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290 results for “variance”
Seasonal Soil Sampling of Grass-dominated, Mesquite-dominated, and Ecotone Sites at the Jornada Basin LTER site for the Analysis of Microbial Community Variance, 2022-2023
Fungal and bacterial soil communities were analyzed to assess the influence of woody shrub encroachment on soil microbial communities. Three study sites in the Jornada Long Term Ecological Research Site were selected to represent a grass-dominated site, a woody shrub dominated site, and an ecotone of woody shrubs and grass. The field sampling began in October 2022 and concluded in July 2023 with five sampling periods that aimed to capture seasonal variation: October 2022, January 2023, March 2023, May 2023, and July 2023. This dataset includes data pertaining to the soil microbial composition, environmental characteristics, microbial sequence processing, and documentation of the code utilized for data processing and statistical analyses. Data on soil microbial composition was collected from Phospholipid Fatty-Acid composition data from soil samples. Data on environmental characteristics were collected from on-site temperature probes, laboratory assessments of soil properties, and Jornada meteorological stations. Information pertaining to microbial sequence processing is included in the documented code as well as in the record of the primers utilized.
SEV-LTER Mean - Variance Experiment Plains Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And How does micro-environmental variation among plots influence how treatments alter soil moisture profiles over three soil depths? This data package includes sensor data from the Mean x Variance experiment in the Plains grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM, which is dominated by the grass species Bouteloua gracilis (blue grama).
SEV-LTER Mean x Variance Experiment Desert Grassland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert grassland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Desert Shrubland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert Shrubland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Juniper Savanna Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Juniper Savanna ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean - Variance Experiment Quadrat Plant Species Cover and Height
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at all Mean - Variance experiment sites. Quadrat locations span five important ecosystems of the American southwest: blue grama-dominated Plains grassland (est. fall 2019), black grama-dominated Chihuahuan Desert grassland (est. fall 2020), creosotebush dominated Chihuahuan Desert shrubland (est. fall 2021), juniper savanna (est. fall 2022) and pinon-juniper woodland (est. fall 2023). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This data package includes plant cover and height only -- for species biomass estimates per quad see package knb-lte
SEV-LTER Mean - Variance Experiment Seasonal Biomass Data at the Sevilleta National Wildlife Refuge, New Mexico
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This data package includes species-level plant cover and biomass data from the Mean - Variance Experiment at five sites comprising the major ecosystems of the Sevilleta National Wildlife Refuge: Chihuahuan Desert shrubland, Chihuahuan Desert grassland, Great Plains grassland, Juniper savanna, and pinon-juniper woodland. Species cover and volume in one-meter-squared quadrats are assessed twice-yearly in spring and fall, and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Mean - Variance Experiment sample archives
We designed novel field experimental infrastructure to resolve the relative importance and interactions among changes in precipitation mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance in a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. We collected samples of soils, biological soil crusts, leaves of the foundation plant species, and roots of the two dominant grass species each year during peak productivity (September-October). These samples enable us to address the question: How do interactions between the mean and variance of precipitation alter the biogeochemistry and microbiomes of plants and soils. This data package includes accession numbers for all samples collected from the Mean x Variance Experiment at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean x Variance Experiment Pinon Juniper Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Pinon Juniper ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder
<p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Zenodo, V1, doi: 10.5281</p> <p>Title: Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder</p> <p>Author: Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p>Date: 25 March 2022 </p> <p>Overview: This dataset contains an improvement and extension of significant data analysis products related to: </p> <p>Heavens, N.G., A. Pankine, J.M. Battalio, C. Wright, D.M. Kass, A. Kleinböhl, S. Piqueux, J.T. Schofield, 2022, Mars Climate Sounder Observations of Gravity-Wave Activity throughout Mars' Lower Atmosphere, Plan. Sci. J., 3, 57, doi: 10.3847/PSJ/ac51ce. </p> <p>These fall into three broad categories: diagnoses of detrended brightness temperature variance (GW) at 595–615 cm-1 (A1), 615–645 cm-1 (A2), and 635-665 cm-1 (A3) in individual views in the nadir or off-nadir by Mars Climate Sounder on board Mars Reconnaissance Orbiter; averages and other statistics of those diagnoses in space and time; and estimated gravity wave visibility functions for nadir, off-nadir, and nadir views with baselines like off-nadir views. This document presumes the manuscript is available to the dataset user.</p> <p>The purpose of archiving this dataset is to allow for comparison with a forthcoming analysis of gravity wave activity in limb observations by Mars Climate Sounder.</p> <p>The original dataset was published as:</p> <p>Heavens, Nicholas (2022), “Brightness Temperature Variances from On-Planet Views in the A1–A3 Channels by the Mars Climate Sounder”, Mendeley Data, V2, doi: 10.17632/5k6nybdy92.2</p> <p>The extension of the dataset consists of extension of the analysis time period to the end of January 2022 (MY 36, Ls=166.87).</p> <p>The improvement consists of a flag to indicate when an on-planet observations is likely to intersect a loop structure observed in the limb, and thus be contaminated by a high altitude cloud, which results in overestimate of gravity wave activity in the tropics at night during the clear season. Averages are now included that filter out flagged observations, as well as the original averages that include the flagged observations. </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete accounts of the contents and a restatement of this description is included as <em>MCS_OP_A13_GW_Analysis_Dataset_Documentation.pdf.</em></p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p>
Dataset: Intercomparison of flux, gradient, and variance-based optical turbulence ($C_n^2$) parameterizations
<p>This repository contains the dataset for the manuscript</p> <p>Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." <em>Applied Optics</em>, 2024. <a href="https://doi.org/10.1364/AO.519942">https://doi.org/10.1364/AO.519942</a></p> <p>The data is organized in the following structure:</p> <ul> <li>`met_cn2_*_10m.nc`: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site<br> using the flux-based and gradient-based methods at the 10 m level.</li> <li>`wrf_cn2_*.nc`: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m)<br> and flux, gradient, and variance-based methods (10m).</li> <li>`wrf_meteo_*.nc`: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data<br> serves as input for `wrf_cn2_*.nc` files.</li> </ul>
Forest carbon removal factor variance by climate domain
<p>Uncertainty (variance) in removal factor (annual sequestration rate) for forest carbon in new and existing forests by climate domain (tropical, subtropical, temperate, boreal). Uncertainty analysis is from Harris et al. 2021 Nature Climate Change. Units are aboveground carbon Mg^2/ha^2/year^2. New and existing forest are distinguished by the presence or absence of Hansen et al. 2013 tree cover gain pixels. </p> <p>Note: Uncertainty for existing temperate forest removal factors is so high because the IPCC national greenhouse gas inventory guidelines have a very high uncertainty for these forests (2019 refinement of guidelines). </p> <p>Note: Uncertainty analysis is for published version of the model (v1.2.0).</p> <p>https://github.com/wri/carbon-budget</p>
Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition
<p>Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition </p> <p>The mean number of <em>H. armigera</em> live larvae were transformed into square-root values before the statistical analysis. The one-way analysis of variance (ANOVA) was used for both transformed values under laboratory conditions. Means were compared using Fisher’s least significant differences (LSD) test at P< 0.05. Under field conditions, a two-way repeated measures analysis of variance (ANOVA) was used to determine the effects of insecticides and exposure time. The computations were carried out using GenStat (19th Edition, VSN International, UK). </p>
Supplementary Files for RIDE Review of Juxta Web Service, LERA, and Variance Viewer
<p>Test datasets and result files for review on web based collation tools <a href="http://www.juxtacommons.org/"><em>Juxta Web Service</em></a>, <a href="https://lera.uzi.uni-halle.de/"><em>LERA</em></a>, and <a href="http://variance-viewer.informatik.uni-wuerzburg.de/Variance-Viewer/"><em>Variance Viewer</em></a> (see <a href="https://ride.i-d-e.de">RIDE</a> issue on Tools and Environments for Digital Scholarly Editing).</p> <p>The first dataset (lorem-*) are constructed dummy texts in TEI, based on filler texts. They cover some basic cases of textual variance.</p> <p>The second dataset (hamlet-*) is based on a TEI encoded version of Shakespeare's <em>Hamlet</em>, taken from <a href="http://quartos.org">The Shakespeare Quartos Archive</a> (CC BY-NC 2.0), with a simplified baseline encoding.</p> <p>The result files (result-*) were produced with the web based collation tools mentioned above. The LERA results are available in PDF, while the other two tools provided TEI-XML. There is an extra configuration file for Variance Viewer.</p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
<p><span>The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons (<em>Falco peregrinus tundrius</em>) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data was collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average-sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand. </span></p>
Environmental effects on genetic variance are likely to constrain adaptation in novel environments
<p>Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. Using a paternal half-sibling breeding design, we generated and then reciprocally planted c.19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.</p>
Data from: Variance sum rule for entropy production
<p>Entropy production is the hallmark of nonequilibrium physics, quantifying irreversibility, dissipation, and the efficiency of energy transduction processes. Despite many efforts, its measurement at the nanoscale remains challenging. We introduce a variance sum rule for displacement and force variances that permits us to measure the entropy production rate in nonequilibrium steady states. We first illustrate it for directly measurable forces, such as an active Brownian particle in an optical trap. Data for this analysis can be found in the repository (1) described below. We then apply the variance sum rule to flickering experiments in human red blood cells (repositories (2-4)). We find that the entropy production rate is spatially heterogeneous with a finite correlation length (in particular, data in the repository (4)) and its average value agrees with calorimetry measurements. </p> <p>The dataset is composed of 4 repositories:</p> <p>1) SwitchingTrap.zip, containing data from Optical-tweezer experiments and used in Fig. 2 and 3 in the main paper, all data are three-column files featuring time (s), position (nm), and force (pN);</p> <p>2) OpticalStretching.zip, containing data from Optical-tweezer experiments shown in Fig. 4a in the main paper, all data are two-column files featuring time (s) and position (nm) traces;</p> <p>3) OpticalSensing.zip, containing data from Optical-tweezer experiments shown in Fig. 4b in the main paper, all data are one-column files featuring position (m) traces, sampling frequency 25kHz;</p> <p>4) OpticalMicroscopy.rar, containing data from Optical-microscopy experiments shown in Fig. 4c in the main paper, all data are one column files featuring position (nm) traces, sampling frequency 2kHz.</p>
Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study
<p>This dataset refers to the article "Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study" Front. Physiol. 12:639722 2021</p> <p>DOI: 10.3389/fphys.2021.639722</p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
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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.