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373 results for “stochastic”
Data for cluster generation for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility. </p>
Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection
<p>Data set and supporting files for article "Brownian Motion or Mean Reversion? A Parameter Based Approach to Stochastic Process Selection"</p> <ul> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> <li>ADF Tests log commodities SA (Excel and PDF files) – EViews outputs (18) of Augmented Dickey-Fuller tests run on all 9 Seasonally Adjusted time series (S A) with trend and intercept, and with only intercept.</li> <li>Commodities data historical x Inflation (Excel file) – Time series used in the paper in 3 versions: <ul> <li>In historical monthly averages prices US$,</li> <li>In December 2018 US$ prices (with US inflation adjustment in appropriate tab),</li> <li>In December 2018 US$ prices Seasonally Adjusted (S A) from EViews output.</li> </ul> </li> <li>commodities log SA data Ev1 (Excel file) – for the log of 9 commodities time series in Dec 2018 values, after STL decomposition in EViews: original series, seasonally adjusted, seasonal factor and trend</li> <li>commodities log SA Regressions (Excel and PDF files) - EViews outputs of OLS regression on log of seasonally adjusted times series: Dx = α + β x.</li> <li>GBMVaratAVG, GBMVaratPaths, MRMVaratAVG, MRMVaratPaths (Matlab codes) – Matlab codes for Variance ratio tests for GBM and MRM, sample paths (200) and averages of 2,500 paths.</li> <li>Series Seasonal data decomposition (PDF file) – graphical display of seasonal decomposition of log of 9 price series into: original series, trend, seasonal factor, remainder, and seasonally adjusted.</li> <li>Variance Ratio Comm SA (Excel file) - EViews outputs of variance ratio tests run of seasonal adjusted log of 9 price series in Dec 2018 values. </li> </ul>
Data for "Stochastic Green's Function Method Considering Non-uniform Rise Time Distribution to Simulate 3D Broadband Ground-Motion"
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Distinct melanocyte subpopulations defined by stochastic expression of proliferation or maturation programs enable a rapid and sustainable pigmentation response
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Data from: Hypothesised diprotomeric enzyme complex supported by stochastic modelling of Palytoxin-induced Na/K pump channels
The sodium-potassium pump (Na+/K+ pump) is crucial for cell physiology. Despite great advances in the understanding of this ionic pumping system, its mechanism is not completely understood. We propose the use of the Statistical Model Checker to investigate palytoxin-induced Na+/K+ pump channels. We modelled a system of reactions representing transitions between the conformational substates of the channel with parameters, concentrations of the substates, and reaction rates extracted from simulations reported in the literature, based on electrophysiological recordings in a whole-cell configuration. The model was implemented using the UPPAAL-SMC platform. Comparing simulations and probabilistic queries from stochastic system semantics with experimental data, it was possible to propose additional reactions to reproduce the single channel dynamic. The probabilistic analyses and simulations suggest that the palytoxin-induced Na+/K+ pump channel functions as a diprotomeric complex in which protein-protein interactions increase the affinity of the Na+/K+ pump affinity for palytoxin.
Data from: Contrasting effects of spatial heterogeneity and environmental stochasticity on population dynamics of a perennial wildflower
Understanding how variation in growth, survival and reproduction affect population dynamics is a fundamental question in ecology. Although the effects of among-year variation (environmental stochasticity) are well understood, the effects of among-site variation (spatial heterogeneity) are less clearly defined. I evaluated the effects of spatial and temporal variation on the population dynamics of Pulsatilla patens, pasqueflower, a perennial prairie forb. I conducted a 10-year demographic monitoring study, and quantified vital rate variation among sites and years using generalized linear models. I incorporated vital rate functions using this variation into integral projection models for stochastic and spatially heterogeneous environments. I also explored the effects of temporal and spatial autocorrelation, by exploring model predictions over the range of possible values for temporal autocorrelation and local seed dispersal. Vital rates varied more among years than among sites. However, environmental stochasticity and spatial heterogeneity had similar magnitude effects on population dynamics. These effects were also qualitatively different: environmental stochasticity reduced population growth rates relative to the average, whereas spatial heterogeneity increased population growth rates. Spatial autocorrelation and negative temporal autocorrelation led to higher population growth rates, although environmental stochasticity still reduced growth rates for all autocorrelation values, and spatial heterogeneity increased growth rates for all autocorrelation values. Some form of autocorrelation would be necessary for model projections to match observed population trends. Synthesis. Spatial heterogeneity is as important as environmental stochasticity for population dynamics, but it is much less often incorporated into population projection models. This study points to a number of interesting avenues for future research into the roles of spatial heterogeneity and spatiotemporal variation for long-term population dynamics.
Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models
Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population-level responses to environmental variation. Expanding on current methods, we 1) apply data-driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changes in seed-bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire-return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.
Data from: Temporal genetic structure in a poecilogonous polychaete: the interplay of developmental mode and environmental stochasticity
Background: Temporal variation in the genetic structure of populations can be caused by multiple factors, including natural selection, stochastic environmental variation, migration, or genetic drift. In benthic marine species, the developmental mode of larvae may indicate a possibility for temporal genetic variation: species with dispersive planktonic larvae are expected to be more likely to show temporal genetic variation than species with benthic or brooded non-dispersive larvae, due to differences in larval mortality and dispersal ability. We examined temporal genetic structure in populations of Pygospio elegans, a poecilogonous polychaete with within-species variation in developmental mode. P. elegans produces either planktonic, benthic, or intermediate larvae, varying both among and within populations, providing a within-species test of the generality of a relationship between temporal genetic variation and larval developmental mode. Results: In contrast to our expectations, our microsatellite analyses of P. elegans revealed temporal genetic stability in the UK population with planktonic larvae, whereas there was variation indicative of drift in temporal samples of the populations from the Baltic Sea, which have predominantly benthic and intermediate larvae. We also detected temporal variation in relatedness within these populations. A large temporal shift in genetic structure was detected in a population from the Netherlands, having multiple developmental modes. This shift could have been caused by local extiction due to extreme environmental conditions and (re)colonization by planktonic larvae from neighboring populations. Conclusions: In our study of P. elegans, temporal genetic variation appears to be due to not only larval developmental mode, but also the stochastic environment of adults. Large temporal genetic shifts may be more likely in marine intertidal habitats (e.g. North Sea and Wadden Sea) which are more prone to environmental stochasticity than the sub-tidal Baltic habitats. Sub-tidal and/or brackish (less saline) habitats may support smaller P. elegans populations and these may be more susceptible to the effects of random genetic drift. Moreover, higher frequencies of asexual reproduction and the benthic larval developmental mode in these populations leads to higher relatedness and contributes to drift. Our results indicate that a general relationship between larval developmental mode and temporal genetic variation may not exist.
Data from: Phenotypic stochasticity prevents lytic bacteriophage population from extinction during bacterial stationary phase
It is generally thought that the adsorption rate of a bacteriophage correlates positively with fitness, but this view neglects that most phages rely only on exponentially growing bacteria for productive infections. Thus, phages must cope with the environmental stochasticity that is their hosts' physiological states. If lysogeny is one alternative, it is unclear how strictly lytic phages can survive the host stationary phase. Three scenarios may explain their maintenance: (1) pseudolysogeny, (2) diversified or (3) conservative bet-hedging. In order to better understand how a strictly lytic phage survives the stationary phase of its host, and how phage adsorption rate impacts this survival, we challenged two strictly lytic phage λ, differing in their adsorption rates, with stationary phase Escherichia coli cells. Our results showed that, pseudolysogeny was not responsible for phage survival and that, contrary to our expectation, high adsorption rate was not more detrimental during stationary phase than low adsorption rate. Interestingly, this last observation was due to the presence of the "residual fraction" (phages exhibiting extremely low adsorption rates), protecting phage populations from extinction. Whether this cryptic phenotypic variation is an adaptation (diversified bet-hedging) or merely reflecting unavoidable defects during protein synthesis remains an open question.
Scheduling Maintenance Activities subject to Stochastic Job-Dependent Machine Deterioration
<p>Test instances and source code</p>
Linear simulation data of "Nonlinear microtearing modes in MAST and their stochastic layer formation"
<p>This dataset contains the linear simulation data used in M. Giacomin <em>et al</em>. "Nonlinear microtearing modes in MAST and their stochastic layer formation", Plasma Phys. Control. Fusion 2023 (DOI 10.1088/1361-6587/aceb89). Please see the README file for information on the dataset. Please cite M. Giacomin <em>et al</em>. "Nonlinear microtearing modes in MAST and their stochastic layer formation", Plasma Phys. Control. Fusion 2023 (DOI 10.1088/1361-6587/aceb89) if you use data from this dataset.</p>
Stochastically Generated Clouds and Code For Examining Cloud Retrieval Errors
<p>This data set contains a static version of the AT3D radiative transfer software, which uses SHDOM, which is in .zip form. Python scripts (.py) to generate clouds and to perform 3D radiative transfer simulations and retrievals using 1D radiative transfer. Associated datasets such as LookUpTables or configuration files are in netcdf format ('.nc'). The stochastically generated clouds are stored in synthetic_clouds.tar.gz.</p>
Stochastic Resonance Applied to Amblyopia Training and the Plasticity of Brain
ClinicalTrials.gov study NCT04213066. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Investigating the Effect of Stochastic Resonance Vibration on Gait and Balance and Upper Extremity Function in Children With Cerebral Palsy
ClinicalTrials.gov study NCT04388787. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response time data
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Data from: Phenotypic stochasticity prevents lytic bacteriophage population from extinction during bacterial stationary phase
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Data from: Stochastic evolutionary demography under a fluctuating optimum phenotype
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Data from: A stochastic model for annual reproductive success
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Data from: Individual variation, population-specific behaviours, and stochastic processes shape marine migration phenologies
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Data from: Stochastic dynamics of three competing clones: conditions and times for invasion, coexistence and fixation
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.