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492 results for “microbial communities”
Data from: Plant, insect, and soil microbial communities vary across brome invasion gradients in northern mixed-grass prairies
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Local vs. site-level effects of algae on coral microbial communities
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Data from: Enhanced climate tolerance for trees derived from microbial communities
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Effects of parental care on skin microbial community composition in poison frogs
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Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest
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Data from: Responses of subsoil organic carbon to climate warming and cooling is determined by microbial community rather than its molecular composition
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Plant management but not fertilization mediates soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
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Nectar peroxide: Assessing variation among plant species, microbial tolerance and effects on microbial community assembly
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Microplastics effects on marine microbial communities and their functioning
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Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community
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Soil Microbial and N cycling data along a wetland plant community gradient
This dataset contains soil chemical and microbial data taken from surface soil cores (15cm) from the Black Spruce, Willow/Birch, Tussock, Emergent Fen, and Rich Fen plant communities along the APEX boardwalk. Data include DOC, soil C, potential nitrification, potential denitrification, soil DNA concentration, water content, ammonium concentration, nitrate concentration, archaea abundance, NirK functional gene abundance, ammonia oxidizing bacteria (AOB) abundance, ammonia oxidizing archaea (AOA) abundance, NosZ functional gene abundance, and eubacteria abundance.
Plant-mediated root methane emissions and oxidation in a thermokarst bog complex in the Bonanza Creek LTER Experimental Forest V - Raw Microbial Community Analysis Data 2015
Vascular plants are important in the wetland methane cycle but their effect on production, oxidation, and transport has high uncertainty, limiting our ability to predict emissions. Vegetation operated on top of baseline methane emissions, which varied with proximity to the thawing permafrost margin. Emissions from vegetated plots increased over the season, resulting in cumulative seasonal methane emissions that were 4.1-5.2 g m-2 season-1 greater than unvegetated plots. Mass balance calculations signify these greater emissions were due to increased methane production (3.0-3.5 g m-2 season-1) and decreased methane oxidation (1.1-1.6 g m-2 season-1). Minimal oxidation occurred along the plant-transport pathway and oxidation was suppressed outside the plant pathway. Our data indicate suppression of methane oxidation was stimulated by root exudates fueling competition among microbes for electron acceptors. Root exudates are known to fuel methane production and our work provides evidence they also decrease methane oxidation. This dataset contains 2015 results from monthly DNA analyses taken on cores from natural conditions in a bog complex in the Bonanza Creek LTER.
Operational taxonomic unit (OTU) table characterizing water track and adjacent soil microbial communities in Taylor Valley, Antarctica during the 2012-13 austral summer
This data package includes the abundance of microbial operational taxonomic units (OTUs) for samples collected during the austral summer of 2012-2013 in the Lake Hoare and Goldman Glacier Basins of Taylor Valley, Antarctica. A total of twenty samples from on- and off-water track soils were collected and analyzed. Samples were collected from the Lake Hoare Basin on 27 December 2012 and from the Goldman Glacier Basin on 4 January 2013. The aim of the study was to identify how variation in the measured physical and chemical environment of water tracks within the two water track systems influenced soil microbial community structure and diversity. Soil bacterial biodiversity was assessed using cultivation independent 16S rRNA gene sequencing.
Investigating Host Feeding Strategy as a Determinant of Insect Gut Microbial Community Profile at the Sevilleta National Wildlife Refuge, New Mexico
Diverse microbial communities live in the gut regions of animals. The precise ecological and evolutionary circumstances that govern relationships between hosts and their gut communities is unclear. In this study, we hypothesize that host feeding strategy shapes the microbial communities within the gut systems of insects. We collected five insect species from the Sevilleta National Wildlife Refuge that exhibited herbivorous, detritovorous and carnivorous diets. Using gut samples from the insects we measured if and how microbial communities are shaped based on any effect host feeding strategy might have. Preliminary analysis of bacterial communities using 16S rDNA sequences has thus far revealed that the sampled community profiles initially appear to show signs of being determined by host feeding type. Analysis has also shown that sequences from the phyla Firmicutes and Proteobacteria appear to contribute most significantly to the differences between communities of different feeding types. We expect that upon further data recovery, the extent of the effect host feeding type has on the communities will be clarified. Additionally we intend to incorporate bacterial community data from previous studies to further broaden our sample set. We expect our results to further define the ecological circumstances that shape the microbial populations within living systems.
Soil and litter chemistry, soil microbial communities and litter decomposition from tropical forest and oil palm
<b>Description: </b><p>A study examining the interactions between soil chemistry, litter chemistry and soil microbial decomposers as controls on rates of litter decomposition across a tropical land use disturbance gradient. Co-located soil and litter samples were collected from old growth forest, moderately logged forest, heavily logged forest and oil palm plantations. Soil and litter were chemically characterised and soil bacterial and fungal community composition and abundance were measured. These were then combined in fully factorial ex-situ microcosms and measured litter decomposition rates at 3 time points during different stages of decomposition.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/124"><b>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NE/K016377/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li><li>Sabah Biodiversity Centre (Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3929632">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_Dataset.xlsx</p><p><b>SAFE_Dataset.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>Soil_Properties</b> (described in worksheet Soil_Properties)</p><p>Description: Basic measured soil properties</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>gravimetric moisture content</b>: Soil moisture content at the time of sample collection (Field type: numeric)</li><li><b>soil_pH</b>: Soil pH measured on fresh soils (Field type: numeric)</li><li><b>soil_N</b>: Total soil Nitrogen (Field type: numeric)</li><li><b>soil_C</b>: Total soil Carbon (Field type: numeric)</li><li><b>soil_C.N</b>: Soil carbon to nitrogen ratio (Field type: numeric)</li><li><b>soil_P</b>: soil inorganic phosphorus (Field type: numeric)</li></ul></li><li><p><b>Litter_Chemistry</b> (described in worksheet Litter_Chemistry)</p><p>Description: Litter chemistry data of mixed forest floor litter, collected, sorted to remove humified material, woody debris and dried</p><p>Number of fields: 20</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Pretreatment</b>: Whether the litter sample was sterilised by autoclaving or not (Field type: categorical)</li><li><b>leaf_K</b>: leaf potassium concentration (Field type: numeric)</li><li><b>leaf_Ca</b>: leaf Calcium concentration (Field type: numeric)</li><li><b>leaf_Mg</b>: leaf Magnesium concentration (Field type: numeric)</li><li><b>leaf_Al</b>: leaf aluminium concentration (Field type: numeric)</li><li><b>leaf_P</b>: leaf phosphorus concentrations (Field type: numeric)</li><li><b>solubles</b>: leaf soluble cell content (Field type: numeric)</li><li><b>hem_pro_cel_lig_rec</b>: leaf hemicellulose, proteins, cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>hem_pro</b>: leaf hemicellulose and proteins (Field type: numeric)</li><li><b>cel_lig_rec</b>: leaf cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>cel</b>: leaf cellulose (Field type: numeric)</li><li><b>lig_rec</b>: leaf lignin and recalcitrants (Field type: numeric)</li><li><b>leaf_N</b>: leaf nitrogen concentration (Field type: numeric)</li><li><b>leaf_C</b>: leaf carbon concentration (Field type: numeric)</li><li><b>c.n</b>: leaf carbon to nitrogen ration (Field type: numeric)</li><li><b>d13c</b>: leaf carbon stable isotope ratio (Field type: numeric)</li><li><b>d15n</b>: leaf nitrogen stable isotope ratio (Field type: numeric)</li></ul></li><li><p><b>PLFA_Concentrations</b> (described in worksheet PLFA_Concentrations)</p><p>Description: Phospolipid Fatty Acid (PLFA) concentrations as biomarkers of soil bacteria and fungi. Extracted from freeze dried soils prior to the microcosm experiment</p><p>Number of fields: 10</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Total_PLFA</b>: Total PLFA concentrations extracted from soil samples (Field type: numeric)</li><li><b>Fungal_PLFA</b>: Fungal PLFA biomarker concentrations extracted from soils (Field type: numeric)</li><li><b>Bacteria_PLFA</b>: Bacteria PLFA biomarkers extracted from soils (Field type: numeric)</li><li><b>Fungal:Bacteria</b>: Ratio of fungal to bacteria PLFAs (Field type: numeric)</li><li><b>Gram_Pos_PLFA</b>: Gram Positive PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>Gram_Neg_PLFA</b>: Gram Negative PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>GramPos:GramNeg</b>: Gram positive to Gram negative PLFA ratios (Field type: numeric)</li></ul></li><li><p><b>Soil_Microbial_Communities</b> (described in worksheet Soil_Microbial_Communities)</p><p>Description: Summary diversity statistics from bacterial 16S and fungal ITS biomarker microbial sequencing. DNA extracted from soils prior to microcosm experiment</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Bacteria_Richness</b>: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Bacteria_Shannon</b>: Bacterial Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Fungal_Richness</b>: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Fungal_Shannon</b>: Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Richness</b>: Number of observed saprotrophic fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Shannon</b>: Saprotrophic Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li></ul></li><li><p><b>Ex_Situ_Litter_Decomposition</b> (described in worksheet Ex_Situ_Litter_Decomposition)</p><p>Description: Fully factorial litter decomposition experiment. 16 unique soil and litter combinations (4x4) were incubated in petri dishes at constant temperature and moisture and mass loss measured after 31, 105 and 398 days.</p><p>Number of fields: 8</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Soil_ID</b>: Soil ID indicating which land use soil was collected from (Field type: categorical)</li><li><b>Litter_Location</b>: Location of which GEM carbon plot the litter was collected from. Litter was collected from the 5 carbon subplots as per soil collection and homogenised into one composite sample per carbon plot (Field type: location)</li><li><b>Litter_ID</b>: Litter ID indicating which land use litter was collected from (Field type: categorical)</li><li><b>Experimental_Block</b>: Which experimental block the microcosm was assigned to. N= 5 (Field type: replicate)</li><li><b>Timepoint</b>: At what timepoint the litter was harvested from each microcosm (Field type: categorical)</li><li><b>Mass_Loss</b>: The mass loss of litter relative to the starting mass of 1g (Field type: numeric)</li><li><b>home_away</b>: Descriptor for whether the soil and litter combination in microcosm (Field type: categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-01 to 2018-09-01</p><p><b>Latitudinal extent: </b>4.6402 to 4.9539</p><p><b>Longitudinal extent: </b>117.4518 to 117.7942</p>
Data from: Sierra Nevada mountain lake microbial communities are structured by temperature, resources, and geographic location
<p><span>Warming, eutrophication (nutrient fertilization) and brownification (increased loading of allochthonous organic matter) are three global trends impacting lake ecosystems. However, the independent and synergistic effects of resource addition and warming on autotrophic and heterotrophic microorganisms are largely unknown. In this study, we investigate the independent and interactive effects of temperature, dissolved organic carbon (DOC, both allochthonous and autochthonous), and nitrogen (N) supply, in addition to the effect of spatial variables, on the composition, richness, and evenness of prokaryotic and eukaryotic microbial communities in lakes across elevation and N deposition gradients in the Sierra Nevada mountains of California, USA. We found that both prokaryotic and eukaryotic communities are structured by temperature, terrestrial (allochthonous) DOC and latitude. Prokaryotic communities are also influenced by total and aquatic (autochthonous) DOC, while eukaryotic communities are also structured by nitrate. Additionally, increasing N availability was associated with reduced richness of prokaryotic communities, and both lower richness and evenness of eukaryotes. We did not detect any synergistic or antagonistic effects as there were no interactions among temperature and resource variables. Together, our results suggest that (a) organic and inorganic resources, temperature, and geographic location (based on latitude and longitude) independently influence lake microbial communities; and (b) increasing N supply due to atmospheric N deposition may reduce richness of both prokaryotic and eukaryotic microbes, likely by reducing niche dimensionality. Our study provides insight into abiotic processes structuring microbial communities across environmental gradients and their potential roles in material and energy fluxes within and between ecosystems.</span></p>
Nitrogen enrichment stimulates wetland plant responses whereas salt amendments alter sediment microbial communities and biogeochemical responses
<p>Freshwater wetlands of the temperate north are exposed to a range of pollutants that may alter their function, including nitrogen (N)-rich agricultural and urban runoff, seawater intrusion, and road salt contamination, though it is largely unknown how these drivers of change interact with the vegetation to affect wetland carbon (C) fluxes and microbial communities. We implemented a full factorial mesocosm (378.5 L tanks) experiment investigating C-related responses to three common wetland plants of eastern North America (<i>Phragmites australis</i>, <i>Spartina pectinata</i>, <i>Typha latifolia</i>), and four water quality treatments (fresh water control, N, road salt, sea salt). During the 2017 growing season, we quantified carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes, above- and below-ground biomass, root porosity, light penetration, pore water chemistry (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>-</sup>², Cl<sup>-</sup>, DOC), soil C mineralization, as well as sediment microbial communities via 16S rRNA gene sequencing. Relative to freshwater controls, N enrichment stimulated plant biomass, which in turn increased CO<sub>2</sub> uptake and reduced light penetration, especially in <i>Spartina</i> stands. Root porosity was not affected by water quality, but was positively correlated with CH<sub>4 </sub>emissions,<sub> </sub>suggesting that plants can be important conduits for CH<sub>4</sub> from anoxic sediment to the atmosphere. Sediment microbial composition was largely unaffected by N addition, whereas salt amendments induced structural shifts, reduced sediment community diversity, and reduced C mineralization rates, presumably due to osmotic stress. Methane emissions were suppressed by sea salt, but not road salt, providing evidence for the additional chemical control (SO<sub>4</sub><sup>-2</sup> availability) on this microbial-mediated process. Thus, N may have stimulated plant activity while salting treatments preferentially enriched specific microbial populations. Together our findings underpin the utility of combining plant and microbial responses, and highlight the need for more integrative studies to predict the consequences of a changing environment on freshwater wetlands. </p>
Data from: Diverse communities of bacteria and archaea flourished in Palaeoarchaean (3.5-3.3 Ga) microbial mats
<p><span><span><span><span><span><span><span><span><span><span><span>Limited taxonomic classification is possible for Archaean microbial mats and this is a fundamental limitation in constraining early ecosystems. Applying Fourier Transform Infrared spectroscopy (FTIR), a powerful tool for identifying vibrational motions attributable to specific functional groups, we characterised fossilised biopolymers in 3.5-3.3 Ga microbial mats from the Barberton greenstone belt (South Africa). Microbial mats from four Palaeoarchaean horizons exhibit significant differences in taxonomically informative aliphatic contents, despite high aromaticity. This reflects precursor biological heterogeneity since all horizons show equally exceptional preservation and underwent similar grades of metamorphism. Low methylene to end-methyl (CH<sub>2</sub>/CH<sub>3</sub>) absorbance ratios in mats from the 3.472 Ga Middle Marker horizon signify short, highly branched <i>n</i>-alkanes interpreted as isoprenoid chains forming archaeal membranes. Mats from the 3.45 Ga Hooggenoeg Chert H5c, 3.334 Ga Footbridge Chert, and 3.33 Ga Josefsdal Chert exhibit higher CH<sub>2</sub>/CH<sub>3</sub>ratios suggesting longer, unbranched fatty acids from bacterial lipid precursors. Absorbance ratios of end-methyl to methylene (CH<sub>3</sub>/CH<sub>2</sub>) in Hooggenoeg, Josefsdal and Footbridge mats yield a range of values (0.20-0.80) suggesting mixed bacterial and archaeal architect communities based on comparison with modern examples. Higher (0.78-1.25) CH<sub>3</sub>/CH<sub>2</sub> ratios in the Middle Marker mats identify Archaea. This exceptional preservation reflects early, rapid silicification preventing the alteration of biogeochemical signals inherited from biomass. Since silicification commenced during the lifetime of the microbial mat, FTIR signals estimate the affinities of the architect community and may be used in the reconstruction of Archaean ecosystems. Together, these results show that Bacteria and Archaea flourished together in Earth's earliest ecosystems.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Spatial patterning of soil microbial communities created by fungus-farming termites
<p><span><span><span><span><span><span><span><span><span><span><span>Spatially overdispersed mounds of fungus-farming termites (Macrotermitinae) are hotspots of nutrient availability and primary productivity in tropical savannas, creating spatial heterogeneity in communities and ecosystem functions. These termites influence the local availability of nutrients in part by redistributing nutrients across the landscape, but the links between termite ecosystem engineering and the soil microbes that are the metabolic agents of nutrient cycling are little understood. We used DNA metabarcoding of soils from <i>Odontotermes montanus</i> mounds to examine the influence of termites on soil microbial communities in a semi-arid Kenyan savanna. We found that bacterial and fungal communities were compositionally distinct in termite-mound topsoils relative to the surrounding savanna, and that bacterial communities were more diverse on mounds. The higher microbial alpha and beta diversity associated with mounds created striking spatial patterning in microbial community composition, and boosted landscape-scale microbial richness and diversity. Selected enzyme assays revealed consistent differences in potential enzymatic activity, suggesting links between termite-induced heterogeneity in microbial community composition and the spatial distribution of ecosystem functions. We conducted a large-scale field experiment in which we attempted to simulate termites' effects on microbes by fertilizing mound-sized patches; this altered both bacterial and fungal communities, but in a different way than natural mounds. Elevated levels of inorganic nitrogen, phosphorus, and potassium may help to explain the distinctive fungal communities in termite-mound soils, but cannot account for the distinctive bacterial communities associated with mounds.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Convergent shifts in host-associated microbial communities across environmentally elicited phenotypes
Morphological plasticity is a genotype-by-environment interaction that enables organisms to increase fitness across varying environments. Symbioses with diverse microbiota may aid in acclimating to this variation, but whether the associated bacteria community is phenotype-specific remains unstudied. Here we induce morphological plasticity in three species of sea urchins and measure changes in the associated bacterial community. While each host species had unique microbial communities, the expression of morphological plasticity resulted in the convergence for a phenotype-specific microbiome that was, in part, driven by differentially associating with α- and γ-proteobacteria. Furthermore, these results suggest that phenotype-specific signatures were the product of the environment, and are correlated with ingestive and digestive structures. By manipulating diet quantity over time, we also support that differentially associating with microbiota along a phenotypic continuum is bidirectional. Taken together, our data support the idea of a phenotype-specific microbial community and that phenotypic plasticity extends beyond a genotype-by-environment interaction.
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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)
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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.
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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.
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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.