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617 results for “Climate models”
Temporal variability is key to modelling the climatic niche
<p><strong>Aim</strong><i>:</i> Niche-based species distribution models (SDMs) have become a ubiquitous tool in ecology and biogeography. These models relate species occurrences with the environmental conditions found at these sites. Climatic variables are the most commonly used environmental data, and are usually included in SDMs as averages of a reference period (30-50 years). In this study we analyze the impact of including inter-annual climatic variability on the estimation of species niches and predicted distributions when assessing plant demographic response to extreme climatic episodes.</p> <p><strong>Location</strong><i>:</i> Mediterranean basin, SE Iberian Peninsula.</p> <p><strong>Methods</strong><i>:</i> We first characterized species niches with inter-annual and average climate in the same environmental space. We then compare the respective capacities of climatic suitability obtained from averaged climate-based and from inter-annual variability-based niches to explain population demographic responses to extreme drought. Furthermore, we assessed the relative increase in niche size when including climatic variability for a set of Mediterranean species exhibiting a wide range of distribution areas.</p> <p><strong>Results</strong><i>:</i> We found that climatic suitability obtained from inter-annual variability-based niches showed higher explanatory capacity than average climate-based suitability, especially for populations living in climatically marginal conditions, although both niches quantifications significantly explained species demographic responses. In addition, species with restricted distribution ranges increased relatively more their niche space when considering climatic variability, probably because in widely distributed species spatial variability compensates for temporal variability.</p> <p><strong>Main Conclusions</strong><i>:</i> The common use of climatic averages when characterizing species niches could lead to underestimations of species distribution and misunderstanding of demographic behavior, with implications for conservation plans derived from SDMs, e.g. overestimations of species extinction risk under climate change, or underestimations of alien species invasion' risk. We highlight that including climatic variability in niche modelling can be particularly important when dealing with species with restricted distribution and populations at the margin of their species niche.</p>
Data from: Integrated modeling predicts shifts in waterbird population dynamics under climate change
Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in-depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N-mixture models for large scale population data. We then analyzed four decades (1972-2013) of Mallard (Anas platyrhynchos) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in Mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.
Data from: On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out?
Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum Entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: (1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or (2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs. instream models. First, equivalence tests assessed average pairwise differences (site-specific comparisons throughout each species' range) among climate and instream models. Second, 'congruence' tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's <i>D</i> and Warren's <i>I</i> niche overlap statistics quantified range-wide similarity in predicted habitat suitability values from climate vs. instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3×10<sup>-4</sup>). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). <i>D</i> and <i>I</i> statistics reflected a high margin of overlap among climate and instream models (median <i>D</i> = 0.78, median <i>I</i> = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species' distributions at the scale of the Columbia Basin.
Data from: Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar
1. Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models have led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. 2. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996-2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest aboveground carbon density. 3. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg.ha-1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R 2 ≤ 26% and RMSE ≥ 63 Mg.ha −1 ). 4. Combining our model with the climatic projections for Madagascar from seven IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7-24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. 5. Synthesis: Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated to anthropogenic deforestation.
Community science validates climate suitability projections from ecological niche modeling
<p><span>Climate change poses an intensifying threat to many bird species, and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated <i>in situ </i>and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. </span><span>Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. </span><span>Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, </span>our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.</p>
Data from: Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change.
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
Data from: Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation in Busseola fusca (Lepidoptera: Noctuidae)
Current population genetic models fail to cope with genetic differentiation for species with large, contiguous and heterogeneous distribution. We show that in such a case, genetic differentiation can be predicted at equilibrium by circuit theory, where conductance corresponds to abundance in species distribution models (SDM). Circuit-SDM approach was used for the phylogeographic study of the lepidopteran cereal stemborer Busseola fusca Füller (Noctuidae) across sub-Saharan Africa. Species abundance was surveyed across its distribution range. SDM models were optimized and selected by cross validation. Relationship between observed matrices of genetic differentiation between individuals, and matrices of resistance distance was assessed through Mantel tests and redundancy discriminant analyses (RDA). A total of 628 individuals from 130 localities in 17 countries were genotyped at 7 microsatellite loci. Six population clusters were found based on a Bayesian analysis. The eastern margin of Dahomey Gap between East and West Africa was the main factor of genetic differentiation. The SDM projections at present, last interglacial and last glacial maximum periods were used for estimation of circuit resistance between locations of genotyped individuals. For all periods of time, when using either all individuals or only East-African individuals, partial Mantel r and RDA analyses conditioning on geographic distance were found significant. Under future projections (year 2080), partial r and RDA significance were different. From this study, it is concluded that analytical solutions provided by circuit theory are useful for the evolutionary management of populations and for phylogeographic analysis when coalescence times are not accessible by approximate Bayesian simulations.
Data from: Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection
Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common.
Data from: Controlled comparison of species- and community-level models across novel climates and communities
Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.
Data from: Modeling the effects of anthropogenic exploitation and climate change on an endemic stag beetle, Lucanus miwai, of Taiwan
Loss of biodiversity is a worldwide phenomenon and conservation of endemic species is becoming a pressing issue. However, species differ in life history characteristics, causing the best strategy for conservation to vary among species. Lucanus miwai is an endemic stag beetle of Taiwan, but the natural history and the conservation status of L. miwai have not been fully studied. Lucanus miwai adults live in forest-edge grassland habitats and are experiencing threats from anthropogenic exploitation. Additionally, climate change may threaten its long-term survival because of its specific habitat preference. Results from this study clearly indicate the need for more studies on the natural history of L. miwai and demonstrate that contemporary intensity of anthropogenic exploitation can have a negative effect on population size of this species. Future habitat loss because of global climate change, in addition, can be another major factor leading to population decline. Moreover, increasing demands on L. miwai because of population decline may lead to population extinction. In addition to protecting adult males from being collected, conservation strategies should also focus on discovering habitats utilized by individuals from different life stages, investigating the connectivity between populations, and maintaining the long-term availability of suitable habitats for L. miwai.
Data from: Modeling effects of climate change and phase shifts on detrital production of a kelp bed
The exchange of energy and nutrients between ecosystems (i.e., resource subsidies) plays a central role in ecological dynamics over a range of spatial and temporal scales. Little attention has been paid to the role of anthropogenic impacts on natural systems in altering the magnitude, timing, and quality of resource subsidies. Kelp ecosystems are highly productive on a local scale and export over 80% of kelp primary production as detritus, subsidizing consumers across broad spatial scales. Here, we generate a model of detrital production from a kelp bed in Nova Scotia to hindcast trends in detrital production based on temperature and wave height recorded in the study region from 1976 to 2009, and to project changes in detrital production that may result from future climate change. Historical and projected increases in temperature and wave height led to higher rates of detrital production through increased blade breakage and kelp dislodgment from the substratum, but this reduced kelp biomass and led to a decline in detrital production in the long term. We also used the model to demonstrate that the phase shift from a highly productive kelp bed to a low-productivity barrens, driven by the grazing activity of sea urchins, reduces kelp detrital production by several orders of magnitude, an effect that would be exacerbated by projected increases in temperature and wave action. These results indicate that climate-mediated changes in ecological dynamics operating on local scales may alter the magnitude of resource subsidies to adjacent ecosystems, affecting ecological dynamics on regional scales.
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.
Data from: Characterizing neutral and adaptive genomic differentiation in a changing climate: the most northerly freshwater fish as a model
Arctic freshwater ecosystems have been profoundly affected by climate change. Given that the Arctic charr (Salvelinus alpinus) is often the only fish species inhabiting these ecosystems, it represents a valuable model for studying the impacts of climate change on species life history diversity and adaptability. Using a genotyping-by-sequencing approach, we identified 5976 neutral single nucleotide polymorphisms (SNPs) and found evidence for reduced gene flow between allopatric morphs from two high Arctic lakes, Linne´vatn (Anadromous, Normal, and Dwarf) and Ellasjøen (Littoral and Pelagic). Within each lake, the degree of genetic differentiation ranged from low (Pelagic vs. Littoral) to moderate (Anadromous and Normal vs. Dwarf). We identified 17 highly diagnostic, putative adaptive SNPs that differentiated the allopatric morphs. Although we found no evidence for adaptive differences between morphs within Ellasjøen, we found evidence for moderate (Anadromous vs. Normal) to high genetic differentiation (Anadromous and Normal vs. Dwarf) among morphs within Linne´vatn based on two adaptive loci. As these freshwater ecosystems become more productive, the frequency of sympatric morphs in Ellasjøen will likely shift based on foraging opportunities, whereas the propensity to migrate may decrease in Linne´vatn, increasing the frequency of the Normal morph. The Dwarf charr was the most genetically distinct group. Identifying the biological basis for small body size should elucidate the potential for increased growth and subsequent interbreeding with sympatric morphs. Overall, neutral and adaptive genomic differentiation between allopatric and some sympatric morphs suggests that the response of Arctic charr to climate change will be variable across freshwater ecosystems.
Sampling data accompanying "An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model"
<p>Datasets recording sampling results, accompanying the manuscript <em>An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: Development and offline assessment for use in an aerosol-climate model, </em>Rothenberg, D. and Wang, C., submitted, GMD. Please see the included README for more details.</p>
Data for "Millennial modulation of Atlantic Multidecadal Variability in a climate model"
<p>Processed data and Jupyter notebooks for manuscript "Millennial modulation of Atlantic Multidecadal Variability in a climate model" by Joakim Kjellsson and Wonsun Park</p>
Dataset: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models
<p>This dataset corresponds to the following publication:<br>Steinert, N., J., et al. 2023: <i>Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models</i>, Environmental Research Letters, 10.1088/1748-9326/ad10d7<br>https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. This dataset contains permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air-ground coupling from an ensemble of 32 Earth System Models.</p><p>This dataset includes two file archives:<br>1. 32 CMIP6 models, 10 permafrost definitions, historical period (1850-2014), annual data<br>2. 32 CMIP6 models, 10 permafrost definitions, SSP5-85 period (2015-2100), annual data</p><p>This dataset represents source data for the following publication. Please refer to this reference for a more detailed description of the definitions used in this dataset:<br>Steinert, N., J., et al. 2023: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models, https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>The results show that variations between permafrost-presence definitions result in substantial differences of up to 18 million km2, where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground-air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground-air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. This dataset therefore supports an emphasis on the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.</p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks
<p>The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data. </p> <p>In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.</p> <p> </p>
Conceptual climate models datasets used for DIRESA
<p>Lorenz '63 and MAOOAM datasets used for for DIRESA</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.