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162 results for “Spatial ecology”
LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S.
The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
MCR LTER: Coral Reef: Spatial portfolios in coral metapopulations are shaped by spatiotemporal asynchrony in environmental conditions; Data for Srednick et al., 2026 Ecology Letters
Using wavelet analyses of a 19-year coral community timeseries from Moorea, French Polynesia, we quantified timescale-specific population synchrony in four common coral genera and evaluated the predictors of spatial portfolio effects. We detected synchrony within genera associated with synchrony in degree heating days, diurnal temperature range (DTR), and macroalgal cover at different timescales. Synchrony in DTR and macroalgal cover was associated with lower synchrony of Pocillopora and Porites populations, respectively. Population (for three of four genera) and environmental synchrony were stronger within than among habitats across timescales, underscoring the role of habitat-specific conditions in driving spatial synchrony and spatial portfolios. These results describe how the spatial and temporal scales of heterogeneity in environmental and ecological conditions determine synchrony in coral population dynamics and support a spatial portfolio effect, which may buffer coral metapopulations from island-scale collapse. Data in support of analyses for: Spatial portfolios in coral metapopulations are shaped by spatiotemporal asynchrony in environmental conditions. Published in Ecology Letters 2026.
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Spatial predictions of the morpho-ecological state of Finnish palsa mires
<p>Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system.</p> <p>These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024). Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: <em>"The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling"</em> (Leppiniemi et al., 2024, in-review).</p> <p> </p> <p>References:</p> <p>Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review).</p> <p>R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024).</p> <p>Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. <em>Suo</em> 73, 1–32. (In Finnish with English summary).</p> <p>Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish).</p> <p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X</p> <p> </p>
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15.
Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15. Spatial beta-diversity may increase landscape productivity if there are positive spatial selection effects. Alternatively, dominant species in mixtures might not be the most productive species in monoculture leading to negative or neutral spatial selection effects. However, these hypotheses remain untested experimentally. Seedling survival can determine species establishment, influencing productivity later. To address this knowledge gap, we experimentally tested whether transplanted seedlings of dominant species optimally sort among habitat types (grassland dominated by Andropogon gerardii, savanna by Quercus macrocarpa, deciduous forest by Acer rubrum, coniferous forest by Pinus strobus, bog by Larix laricina), creating positive effects of landscape diversity on seedling survival and net biodiversity effects at Cedar Creek Ecosystem Science Reserve (CCESR) in Minnesota, USA. The study is named BetaDIV and consists of 100 plots (20 plots per habitat × 5 habitats). Each of the five habitats includes two true replicate monocultures for each of the five species and two true replicates for each of the five possible mixture compositions of four species (leaving each one out in turn to eventually explore the effect of species identity). Each plot is 1.5 by 1.5 m, with 12 seedlings planted 0.5 m apart in a 4 × 4 square grid, except in the plot corners. In the early June 2022, we tagged and planted all seedlings (i.e., bareroot seedlings for trees and plugs for the grass A. gerardii). Two weeks after the initial transplanting, we started tracking seedling survival (presented here) to investigate how seedlings responded to local habitat conditions. We conducted a seedling census for each of the 1200 tagged seedlings (12 seedlings per plot×100 plots), in early September 2022, which was two months at the end
Temporal heterogeneity increases with spatial heterogeneity in ecological communities
Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Indeed, spatial heterogeneity is commonly used in alternative state theory as an early indicator of regime shifts. To evaluate if spatial heterogeneity of communities is a predictor of temporal heterogeneity, we used mixed effects models to synthesize 68 community datasets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Overall, we found a significant positive relationship between spatial and temporal heterogeneity across all ecosystems. In addition, lifespan and successional stage were related to temporal heterogeneity. Therefore we found evidence that spatial heterogeneity is a potential tool to predict temporal heterogeneity in ecological communities. This data package consists of six files. First we used a (1) R script to derive community dynamic metrics from source files to calculate (2) spatial and temporal heterogeneity over time as well as other measures of the community. We used this derived dataset to run analyses (3) with a R script to study the relationship between spatial and temporal heterogeneity communities. These analyses resulted in three figures, (4) the overall relationship between spatial and temporal heterogeneity, (5) output of mixed models investigating how experimental and biological factors affect this relationship, and (6) figures exploring how lifespan of the study organism affects the relationship between spatial and temporal datasets.
Identifying the drivers and responses of abrupt changes across spatial and temporal scales in ecology: a review
Recently, the theoretical basis for understanding abrupt changes in ecosystems relative to regime shifts has emerged (Ratajczak et al. 2018). Abrupt changes are defined as, “substantial changes in the mean or variability of a system that occur in a short period of time relative to typical rates of change” (Ratajczak et al. 2018). Despite a driver-response framework to guide the environmental conditions under which abrupt changes are likely to occur coupled with many examples of unexpected changes from long-term ecological research, our theoretical basis of understanding of abrupt changes doesn’t include long-term scales, variability in drivers and responses, changes in the magnitude or direction of drivers, or the interactions among multiple drivers across spatiotemporal scales (sensu Ratajczak et al. 2018). Further, a critical review of the literature is lacking and essential to further understanding how common abrupt changes are detected and reported, as well as patterns and scales of drivers and responses of abrupt change in ecosystems. To address this knowledge gap, we searched the existing ecological literature for evidence and commonalities of abrupt change across ecosystems to identify commonalities and differences of abrupt change drivers and responses across terrestrial, freshwater, and marine ecosystems. We specifically asked the following questions: (1) How common are abrupt changes reported in the ecological literature? (2) How do driver and response temporal and spatial scales of abrupt changes compare and vary across terrestrial, freshwater, and marine ecosystem types? (3) Is there relative congruence between the temporal and spatial scale of drivers and responses? (3) What are common types of drivers and responses to abrupt changes, and how do they vary across ecosystem types? (4) What terms are most associated with drivers and responses of abrupt changes among ecosystem types?
Data and code from: Spatial ecology of the Turks & Caicos boa, Chilabothrus c. chrysogaster Cope, 1871 (Serpentes: Boidae)
<p><span>Obtaining ecological and natural history data from cryptic squamates can be challenging, but is crucial to understanding species' biology, particularly in the context of conservation. In the Greater Antilles, this challenge is especially apparent, particularly among the West Indian boas (genus <em>Chilabothrus</em>). Most species have had only minimal natural history study, with a few exceptions. The Turks & Caicos boa (<em>C. chrysogaster</em>) has been studied intensively for over 16 years on the small privately owned island of Big Ambergris Cay, Turks and Caicos Islands. We conducted a multi-year radio-tracking study on the species to generate information relevant to spatial habitat use and movement that will inform conservation decision-making in the face of increasing development pressure. We tracked a total of 19 female snakes using surgically implanted transmitters, enabling us to obtain between 16 and 40 location observations per boa over the lifetime of each transmitter. We estimated home ranges, the core space used by an animal, using range distributions, finding that females have a home range of 0.70 ha to 1.2 ha. We also estimated occurrence distributions, the use of space between specific time intervals, finding an average occurrence area of 1.62 ha. Several females overlapped in their spatial habitat use, and we observed female boas using two novel habitats for the species (iron shore wrack and red mangrove). This study provides valuable information on the spatial ecology of an endangered boa and will serve to inform conservation work that is currently underway. </span></p>
Data from: Developing spatially explicit and stochastic measures of ecological departure
<p>Background: Ecological departure is a metric applied to mapped ecological systems measuring dissimilarity between the distributions of observed and expected proportions of non-stochastic reference vegetation classes within an area.</p> <p>Aims: We created spatially explicit measures of ecological departure incorporating stochasticity for each ecological system and all ecological systems from a central Nevada USA landscape.</p> <p>Methods: Spatially explicit ecological departures were estimated from a radius from each pixel governed by a distance-decay function within a moving window. Variability was introduced by simulating replicate climate time series for each spatial reference condition and calculating departure per replicate.</p> <p>Key results: Single system spatial ecological departure was highly and extensively departed, except for one area of low-elevation groundwater-dependent systems. Variance of spatial ecological departure was extensively low, except in areas of lower ecological departure, despite vegetation differences among replicates. The multiple-system ecological departure exhibited lower ecological departure.</p> <p>Conclusions: Spatial ecological departure was warranted for efficient land management as results were concordant between non-spatial and spatial metrics; however, rapid coding languages will be required.</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Fig. 4 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 4. Distribution of resources (light bars) and distribution of resources used by P. major (grey bars).
Fig. 5 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 5. Distribution of pseudo absence cells: a — the distance to the presence cells is not less than 1000 meters; b — the distance to the presence cells is not less than 500 meters; c — the distance to the presence cells is not less than 250 meters; d — distance to the presence cells is not less than 100 meters.
Fig. 3 in Javan mongoose (Herpestes javanicus) abundance and spatial ecology in a degraded dry dipterocarp forest
Fig. 3. Map of Sakaerat Biosphere Reserve with radio tracked (December 2019 to January 2021) Javan mongoose (Herpestes javanicus) home ranges and prey grids (PG). 95% utilisation contours (U.C) for male (M6, M1) and female (F1) mongooses are labelled in the legend. 50% U.C are solid line circles within each individual's home range. Prey grids collected ground-dwelling invertebrate mass as well as rodent biomass within the DDF (October to December 2020). Stars indicate where only ground-dwelling invertebrates were collected. Triangles indicate areas where sweep netting for invertebrates occurred in addition to sampling for rodent biomass and ground-dwelling invertebrates.
Fig. 1 in Javan mongoose (Herpestes javanicus) abundance and spatial ecology in a degraded dry dipterocarp forest
Fig. 1. Map and location of Sakaerat Biosphere Reserve with camera trap stations used to estimate Javan mongoose (Herpestes javanicus) abundance in 2017. Prey grid stations were used to calculate yearly averaged rodent biomass from January 2017 to November 2017.
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Figure 5 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 5. Correlation between the environment variables and the axes selected as a result of FANTER analysis. A – marginality axes 1 and 2; B - specialization axes 44 and 45. Type_1 - type_6 - the proportion of physiognomic types of vegetation cover; temp_05 - top soil temperature (3-5 cm) May 3, 2012; temp_06 - temperature of the top layer of soil (3-5 cm) June 20, 2012; Tm - thermoclimate; Kn - continentality; Om - ombroclimate; Kr - cryoclimate; Hd - humidity; Tr - salt regime; Nt - nitrogen nutrition; Rc - acidity; Lc - lighting; St - stepants; Pr - pratants; Humus – humus comtant; EC – soil electrical conductivity, imp_05 - imp_50 - soil mechanical impedance at a depth of 5, ..., 50 cm, Agr_10 - Agr_025 - aggregate fractions of size> 10, ..., <0.25 mm, g_Vlag - hygroscopic humidity,%; Compact – soil shrinkage, in %.
Figure 8 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 8. Spatial distribution of the habitat preference index (HSI) for Vallonia pulchella within the experimental site on red-brown clays based on ENFA (top) and MADIFA (bottom) procedures. The arrow indicates the zones of greatest difference.
Figure 4 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 4. An histogram of the available resource units. Resource allocation (black bars) and an histogram of the used resource units distribution of resource use (gray bars) of Vallonia pulchella. Type_1 - type_6 - the proportion of physiognomic types of vegetation cover; temp_05 - top soil temperature (3-5 cm) May 3, 2012; temp_06 - temperature of the top layer of soil (3-5 cm) June 20, 2012; Tm - thermoclimate; Kn - continentality; Om - ombroclimate; Kr - cryoclimate; Hd - humidity; Tr - salt regime; Nt - nitrogen nutrition; Rc - acidity; Lc - lighting; St - stepants; Pr - pratants; Humus – humus comtant; EC – soil electrical conductivity, imp_05 - imp_50 - soil mechanical impedance at a depth of 5, ..., 50 cm, Agr_10 - Agr_025 - aggregate fractions of size> 10, ..., <0.25 mm, g_Vlag - hygroscopic humidity,%; Compact – soil shrinkage, in %.
Figure 2 in Analysis of the spatial organization of Vallonia pulchella (Muller, 1774) ecological niche in Technosols (Nikopol manganese ore basin, Ukraine)
Figure 2. Soil surface and physiognomic characteristics of the vegetation cover. 1 – type_1 (Bromus sguarrosus L.); 2 – type_2 (Seseli tortuosum L.); 3 – type_3 (Lactuca tatarica (L.) C.A. Mey.); 4 – type_4 (Medicago sativa L.); 5 – type_5 (dead plant residue); 6 – type_6 (open soil cover).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.