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223 results for “future changes”
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean's biological carbon pump
<p>This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change (https://doi.org/10.1038/s41558-024-02233-6). The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.</p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Future monthly discharge and water temperature simulations under global change (CMIP6)
<p>Monthly discharge (m3 s-1) and water temperature (K) simulated by a global hydrological model coupled to a surface water quality model (<i>PCR-GLOBWB2-DynQual)</i> for the time period 2005 - 2100, for an ensemble of 15 projections based on three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and five general circulation models (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0)</p><p>Output data are provided at 10km resolution and are averaged at monthly temporal resolution.</p><p>These datasets were generated as part of the work presented in: Jones, E.R., Bierkens, M.F.P., van Puijenbroek, P.J.T.M. <i>et al.</i> Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. <i>Nat Water</i> <strong>1</strong>, 602–613 (2023). <a href="https://www.nature.com/articles/s44221-023-00105-5#citeas">https://doi.org/10.1038/s44221-023-00105-5</a></p><p>Relevant model description papers can be found at the following links:</p><ul><li><i>PCR-GLOBWB2</i>: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, <i>Geoscientific Model Development</i>, 11, 2429–2453, <a href="https://gmd.copernicus.org/articles/11/2429/2018/gmd-11-2429-2018.html">https://doi.org/10.5194/gmd-11-2429-2018</a>, 2018.</li><li><i>DynQual</i>: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.: DynQual v1.0: a high-resolution global surface water quality model, <i>Geoscientific Model Development</i>, 16, 4481–4500, <a href="https://gmd.copernicus.org/articles/16/4481/2023/gmd-16-4481-2023.html">https://doi.org/10.5194/gmd-16-4481-2023</a>, 2023.</li></ul><p>Additional information on the water temperature modelling can also be found at:</p><ul><li>Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (Rens): High-resolution global water temperature modeling. <i>Water Resources Research</i>, 55, 2760–2778, <a href="https://doi.org/10.1029/2018WR023250">https://doi.org/10.1029/2018WR023250</a>, 2019</li><li>van Beek, L. P. H., Eikelboom, T., van Vliet, M. T. H., and Bierkens, M. F. P.: A physically based model of global freshwater surface temperature, <i>Water Resources. Research</i>, 48, W09530, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2012WR011819">https://doi.org/10.1029/2012WR011819</a> , 2012.</li></ul>
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes
<p>Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3
<p>The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context".</p> <p>The second file S3 contains the economic data and the climate change impacts of the same article.</p> <p>In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.</p> <p> </p>
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
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>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Future seasonal changes in habitat for Arctic whales during predicted ocean warming
<p><span>Ocean warming is causing shifts in the distributions of marine species, but the location of suitable habitats in the future is unknown, especially in remote regions such as the Arctic. Using satellite tracking data from a 28-year long period, covering all three endemic Arctic cetaceans (227 individuals) in the Atlantic sector of the Arctic, together with climate models under two emission scenarios, species distributions were projected to assess responses of these whales to climate change by the end of the century. While contrasting responses were observed across species and seasons, long-term predictions suggest northward shifts (243 km in summer vs. 121 km in winter) in distribution to cope with climate change. Current summer habitats will decline (mean loss: </span><span>-</span><span>25%), while some expansion into new winter areas (mean gain: +3%) is likely. However, comparing gains vs. losses raises serious concerns about the ability of these polar species to deal with the disappearance of traditional colder habitats.</span></p>
Present-day and future changes in the hydrology of the Bhagirathi Basin
<p>This repository contains the daily outputs (Jan 1, 1991 to Dec 31 2020) produced in the project SDC project. The folder 'Final_full_30yrs_baseline.rar' contains all the historical outputs generated from the SPHY model. The folder contains data in the different formats (spatial and non spatial) '.map','.csv' and '.tss'</p> <p>The folder 'Climate_change.rar' contains climate runs from (Jan 1, 2021 to Dec 31 2100) for 4 GCM-RCM and ssp combinations.</p>
Historical and future climate change fosters expansion of Australian harvester termites, Drepanotermes
<p>Past evolutionary adaptations to Australia's aridification can help us to understand potential responses of species in the face of global climate change. Here, we focus on the Australian-endemic termite genus <em>Drepanotermes</em>, which is widespread in semi-arid and arid regions of Australia. We used species delineation, phylogenetic inference, and ancestral state reconstruction to investigate the evolution of mound-building and in relation to reconstructed past climatic conditions. Our results suggest that mound-building evolved several times independently, apparently facilitating expansion into tropical and mesic regions of Australia. Strong phylogenetic signal of bioclimatic variables, especially of limiting environmental factors (e.g. precipitation of warmest quarter), indicates that climate exerts a strong selective pressure. Finally, we used environmental niche modeling to predict present and future habitat suitability for eight <em>Drepanotermes</em> species. Abiotic factors such as annual temperature contributed disproportionately to calibrations, while the inclusion of biotic factors like vegetation cover improved ecological niche models in some species. A comparison between present and future habitat suitability under two different emission scenarios revealed continued suitability of current ranges as well as substantial habitat gains for most studied species, irrespective of nesting habit, yet extensive range expansions in the near future are likely precluded by low dispersal abilities.</p>
Supplementary data: Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments
<p>The files in this record represent supplementary data for the article titled “Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments” in Water Resources Research. The files contain simulations of the HBV rainfall-runoff model for 14 alpine catchments in Switzerland. The model simulated different water balance components (such as runoff, snow water equivalent and evapotranspiration) for the reference period 1980-2009 and the three scenario periods (2020-2049, 2045-2074 and 2070-2099) using the A1B emission scenario.</p>
FIGURE 2 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 2 Distributions (left), maximum likelihood (ML) phylogenetic trees (middle), principal component analysis (PCA) ordination plots from cranial measurements, photographs or drawings of the baculum and sonograms of echolocation calls (right) of selected groups of paramontane southern African bats having ranges categorized as arid (red symbols), Mediterranean (turquoise symbols), temperate-montane (blue), savanna-montane (orange), and tropical rain forest (green; see Table S1 for classification): horseshoe bats (Rhinolophus) of the R. capensis (a), R. darlingi (b), R. ferrumequinum (c), R. fumigatus (d) groups, wing-gland bats (Family Cistugidae, genus Cistugo (e), and long-eared serotine bats of the genus Laephotis (f)). Distribution maps were based on IUCN Redlist maps (open polygons), correctly identified vouchers from molecular studies (colored symbols; this study; GenBank; Curran et al., 2022; Demos et al., 2019; Dool et al., 2016; Taylor et al., 2018) and skulls measured in this study (crosses). In a few cases (see legends), GBIF records were indicated for the Angolan range of species. Gray shading indicates elevations over 1200 m a.s.l. Phylogenetic trees are shown for sub-clades (i.e., excluding outgroups) of three separate ML analyses undertaken with IQTREE of Rhinolophus, Cistugo, and Laephotis (Figures S2–S4). Values above nodes (in bold) represent median dates obtained for corresponding nodes from separate BEAST analyses in Figures S5–S7 (see text for details). Node support values for ML trees, obtained by the IQTREE program, are given below the nodes for SH-like approximate likelihood ratio tests (SH-aLRT), aBayes posterior probabilities, and ultra-fast bootstrap values (UFBS) respectively (see text for details). Tip labels marked in bold represent new sequences from this study. Underlined tip labels represent two instances of mtDNA introgression where morphologically distinct taxa from different biomes have near-identical cyt-b sequences. Species ranges of echolocation call peak frequencies were obtained from the literature for Rhinolophidae (Adams & Kwiecinski, 2018; Curran et al., 2022; Jacobs et al., 2013; Jacobs et al., 2017; Laverty & Berger, 2020; Monadjem et al., 2020; Mutumi et al., 2016; Odendaal & Jacobs, 2011; Odendaal et al., 2014; Schoeman & Jacobs, 2008), Cistugo (Monadjem et al., 2020; Schoeman & Jacobs, 2003, 2008), and long-eared Laephotis (Adams & Kwiecinski, 2018; Jacobs et al., 2005; Monadjem et al., 2020; Pierce et al., 2011). Bacula photographs and drawings were obtained from this study as well as Benda and Vallo (2012), Taylor et al. (2018), Curran et al. (2022). Abbreviation of South African province names: EC, Eastern Cape; FS, Free State; GP, Gauteng; KZN, KwaZulu-Natal; LP, Limpopo; MP, Mpumalanga; NC, Northern Cape; WC, Western Cape. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 1 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 1 Maps of southern, central, and eastern Africa showing (a) topographical features referred to in this study (see text for details), and (b) the extent of minimum monthly temperatures (bioclim6) <0°C from present and past (last glacial maximum [LGM]) models (from Worldclim; https://www.worldclim.com/; see Methods for more details). Gray or darker shading in both maps indicates mountains>1200 m in elevation. In (a), the acronym HEAN stands for the Highlands and Escarpments of Angola and Namibia (Mendelsohn et al., 2023); SEAMA stands for the South-East African Montane Archipelago (Bayliss et al., 2024); LMEE stands for the Limpopo–Mpumalanga– Eswatini Escarpment (Clark et al., 2022). The map in (b) shows distribution points of horseshoe bats, Rhinolophus (crosses), wing-gland bats, Cistugo (open triangles) and long-eared bats, Laephotis (open squares) based on morphological and molecular results from this study and from published a GenBank cyt-b sequences. In (b), minimum monthly temperatures <0°C indicated for the present (blue) and LGM (red), approximating the extent of frost (and hence temperate grasslands) currently and during the LGM (idea from Brain, 1985). Map lines delineate study areas and do not necessarily depict accepted national boundaries.
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.