YRZ and YHZ_Q sites.</p> <ul> <li>File name: dat_nitrate_stream_YRZ_YHZ_2011_2019_daily.csv</li> <li>Note: The data is at daily basis but also available at 15-min high frequency basis, which can be requested from the authors.</li> </ul> <p>3. Water quality data in the inflows from 2010 to 2021 at biweekly basis, from YRZ and YHZ_WQ sites.</p> <ul> <li>File name: dat_waterquality_stream_YRZ_YHZ_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> <p>4. Water quality data in the predams from 2010 to 2021 at biweekly basis, from YR1 and YH1 sites.</p> <ul> <li>File name: dat_waterquality_predams_YR1_YH1_2010_2021_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at the depth of 0.5m under water surface, from both probe and lab.</li> </ul> <p>5. Water quality data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites.</p> <ul> <li>File name: dat_waterquality_predams_YR3_YH3_2010_2015_biweekly.csv</li> <li>Note: The data is at biweekly basis, measured at various water depth, only from lab.</li> </ul> <p>6. CTD and BBE probe profile data in the predams from 2010 to 2015 at biweekely basis, from YR3 and YH3 sites.</p> <ul> <li>Note: Data stored in the folder "probe_profiles_predam_YR3_YH3_2010_2015_biweekly". The data is at biweekly basis, measured at various water depths. The measurements include water temperature (Celcius), DO (mg/L), Chl-a (mg/m3), bluegreen, green and diatom (all in Chl-a, mg/m3)</li> </ul>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Data for figures in the global streamflow change paper
<p>This is the dataset used for generating figures 1-4 in the manuscript of "<strong>Future global streamflow declines likely more severe than previously estimated</strong>"</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Climate change threats to the global functional diversity of freshwater fish
<p>This dataset provides supplementary information for the paper entitled "Climate change threats to the global functional diversity of freshwater fish".</p> <p> </p> <p><strong>Fish trait data</strong></p> <p>fish_traits_removed.csv<br> - species with missing trait values were removed<br> - species coverage: 3,792</p> <p>fish_traits_imputed.csv<br> - missing trait values were imputed<br> - species coverage: 11,425</p> <p>Traits<br> - HLrel = relative head length<br> - BDrel = relative body depth<br> - Troph = trophic level<br> - K = relative growth rate</p> <p><br> <strong>Geospatial data</strong></p> <p>Files<br> Data under the assumption of no dispersal<br> - SR.tif: species richness<br> - FRic.tif: functional richness<br> - FEve.tif: functional evenness<br> - FDiv.tif: functional divergence<br> - FRic_loss.tif: functional richness loss<br> - FEve_loss.tif: functional evenness loss<br> - FDiv_loss.tif: functional divergence loss</p> <p>Data under the assumption of maximal dispersal<br> - SR_dispersal.tif: species richness<br> - FRic_dispersal.tif: functional richness<br> - FEve_dispersal.tif: functional evenness<br> - FDiv_dispersal.tif: functional divergence<br> - FRic_loss_dispersal.tif: functional richness loss<br> - FEve_loss_dispersal.tif: functional evenness loss<br> - FDiv_loss_dispersal.tif: functional divergence loss</p> <p>Layers<br> - imp_*: missing trait values were imputed<br> - rem_*: species with missing trait values were removed<br> - *_hist: historical reference scenario<br> - *_1p5: warming level of 1.5°C<br> - *_2p0: warming level of 2.0°C<br> - *_3p2: warming level of 3.2°C<br> - *_4p5: warming level of 4.5°C</p> <p>Spatial resolution: 0.08333333, 0.08333333 (x, y)<br> Spatial extent: -180, 180, -60, 85 (xmin, xmax, ymin, ymax)<br> Coordinate reference system: WGS84</p>
Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers
<p>Raw data (occurrences and environmental predictors) used in the manuscript "Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers"</p>
Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology
<p>Zip folder conaining the data and code that support the findings of <em>Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology.</em></p> <p>The <em>Data_code.zip</em> folder contains four subfolders with the following files:</p> <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>
Data for "Pollinator Conservation Paradox: Exotic Forbs Support Native Pollinators Under Global Changes" by Nelson, Seabloom and Borer 2025, California grasslands, 2023-2024
Data for analysis on how plant provenance mediates plant-pollinator interaction responses to fertilization and herbivore exclusion, associated with Nelson, Seabloom, and Borer 2025. Data on pollinator visitation and floral abundance were collected in plots that received factorial experimental treatments of combined nitrogen, phosphorus and potassium with micronutrients by herbivore exclusion fencing in three California grasslands in 2023-2024.
MCR LTER: Coral Reef: Data in support of Edmunds 2012 Global Change Biology, v18 2173-2183
These data were generated from a one-time experiment in support of a coral ecophysiology manuscript; published in Global Change Biology 2012. Edmunds et al. (2012) Global Change Biology 18: 2173-2183 doi:10.1111/j.1365-2486.2012.02695.x These data were collected to test the hypothesis that the response of corals to temperature and pCO2 is consistent between taxa. Juvenile massive Porites spp. and branches of P. rus from the back reef of Moorea were incubated for 1 month under combinations of temperature (29.3 °C and 25.6 °C) and pCO2 (41.6 Pa and 81.5 Pa) at an irradiance of 599 μmol quanta m-2 s-1. Using microcosms and CO2 gas mixing technology, treatments were created in a partly nested design (tanks) with two between-plot factors (temperature and pCO2), and one within-plot factor (taxon); calcification was used as a dependent variable. A compilation of studies placed the present results in a broader context and tested the hypothesis that calcification for individual coral genera is independent of pH, [HCO3 -], and [CO3 2-]. Unlike recent reviews, this analysis was restricted to studies reporting calcification in units that could be converted to nmol CaCO3 cm-2 h-1. These data include coral growth, seawater temperature, and salinity, and seawater carbonate chemistry including total alkalinity, pH, carbon dioxide (pCO2) and saturation state of aragonite (omega).
Data for 'Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study'
<p><strong>This dataset is a supplement to the following publication (please cite that when using the data):</strong></p> <p>Munia et al. 2020. Future transboundary water stress and its drivers under climate change: a global study. Earth’s future. <a href="https://doi.org/10.1029/2019EF001321">https://doi.org/10.1029/2019EF001321</a></p> <p> </p> <p><strong>Water stress category data</strong></p> <p>Dataset presents the water stress category in transboundary basins at sub-basin level for different scenarios (see article for details):</p> <ul> <li> <p>stress_category_Historical.gpkg: stress for years 1980 and 2010</p> </li> <li> <p>stress_category_SSP1‐RCP26.gpkg: stress for year 2050, SSP1‐RCP2.6 scenario</p> </li> <li> <p>stress_category_SSP1‐RCP45.gpkg: stress for year 2050, SSP1‐RCP4.5 scenario</p> </li> <li> <p>stress_category_SSP2‐RCP60.gpkg: stress for year 2050, SSP2‐RCP6.0 scenario</p> </li> <li> <p>stress_category_SSP3‐RCP60.gpkg: stress for year 2050, SSP3‐RCP6.0 scenario</p> </li> </ul> <p> </p> <p><strong>Dataset specifications:</strong></p> <p>Type: geopackage (gpkg)</p> <p>Spatial extent: -165, 141.5, -54.5, 70.5 (xmin, xmax, ymin, ymax)</p> <p>Temporal extent: see above</p> <p>Projection: long/lat WGS84 (EPSG:4326)</p> <p>Information: sub-basin name, country, stress level, stress category</p> <p>Unit: -</p> <p> </p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Indicators of Global Climate Change 2024
<p>This release contains the indicators of global climate change updated to the end of 2024. Datasets included are:</p> <ul> <li>Attribution of historical warming 1850-2024</li> <li>Earth's energy imbalance 1971-2024</li> <li>Effective radiative forcing 1750-2024</li> <li>Global mean surface temperature anomalies 1850-2024</li> <li>Global temperature extreme anomalies 1950-2024</li> <li>Greenhouse gas concentrations 1750-2024</li> <li>Greenhouse gas emissions 1750-2023</li> <li>Remaining carbon budgets in 0.1°C increments</li> <li>Sea level rise 1880-2024 (corrected time bounds)</li> </ul>
A meta-analysis on global change drivers and the risk of infectious disease database and code
<p>Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".</p>
Explaining and predicting animal migration under global change
<p class="MsoNormal"><span>Many migratory species are declining due to global environmental change. Yet, their complex annual cycles make unravelling the impacts of potential drivers such as climate and land-use change on migrations a major challenge. Identifying where, when, and how threatening processes impact species' migratory journeys and population dynamics is crucial for identifying effective conservation actions. Here, we describe how a new migration modelling framework – Spatially-explicit Adaptive Migration Models (SAMMs) – can simulate the optimal behavioural decisions required to migrate across open land- or seascapes varying in character over space and time, without requiring predefined behavioural rules. Models of adaptive behaviour have been used widely in theoretical ecology but have great untapped potential in real-world contexts. Applying adaptive behaviour models across open environments will allow users to explore flexibility in how migratory strategies respond to environmental change and the consequences of migrants not being able to adapt to change. We outline how SAMMs can be used to model migratory journeys through aerial, terrestrial, and aquatic environments, demonstrating their potential using a case study on the common cuckoo (<em><span>Cuculus canorus</span></em>) and comparing modelled to observed behaviours. SAMMs offer a tool to identify the key threats faced by migratory species and to predict how they will adapt future migratory journeys in response to changing environmental conditions.</span></p>
SDUST2020MGCR: a global marine gravity change rate model determined from multi-satellite altimeter data
<p>SDUST2020MGCR.nc is the global marine gravity change rate model covering 70°S~70°N and 0°~360°E on 5′×5′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2020MGCR and an attachment data (GIA MGCR).</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Deep learning reveals hotspots of global oceanic oxygen changes in the 21st century
<p>This is a global ocean-scale dissolved oxygen dataset derived from a deep learning method using hydrometeorological and biogeocheical driver factors. The dataset provides yearly estimates of dissolved oxygen concentration during 2003 to 2020 throughout the whole water column, with a spatial resolution of 0.25° × 0.25°. The unprecedented high-resolution dataset provides information on dissolved oxygen spatial distribution and temporal trend, making it a significant contribution to understanding global warming and anthropogenic stressors. </p>
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
<p>Dataset used to analyize role of climate variability</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.