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28 results for “Stochastic simulation”
Test Collection Reliability: A Study of Bias and Robustness to Statistical Assumptions via Stochastic Simulation
<p>This archive contains the simulated collections, their diagnosis data, and the estimates of accuracy. For the full code and description, please refer to https://github.com/julian-urbano/irj2015-reliability</p>
Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging
<p>In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species' range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of <em>Trichocentrum</em> <em>undulatum</em>, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (𝜆 and 𝜆<sub>s</sub>) among sites.</p>
Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour
<p>An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model.</p> <p>References</p> <p>[1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></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>
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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Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging
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Simulation for "Asymptotic Behavior for a Time-Inhomogeneous Stochastic Differential Equation Driven by an α-Stable Lévy Process"
<p>The code and the video are related to the article G.Mihai, and E. Luirard. « Asymptotic Behavior for a Time-Inhomogeneous Stochastic Differential Equation Driven by an α-Stable Lévy Process », available on http://arxiv.org/abs/2112.07287.</p>
Simulation scripts and data for the stochastic modelling of evolutionary rescue in resistance to pesticides
<p>Evolutionary rescue occurs when the genetic evolution of adaptation saves a population from extinction after environmental change. The evolution of resistance to pesticides is a special scenario of abrupt environmental change, where rescue occurs under strong selection for one or a few <em>de novo</em> resistance mutations of large effect. Here, we develop continuous-time approximations that accurately predict classic discrete-time dynamics in population genetics and population ecology in an integrated eco-evolutionary model of adaptive rescue through pesticide resistance. We derive analytical approximations for the key distributions and statistics that characterise the results, including the probability density function for the time to resistance and the probability of population extinction. The time to resistance shows a lag period, a narrow peak and a long tail, which implies that it can be difficult to predict when resistance will arise. The probability of population extinction shows a sharp transition, in that when extinction is possible, it is also highly likely, which can make eradication a theoretically achievable goal. Alongside these results contributing to the theory of evolutionary rescue, the methods have produced powerful approximations that lay the foundations of a flexible modelling framework for the applied study of eco-evolutionary dynamics to improve scientific resistance management.</p>
Ungulate herbivores promote beta diversity and drive stochastic plant community assembly by selective defoliation and trampling: From a four-year simulation experiment
<p>Ungulate herbivores shape grassland plant communities at multiple scales, ultimately affecting ecosystem function. However, ungulates have complex effects on grasslands, including defoliation, trampling, excreta return, and their interactions. Moreover, the effects of ungulate density on grasslands are regulated by these three mechanisms. Nevertheless, how these three mechanisms affect biodiversity at multiple scales and community assembly remains poorly understood.</p> <p>Here, we conducted a 4-year novel field experiment to disentangle the effects of defoliation, trampling, and excreta return by ungulates on plant community assembly in a temperate grassland in Inner Mongolia, China. This experiment set two different scenarios: moderate ungulate density (Moderate, characterised by selective defoliation and moderate trampling) and high ungulate density (Intense, characterised by non-selective defoliation and heavy trampling), including different combinations of defoliation, trampling, and excreta return in each scenario.</p> <p>We found that defoliation and trampling increased stochasticity in community assembly and promoted alpha and beta diversity under both scenarios. Specifically, defoliation promoted the coexistence of species with multiple resource acquisition strategies (higher functional trait diversity) by reducing interspecific competition; trampling tended to facilitate random species colonisation. Conversely, excreta return favoured grasses, promoting deterministic assembly and impacting species coexistence. Notably, selective defoliation in the moderate scenario led to a dominance of stochastic processes during community assembly, whereas non-selective defoliation still did not change the dominance of deterministic processes. Further, communities subject to selective defoliation were insensitive to changes in soil properties caused by trampling and excreta return, maintaining a high-level beta diversity and the stochastic of community assembly.</p> <p><em>Synthesis:</em></p> <p>Our study provides important insights into the mechanisms by which ungulate herbivores influence plant community assembly, suggesting that defoliation and trampling have the potential to drive stochastic processes, while excreta return plays the opposite role. Our study also suggests that selective foraging by ungulates acts as stronger stochastic forces during community assembly compared to non-selective defoliation. These results imply that considering ungulate feeding preferences and foraging behaviour in grassland management will help prevent biodiversity loss and biotic homogenisation.</p>
Bayesian Analysis of Paleotsunami Sources: Data and Stochastic Simulations
<p>This repository contains the datasets utilized in the research titled “Tracing the Sources of Paleotsunamis Using Bayesian Frameworks.” Each dataset is integral to the analysis and reconstruction efforts undertaken in the study.</p> <p> </p> <p><strong>File Descriptions</strong></p> <p> </p> <p><strong>1. CorrectedShoreline_jogan_deposit_data.csv</strong></p> <p> </p> <p>This file contains paleotsunami data collected by Sugawara et al. The data has been corrected to account for the shoreline position at the time of the paleotsunami event.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Sugawara, D., Goto, K., Imamura, F., Matsumoto, H., & Minoura, K. (2012). Assessing the magnitude of the 869 Jogan tsunami using sedimentary deposits: Prediction and consequence of the 2011 Tohoku-oki tsunami. <em>Sedimentary Geology, 282</em>, 14–26.</p> <p> </p> <p><strong>2. Stochastic_samples.zip</strong></p> <p> </p> <p>This archive contains the stochastic samples generated for the Japan Trench, utilizing the coupling distribution model from Loveless et al.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Loveless, J. P., & Meade, B. J. (2011). Spatial correlation of interseismic coupling and coseismic rupture extent of the 2011 Mw = 9.0 Tohoku-oki earthquake. <em>Geophysical Research Letters, 38</em>.</p> <p> </p> <p><strong>3. Selected_Stochastic_Samples.zip</strong></p> <p> </p> <p>This file includes a reduced sample space derived from the original stochastic samples, specifically selected for statistical analysis.</p> <p> </p> <p><strong>4. Sendai_1961.zip</strong></p> <p> </p> <p>This dataset contains the reconstructed morphology of the Sendai plain as it appeared in 1961. The reconstruction is based on aerial photographs provided by the Geospatial Information Authority of Japan (GSI).</p>
Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy
<p>This data deposit contains all the datasets needed to draw Figures 8 and 9 in the paper "Stochastic Simulation of the Suspended Sediment Deposition in the Channel with Vegetation and Its Relevance to Turbulent Kinetic Energy", which is now under review for potential publication in Water Resources Research. </p>
Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study
<p>Data and R codes necessary to replicate the analyses presented in the paper entitled "Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study", published in Peer Community in Ecology (<a href="https://doi.org/10.24072/pcjournal.263">10.24072/pcjournal.263</a>).</p>
Data for: Comparative evaluation of phenotypic, pedigree, and family-based selection in insect breeding using stochastic simulation
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Simulation scripts and data for the stochastic modelling of evolutionary rescue in resistance to pesticides
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Ungulate herbivores promote beta diversity and drive stochastic plant community assembly by selective defoliation and trampling: From a four-year simulation experiment
Open the record for dataset details and reuse information.
Simulation for "Asymptotic behavior of a time-inhomogeneous stochastic system in the harmonic potential "
<p>The code and the videos are related to the article T. Cavallazzi, and E. Luirard, « Asymptotic behavior of a time-inhomogeneous stochastic system in the harmonic potential ».</p> <p> </p>
Dataset for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme"
<p>Datasets for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme". The global simulation outputs (climatologies and daily anomalies), calculated RMM indexes, and the results of the budget analysis are included.</p>
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
Data from: Demographic stochasticity alters expected outcomes in experimental and simulated non-neutral communities
Theory has shown that the effects of demographic stochasticity on communities may depend on the magnitude of fitness differences between species. In particular, it has been suggested that demographic stochasticity has the potential to significantly alter competitive outcomes when fitness differences are small (nearly neutral), but that it has minimal effects when fitness differences are large (highly non-neutral). Here we test such theory experimentally and extend it to examine how demographic stochasticity affects exclusion frequency and mean densities of consumers in simple, but non-neutral, consumer-resource communities. We used experimental microcosms of protists and rotifers feeding on a bacterial resource to test how varying absolute population sizes (a driver of demographic stochasticity) affected the probability of competitive exclusion of the weakest competitor. To explore whether demographic stochasticity could explain our experimental results, and to generalize beyond our experiment, we paired the experiment with a continuous-time stochastic model of resource competition, which we simulated for 11 different fitness inequalities between competiting consumers. Consistent with theory, in both our experiments and our simulations we found that demographic stochasticity altered competitive outcomes in communities where fitness differences were small. However, we also found that demographic stochasticity alone could affect communities in other ways, even when fitness differences between competitors were large. Specifically, demographic stochasticity altered mean densities of both weak and strong competitors in experimental and simulated communities. These findings highlight how demographic stochasticity can change both competitive outcomes in non-neutral communities and the processes underlying overall community dynamics.
Data for "Stochastic Green's Function Method Considering Non-uniform Rise Time Distribution to Simulate 3D Broadband Ground-Motion"
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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.