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373 results for “stochastic”
Figure data for stochastic kinetics calculations of water autoionization in nanoscopic pools
<p>CSV files of data arrays used to construct figures shown in a submitted manuscript. V2 of the data set adds one new CSV. No other changes have been made.</p>
Map-based stochastic simulation data of a transient Ekman boundary layer
<p><strong>Overview</strong><br> <br> A journal paper in Advances in Science and Research [1] details the numerical modeling approach used to create the data. Here, the model input files, the raw data, processed data, and plot scripts are provided that support the research.</p> <p>The code used here [2,3] is an extended version of the one-dimensional turbulence (ODT) model [4,5]. The current model implementation utilizes an adaptive grid that further increases numerical efficiency [6.7]. A truncated version of the adaptive ODT code of this work is described in [8] and publicly available free of charge in [9].</p> <p>The theoretical foundation and numerical as well as experimental evidence for this work is given in [10,11,12,13], and the main motivation in [14].</p> <p>The bash script makePlot.sh is the top-level driver and contains all additional information about the cases. Some other Details are provided by low-level README files. Python-3.8 is required to run the scripts.</p> <p><strong>References</strong></p> <p>[1] M. Klein, and H. Schmidt. Capturing features of turbulent Ekman–Stokes boundary layers<br> with a stochastic modeling approach. <em>Adv. Sci. Res,</em> <strong>20</strong>, 55–64, https://doi.org/10.5194/asr-20-55-2023, 2023.</p> <p>[2] M. Klein, and H. Schmidt. Exploring stratification effects in stable Ekman boundary layers using a stochastic one-dimensional turbulence model, <em>Adv. Sci. Res.</em>, <strong>19</strong>, 117–136, https://doi.org/10.5194/asr-19-117-2022, 2022.</p> <p>[3] M. Klein, and H. Schmidt. A stochastic modeling strategy for intermittently unstable Ekman—Stokes boundary layers, <em>Proc. Appl. Math. Mech.</em>, <strong>20</strong>, e202000127, https://doi.org/10.1002/pamm.202000127, 2020.</p> <p>[4] A. R. Kerstein. One-dimensional turbulence: Model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, <em>J. Fluid Mech.</em>, <strong>392</strong>, 277–334, https://doi.org/10.1017/S0022112099005376, 1999.</p> <p>[5] A. R. Kerstein, and S. Wunsch. _Simulation of a stably stratified atmospheric boundary layer using one-dimensional turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>118</strong>, 325–356, https://doi.org/10.1007/s10546-005-9004-x, 2006.</p> <p>[6] D. O. Lignell, A. R. Kerstein, G. Sun, and E. T. Monson. Mesh adaption for efficient multiscale implementation of one-dimensional turbulence, <em>Theor. Comput. Fluid Dyn.</em>, <strong>27</strong>, 273–295, https://doi.org/10.1007/s00162-012-0267-9, 2013.</p> <p>[7] D. O. Lignell V. B. Lansinger, J. Medina, M. Klein A. R. Kerstein, H. Schmmidt, M. Fistler, and M. Oevermann. One-dimensional turbulence modeling for cylindrical and spherical flows: model formulation and application, <em>Theor. Comput. Fluid Dyn.</em>, <strong>32</strong>, 495–520, https://doi.org/10.1007/s00162-018-0465-1, 2018.</p> <p>[8] V. B. Stephens, and D. O. Lignell. One-dimensional turbulence (ODT): Computationally efficient modeling and simulation of turbulent flows, <em>Software X</em>, <strong>13</strong>, 100641, https://doi.org/10.1016/j.softx.2020.100641, 2021.</p> <p>[9] BYU Ignite. Adaptive ODT source code, https://github.com/BYUignite/ODT.</p> <p>[10] S. Salon, and V. Armenio. A numerical investigation of the turbulent Stokes–Ekman bottom boundary layer, <em>J. Fluid Mech.</em>, <strong>684</strong>, 316–352, https://doi.org/10.1017/jfm.2011.303, 2011.</p> <p>[11] M. Klein, T. Seelig, M. V. Kurgansky, A. Ghasemi V., I. D. Borcia, A. Will, E. Schaller, C. Egbers, and U. Harlander. Inertial wave excitation and focusing in a liquid bounded by a frustum and a cylinder, <em>J. Fluid Mech.</em>, <strong>751</strong>, 255–297, https://doi.org/10.1017/jfm.2014.304, 2014.</p> <p>[12] A. Ghasemi, M. Klein, A. Will, and U. Harlander. Mean flow generation by an intermittently unstable boundary layer over a sloping wall, <em>J. Fluid Mech.</em>, <strong>853</strong>, 111–149, https://doi.org/10.1017/jfm.2018.552, 2018.</p> <p>[13] M. Vincze, N. Fenyvesi, M. Klein, J. Sommeria, S. Viboud, and Y. Ashkenazy. Evidence for wind-induced Ekman layer resonance based on rotating tank experiments, <em>EPL</em>, <strong>125</strong>, 44001, https://doi.org/10.1209/0295-5075/125/44001, 2019.</p> <p>[14] L. S. Freire. Large-eddy simulation of the atmospheric boundary layer with near-wall resolved turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>184</strong>, 25–43, https://doi.org/https://doi.org/10.1007/s10546-022-00702-z, 2022.</p> <p> </p>
Noise Spectra and Stochastic Background Sensitivity Curve for the NG15-year Dataset
<p>This repository contains noise spectra for individual pulsars and stochastic gravitational wave background sensitivity curves for the NANOGrav 15-year data set analysis, highlighted in the paper <em>"The NANOGrav 15-Year Data Set: Detector Characterization and Noise Budget"</em> (DOI: <a href="https://iopscience.iop.org/article/10.3847/2041-8213/acda88">10.3847/2041-8213/acda88</a>). As in the paper, these spectra include the noise recovered from a common uncorrelated process analysis across the entire PTA. In other words, the spectra include the white noise, the power from the common process and any significant additional red noise in an individual pulsar.</p>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>
The Continuous Stochastic Gradient Method
<p>Abstract of the underlying manuscript (<a href="https://doi.org/10.48550/arXiv.2303.12477">https://doi.org/10.48550/arXiv.2303.12477</a>):</p><p>In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from previous iterations. Instead, design dependent integration weights are calculated to form a linear combination as an approximation to the true gradient at the current design. As the approximation error vanishes in the course of the iterations, CSG represents a hybrid approach, starting off like a purely stochastic method and behaving like a full gradient scheme in the limit. In this work, the efficiency of CSG is demonstrated for practically relevant applications from topology optimization. These settings are characterized by both, a large number of optimization variables <i>and</i> an objective function, whose evaluation requires the numerical computation of multiple integrals concatenated in a nonlinear fashion. Such problems could not be solved by any existing optimization method before. Lastly, with regards to convergence rates, first estimates are provided and confirmed with the help of numerical experiments.</p>
Data from: An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain
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Data from: Developing spatially explicit and stochastic measures of ecological departure
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Data from: Homogeneous selection and stochasticity overrule heterogeneous selection across biotic taxa and ecosystems
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Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
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The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics
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Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
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Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
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Data and code from: Quantitative analyses of stochastic influences on the response to phenotypic selection in a small passerine, the collared flycatcher
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Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Python codes for deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs
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Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging
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Data from: Dynamics of mixed-ploidy populations under demographic and environmental stochasticities
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Data from: Order among chaos: high throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system
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Environmental stochasticity increases extinction risk to a greater degree in pollination specialists than in generalists
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Inferring core processes using stochastic models of the geodynamo
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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.