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28 results for “Asymmetric response”
Ecological data for: Subsidy accessibility drives asymmetric food web responses
<p>Global change is fundamentally altering flows of natural and anthropogenic subsidies across space and time. After a pointed call for research on subsidies in the 1990s, an industry of empirical work has documented the ubiquitous role subsidies play in ecosystem structure, stability and function. Here, we argue that physical constraints (e.g., water temperature) and species traits can govern a species' accessibility to resource subsidies, which has been largely overlooked in the subsidy literature. We examined the input of a high quality, point-source anthropogenic subsidy (aquaculture feed) into a recipient freshwater lake food web. By using a combined bio-tracer approach, we detect a gradient in accessibility of the anthropogenic subsidy within the surrounding food web driven by the thermal preferences of three constituent species, effectively rewiring the recipient lake food web. Since aquaculture is predicted to increase significantly in coming decades to support growing human populations, and global change is altering temperature regimes, then this form of food web alteration may be expected to occur frequently. We argue that subsidy accessibility is a key characteristic of recipient food web interactions that must be considered when trying to understand the impacts of subsidies on ecosystem stability and function under continued global change.</p>
MOM6-COBALT2 model results for asymmetrical Ocean Carbon Responses to La Niña and El Niño in the Tropical Pacific Ocean
<p>1. MOM6 COBALT2 model results</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc</a></p> <p>(air-sea carbon flux decomposition)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_chl_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_chl_1980_2023.nc</a></p> <p> (sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_no3_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_no3_1980_2023.nc</a></p> <p>(sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1980-2023.nc</a></p> <p> (sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_po4_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_po4_1980_2023.nc</a></p> <p> (sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1980-2023.nc</a></p> <p> (sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface zonal velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_MLD_003_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_MLD_003_1980-2023.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface meridional velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized ocean pCO2 in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/hist_gcb_simA_ocean_static.nc/content" target="_blank" rel="noopener noreferrer">hist_gcb_simA_ocean_static.nc</a></p> <p>(grid of model)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br>pco2_hadv_hdif: horizontal transport term, H_circ;<br>dpco2_vadv_vdif: vertical transport term,V_circ;<br>dpco2_bio: biological term<br>dpco2_rain: surface freshwater term<br>dpco2_sst: thermal term<br>dpco2_dt: pco2 response term<br>dpco2_flux: CO2 flux response term</p> <p>2. dataset</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc</a></p> <p>(CO2 based MLS: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_area.nc</a></p> <div>(the grid of MLS)</div> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc</a></p> <p>(CO2 based SOM-FFN: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc</a></p> <p>(the grid of SOM-FFN)</p> <p><a href="../api/records/13325632/draft/files/precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JRA: detrended and deseasonalized precipitation)</p> <p><a href="../api/records/13325632/draft/files/phosphate_all_woa18_all_p_monthly.nc/content" target="_blank" rel="noopener noreferrer">phosphate_all_woa18_all_p_monthly.nc</a></p> <p>(WOA: sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc</a></p> <p>(AVISO: detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc</a></p> <p>(OISSS: sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc</a></p> <div>(OISSS: detrended and deseasonalized sea surface salinity)</div> <p><a href="../api/records/13325632/draft/files/co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc/content" target="_blank" rel="noopener noreferrer">co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc</a></p> <p>(SOCAT: ocean pCO2)</p> <p><a href="../api/records/13325632/draft/files/mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc/content" target="_blank" rel="noopener noreferrer">mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/nitrate_all_woa18_all_n_monthly.nc/content" target="_blank" rel="noopener noreferrer">nitrate_all_woa18_all_n_monthly.nc</a></p> <p>(WOA: sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc</a></p> <p> (OISST: sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc</a></p> <p>(OISST: detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc/content" target="_blank" rel="noopener noreferrer">chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc</a></p> <p>(GlobColour: sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JAR: detrended and deseasonalized zonal wind velocity)</p> <p><a href="../api/records/13325632/draft/files/TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc/content" target="_blank" rel="noopener noreferrer">TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc</a></p> <p>(TAO: sea surface temperature, sea surface salinity, and ocean pco2)</p> <p>3. Figure</p> <p>Figure1-4, FigureS1-S11 are data analysis files used python</p> <p> </p>
Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
<p>Climate change may alter phenology within populations with cascading consequences for community interactions and ongoing evolutionary processes. Here, we measured the response to climate change in two sympatric, recently diverged (~170 years) populations of <em>Rhagoletis</em> <em>pomonella</em> flies specialized on different host fruits (hawthorn and apple) and their parasitoid wasp communities. We tested whether warmer temperatures affect dormancy regulation and its consequences for synchrony across trophic levels and temporal isolation between divergent populations. Under warmer temperatures, both fly populations developed earlier. However, warming significantly increased the proportion of maladaptive pre-winter development in apple, but not hawthorn, flies. Parasitoid phenology was less affected, potentially generating ecological asynchrony. Observed shifts in fly phenology under warming may decrease temporal isolation, potentially limiting ongoing divergence. Our findings of complex sensitivity of life-history timing to changing temperatures predict that coming decades may see multifaceted ecological and evolutionary changes in temporal specialist communities.</p>
Ecological data for: Subsidy accessibility drives asymmetric food web responses
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Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
Open the record for dataset details and reuse information.
Asymmetric responses of resource use efficiency to previous-year precipitation in a semi-arid grassland
<p>1. Intensified inter-annual fluctuations in precipitation could profoundly impact terrestrial ecosystems. However, how changes previous-year precipitation influence current ecosystem functioning (e.g., resource use efficiency) in semi-arid regions remains unclear.</p> <p>2. In this study, water use efficiency (WUE) and light use efficiency (LUE) were investigated in a multi-year precipitation gradient experiment with seven treatment levels: 20%, 40% and 60% decreases and 20%, 40% and 60% increases in the amount of natural rainfall plus ambient precipitation. Plots receiving 60% less precipitation were representative of extreme dry years whereas the other treatment levels fell within the normal year-to-year range in precipitation change. Measurements made in both the post-treatment period (2013-2015) and the treatment period (2010-2012) provided an opportunity to quantify the legacy effects of precipitation on resource use efficiency (RUE).</p> <p>3. Sensitivities of LUE to previous-year precipitation were not changed among treatments in 2013. However, asymmetric responses of RUEs (i.e., WUE and LUE) to previous-year precipitation were found in 2014-2015. WUE<sub>2014</sub>, WUE<sub>2015</sub>, LUE<sub>2014</sub>, and LUE<sub>2015</sub> responded more strongly to previous normal decreased than increased precipitation. Importantly, they were more sensitive to previous extreme dry year (represented by 60% precipitation reduction) than normal wet year (represented by 60% precipitation increment). Aboveground net primary productivity (ANPP) rather than resource absorption (R<sub>uptake</sub>) drove these asymmetric responses of RUE, and biomass of grasses further explained the asymmetric responses of ANPP.</p> <p>4. This study reveals the non-linear responses of RUE to previous-year precipitation and highlighted that the legacy effects of precipitation on RUE can be ascribed to the changes in vegetation composition. Our findings can facilitate the prediction of the legacy effects of precipitation variation on grassland ecosystem functions in the future.</p>
Body size and trophic levels explain global asymmetric response of tetrapod diversity to climate effects
<p><span>Although c</span><span>limate-based hypotheses are widely used to explain large-scale diversity patterns, they fall short of explaining the spatial variation among taxonomic groups. Integrating food web and metabolic theories into macroecology is a promising step forward, as they allow including explicit taxon-specific traits that can potentially mediate the relationship between climate and diversity. Our investigation focuses on the role of body size and trophic structure in mediating the influence of contemporary climate and historical climate change on global tetrapods species richness. We used piecewise structural equation modeling to assess the direct effects of contemporary climate and climate instability of species richness and the indirect effects of climate on tetrapod richness mediated by community-wide species traits. We found that birds and mammals are less sensitive to the direct effect of contemporary climate than amphibians and squamates. Contemporary climate and climate instability favored the species richness in mammals and amphibians. However, for birds and squamates, this link is only associated with contemporary climate. Moreover, we demonstrated that community-wide traits favored the species richness gradients of tetrapod groups, except amphibians, but this link depends on traits and taxonomic groups. Specifically, bird communities with smaller bodies and bottom-heavy structures support higher species richness. Squamates also tend to be more diverse in communities with prevalence of smaller bodies, while mammals are correlated with top-heavy structures. Moreover, we show that higher contemporary climate and climate instability reduce the species richness of birds and mammals through community-wide traits and tend indirectly improve squamate species richness. Thus, we showed the significance of body size and trophic structure effect in driving a global asymmetric response of tetrapod diversity to climate effects highlights the limitations of applying "typical" climate-based hypotheses. Furthermore, by combining multiple theories, our research contributes to a more realistic and mechanistic understanding of diversity patterns across taxonomic groups. </span></p>
Data from: The effect of initial vortex asymmetric structure on tropical cyclone intensity change in response to an imposed environmental vertical wind shear
<p>Previous studies have investigated how the environmental vertical wind shear (VWS) may trigger the asymmetric structure in an initially axisymmetric tropical cyclone (TC) vortex and how TC intensity changes in response. In this study, the possible effect of the initial vortex asymmetric structure on the TC intensity change in response to an imposed environmental VWS is investigated based on idealized full-physics model simulations. Results show that the effect of the asymmetric structure in the initial TC vortex can either enhance or suppress the initial weakening of the TC in response to the imposed environmental VWS. When the initial asymmetric structure is in phase of the VWS-induced asymmetric structure, the TC weakening will be enhanced and vice versa. Our finding calls for realistic representation of initial TC asymmetric structure in numerical weather prediction models and observations to better resolve the asymmetric structure in TCs.</p>
Asymmetric responses of abundance and diversity of N-cycling genes to altered precipitation in arid grasslands
<p>Precipitation changes exert a fundamental effect on the nitrogen (N) cycle in water-limited grasslands. Soil microbes are essential drivers of N cycle, and the rates and their stabilities of interrelated N-cycling processes are reflected by the abundance and diversity of N-cycling genes. Yet, little is known about how altered precipitation affects the genes involved in the entire N-cycling pathways.</p> <p>By combining a 6-year precipitation manipulation experiment (-30%, ambient, +30%, +50%) with metagenomic sequencing, we investigated the responses of N-cycling gene abundance and diversity to altered precipitation at two soil depths (0-10 and 30-50 cm).</p> <p>We found that increased precipitation enhanced the abundance of numerous key genes, leading to an acceleration of N turnover, but decreased the diversity of ammonium assimilation genes. Decreased precipitation did not reduce abundance or diversity of N-cycling genes. Most N-cycling genes showed generally consistent responses to altered precipitation in the topsoil (0-10 cm) and subsoil (30-50 cm), albeit with clear distinctions in both abundance and diversity by soil depth. These precipitation-specific responses and depth-dependent variabilities of functional genes were attributed to the distinct taxonomic composition of each N-cycling gene. Further, we quantified gross N transformation rates and found that they were well predicted by the abundance of most N-cycling genes (e.g., genes involved in ammonium assimilation and nitrification).</p> <p>Our study sheds new light on the soil N cycle under precipitation alterations from the perspective of individual gene abundance and diversity, and shows that future increases in precipitation could accelerate soil N turnover in arid and semi-arid lands. </p>
Larval and adult traits coevolve in response to asymmetric coastal currents to shape marine dispersal kernels
<p>Dispersal emerges as an outcome of organismal traits and external forcings. However, it remains unclear how the emergent dispersal kernel evolves as a by-product of selection on the underlying traits. This question is particularly compelling in coastal marine systems where dispersal is tied to development and reproduction, and where directional currents bias larval dispersal downstream causing selection for retention. We modelled the dynamics of a metapopulation along a finite coastline using an integral projection model and adaptive dynamics to understand how asymmetric coastal currents influence the evolution of larval (pelagic larval duration) and adult (spawning frequency) life history traits, which indirectly shape the evolution of marine dispersal kernels. Selection induced by alongshore currents favors the release of larvae over multiple time periods, allowing long pelagic larval durations and long-distance dispersal to be maintained in marine life cycles in situations where they were previously predicted to be selected against. Two evolutionary stable strategies emerged: one with a long pelagic larval duration and many spawning events resulting in a dispersal kernel with a larger mean and variance, and another with a short pelagic larval duration and few spawning events resulting in a dispersal kernel with a smaller mean and variance. Our theory shows how coastal ocean flows are important agents of selection that can generate multiple, often co-occurring, evolutionary outcomes for marine life history traits that affect dispersal.</p>
Dataset for: Asymmetric response of aboveground and belowground temporal stability to nitrogen and phosphorus addition in a Tibetan alpine grassland
<p><span>Anthropogenic eutrophication is known to impair the stability of aboveground net primary productivity (ANPP), but its effects on the stability of belowground (BNPP) and total (TNPP) net primary productivity remain poorly understood. Based on a nitrogen and phosphorus addition experiment in a Tibetan alpine grassland, we show that nitrogen addition had little impact on the </span><span>temporal stability</span><span> of ANPP, BNPP and TNPP, whereas phosphorus addition reduced the </span><span>temporal stability</span><span> of BNPP and TNPP, </span><span>but not ANPP</span><span>. Significant interactive effects of nitrogen and phosphorus addition were observed on the stability of ANPP because of the opposite phosphorus effects under </span><span>ambient and enriched nitrogen conditions</span><span>. We found that the stability of TNPP was primarily driven by that of BNPP rather than that of ANPP. The responses of BNPP stability cannot be predicted by those of ANPP stability, as the variations in responses of ANPP and BNPP to enriched nutrients, with ANPP increased while BNPP remained unaffected, resulted in asymmetric responses in their stability. The dynamics of grasses, the most abundant plant functional group, instead of community species diversity, largely contributed to the ANPP stability. </span><span>Under the enriched nutrient condition, the synchronization of grasses reduced the grass stability, while the latter had a significant but weak negative impact on the BNPP stability. </span><span>These findings challenge the prevalent view that species diversity regulates the responses of ecosystem stability to nutrient enrichment. Our findings also suggest that the ecological consequences of nutrient enrichment on ecosystem stability cannot be accurately predicted from the responses of aboveground components, and highlight the need for a better understanding of the belowground ecosystem dynamics.</span></p>
Larval and adult traits coevolve in response to asymmetric coastal currents to shape marine dispersal kernels
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Asymmetric responses of resource use efficiency to previous-year precipitation in a semi-arid grassland
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Asymmetric responses of abundance and diversity of N-cycling genes to altered precipitation in arid grasslands
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Body size and trophic levels explain global asymmetric response of tetrapod diversity to climate effects
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Dataset for: Asymmetric response of aboveground and belowground temporal stability to nitrogen and phosphorus addition in a Tibetan alpine grassland
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Data from: Individual asymmetric competition responses across multidimensional niches may enable coexistence of closely related species
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Data from: Asymmetric micro-evolutionary responses in a warming world: Heat-driven adaptation enhances metal tolerance, but not vice versa
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Data from: Genomic divergence, demographic histories, and male territorial response reveal asymmetric reproductive barriers in allopatric eastern versus western Nashville warbler subspecies (<em>Leiothlypis ruficapilla</em>)
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Data from: Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky
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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.