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6 results for “individual stochasticity”
Quantum stochastic resonance of individual Fe atoms. Open data sets.
<p>Data sets for publication:</p> <p><strong>Quantum Stochastic Resonance of individual Fe atoms</strong><br> Max Hänze, Gregory McMurtrie, Susanne Baumann, Luigi Malavolti, Susan N. Coppersmith, Sebastian Loth</p>
Data from: Interacting effects of unobserved heterogeneity and individual stochasticity in the life-history of the Southern fulmar
1.Individuals are heterogeneous in many ways. Some of these differences are incorporated as individual states (e.g., age, size, breeding status) in population models. However, substantial amounts of heterogeneity may remain unaccounted for, due to unmeasurable genetic, maternal, or environmental factors. 2.Such unobserved heterogeneity (UH) affects the behavior of heterogeneous cohorts via intra-cohort selection and contributes to inter-individual variance in demographic outcomes such as longevity and lifetime reproduction. Variance is also produced by individual stochasticity, due to random events in the life cycle of wild organisms, yet no study thus far has attempted to decompose the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity for an animal population in the wild. 3.We developed a stage-classified matrix population model for the Southern fulmar breeding on Ile des Pétrels, Antarctica. We applied multi-event, multi-state markrecapture methods to estimate a finite mixture model accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes. Finally, we partitioned the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity. 4.We identify three UH groups, differing substantially in longevity, lifetime reproductive output, age at first reproduction, and in the proportion of the life spent in each reproductive state. 14% of individuals at fledging have a delayed but high probability of recruitment and extended reproductive lifespan. 67% of individuals are less likely to reach adulthood, recruit late and skip breeding often but have the highest adult survival rate. 19% of individuals recruit early and attempt to breed often. They are likely to raise their offspring successfully, but experience a relatively short lifespan. Unobserved heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first reproduction (3.7%) and lifetime reproduction (22%). 5.UH can affect the entire life cycle, including survival, development, and reproductive rates, with consequences over the lifetime of individuals and impacts on cohort dynamics. The respective role of unobserved heterogeneity versus individual stochasticity varies greatly among demographic outcomes. We discuss the implication of our finding for the gradient of life-history strategies observed among species and argue that individual differences should always be accounted for in demographic studies of wild populations.
Data from: Interacting effects of unobserved heterogeneity and individual stochasticity in the life-history of the Southern fulmar
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Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Data from: Individual variation, population-specific behaviours, and stochastic processes shape marine migration phenologies
1. The phenology of long distance migrations can influence individual fitness, moderate population dynamics, and regulate the availability of ecosystem services to other trophic levels. Phenology varies within and among populations, and can be influenced by conditions individuals experience both prior to departure and encounter en route. 2. Assessing how intrinsic and extrinsic factors (e.g. individual physical condition vs. environmental conditions) interact to influence variation in migratory phenologies across ecological scales is often limited due to logistical constraints associated with tracking large numbers of individuals from multiple populations simultaneously. 3. We used two natural tags, DNA and otolith microstructure analysis, to estimate the relative influence of individual traits (life history strategy, body size at departure, and growth during migration), population-specific strategies, and interannual variability on the phenology of marine migrations in juvenile sockeye salmon Oncorhynchus nerka. 4. We show that the timing and duration of juvenile sockeye salmon migrations were correlated with both life history strategy and body size, while migration duration was also correlated with departure timing and growth rates during migration. Even after accounting for the effect of individual traits, several populations exhibited distinct migration phenologies. Finally, we observed substantial interannual and residual variation, suggesting stochastic environmental conditions moderate the influence of carry-over effects that develop prior to departure, as well as population-specific strategies. 5. Migratory phenologies are shaped by complex interactions between drivers acting at multiple ecological and temporal scales. Given evidence that intraspecific diversity can stabilize ecological systems, conservation efforts should seek to maintain migratory variation among populations and preserve locally adapted phenotypes; however, variation within populations, which may buffer systems from environmental stochasticity, should also be regularly assessed and preserved.
Data from: Individual variation, population-specific behaviours, and stochastic processes shape marine migration phenologies
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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.