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373 results for “stochastic”
Molecular dynamics of solids at constant pressure and stress using anisotropic stochastic cell rescaling - dataset
<p>Supporting data related to manuscript "Molecular dynamics at constant pressure and stress using anisotropic stochastic cell rescaling"</p>
A Stochastic Extension of Stateflow - Artifact
<p>Artifact for reproducing the results Vizualised in Figure 5 of the paper "A stochastic extension of Stateflow" for the conference the 13th ACM/SPEC International Conference on Performance Engineering (ICPE). </p>
The cost of noise: Stochastic punishment falls short of sustaining cooperation in social dilemma experiments
<p>Identifying mechanisms able to sustain costly cooperation among self-interested agents is a central problem across social and biological sciences. One possible solution is peer punishment: when agents have an opportunity to sanction defectors, classical behavioral experiments suggest that cooperation can take root. Overlooked from standard experimental designs, however, is the fact that real-world human punishment---the administration of justice---is intrinsically noisy. Here we show that stochastic punishment falls short of sustaining cooperation in the repeated public good game. As punishment noise increases, we find that contributions decrease and punishment efforts intensify, resulting in a 45% drop in gains compared to a noiseless control. Moreover, we observe that uncertainty causes a rise in antisocial punishment, a mutually harmful behavior previously associated with societies with a weak rule of law. Our approach brings to light challenges to cooperation that cannot be explained by economic rationality and strengthens the case for further investigations of the effect of noise---and not just bias---on human behavior.</p>
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>
Data from: Stochasticity leads to coexistence of generalists and specialists in assembling mutualistic communities
<p>Previous models for assembling ecological networks did not include stochasticity at the level of population dynamics (e.g., demographic noise, environmental noise) and focused mainly on food webs. Here, we present a model for the assembly of mutualistic bipartite networks, such as plant-pollinator networks, and examine the influence of demographic noise on the trajectory of species and strategy diversity, i.e., the range of present strategies from specialism to generalism. We find that assembled communities show at intermediate assembly stages a maximum of species diversity and of average generalization. Our model thus provides a mechanism for non-linear, hump-shaped diversity trajectories at intermediate succession, consistent with the intermediate disturbance hypothesis. Long-term coexistence of specialists and generalists emerges only in the presence of demographic noise and is due to a persistent species turnover. These findings highlight the importance of stochasticity for maintaining long-term diversity.</p>
Determinism and stochasticity in the spatial-temporal continuum of ecological communities: the case of tropical mountains
<p>Ecological communities are assembled in a spatial-temporal continuum. However, we still have a poor understanding of the relative importance of different mechanisms structuring community composition (i.e., beta-diversity) in space and time. In this study, we start by introducing a conceptual model that capitalizes upon the core-occasional species concept to predict that the assembly process in tropical mountains is driven by the deterministic turnover of core species in space via habitat sorting, but the turnover of occasional species through time via stochastic events of colonization and local extinctions. We then propose a general analytical framework that allows assessing these predictions by partitioning the total variance of a species-by-site-by-time matrix (i.e., total beta-diversity) among its purely spatial (variation in space independent of time), purely temporal (variation in time independent of space), and spatiotemporal (i.e., variation across different sites across different moments in time) components. Through simulation models, we provided theoretical support that the proposed analytical framework is suitable to test the predictions derived from our conceptual model. We then used this framework to identify general patterns and quantify the relative importance of processes underlying the spatial and temporal organization of ten distinct insect metacommunities along a tropical elevational gradient. As predicted, we found that, across taxa, spatial beta-diversity was mainly explained by environmental variation alone: a pattern that indicates the spatial turnover of core species. In contrast, temporal beta-diversity could not be distinguished from the expectation of null models where communities are simply represented by random draws from species pools: a pattern that indicates a temporal turnover of occasional species within communities. Taken together, our findings illustrate how our conceptual model and quantitative framework can articulate a better understanding of community assembly in space and time.</p>
A High-Resolution Stochastic Modeling Method for Elastic Parameters Based on FDMA
<p>Data used in article ‘A High-Resolution Stochastic Modeling Method for Elastic Parameters Based on FDMA’. Including logging data, seismic P-wave velocity data and data of figures in this article.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
A Stochastically Generated meteorological forcings for the Laurentian Great Lakes to support the Great Lakes Restoration Initiative
<p>The dataset consists of three folders, each with time series for the Laurentian Great Lakes and their contributing watersheds: </p> <ol> <li>"observed_historical.zip" : containing daily forcings for Large Lake Thermodynamics Model and Large Basin Runoff Model and Overlake Precipitation time series from GLERL Hydrometeorological Database and consistent with the inputs of GLSGyFS software.</li> <li>"baseline.zip" : 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator, without any thermodynamic climate perturbations. </li> <li>"scenarios.zip" : 30 scenarios of each 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator with different thermodynamic perturbations, consistent with Coupled Model Intercomparison Project 6 (CMIP6) projections for the Great Lakes region. </li> </ol> <p> </p> <p><strong>Acknowledgements: </strong></p> <p>This work was funded by the Great Lakes Restoration Initiative (GLRI) Action Plan 3, Focus Area 5.2 Conduct Comprehensive Science Programs and Projects. Funding for this work was also provided through the National Science Foundation Grant No. CBET-2144332, and the U.S. Geological Survey Northeast Climate Adaptation Science Center, which is managed by the USGS National Climate Adaptation Science Center, under Grant/Cooperative Agreement No. G21AC10601-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the Northeast Climate Adaptation Science Center or the U.S. Geological Survey.</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>
Stochastic Ocean Energy Backscatter via Pressure and Momentum Perturbation
<p>Our research aims to enhance the representation of mesoscale eddies in ocean models, particularly for eddy-permitting resolutions, by incorporating a dynamic backscatter parameterization and additional stochastic perturbations. This study addresses several key modeling issues, including enhancing the representation of missing variability through stochastic forcing, the need for incorporating stochastic terms alongside dynamic backscatter, the propagation of energy across scales and regimes, and distinguishing between different stochastic approaches.</p> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>The output data from the FESOM2 model (<a href="https://fesom.de/" target="_new" rel="noreferrer">https://fesom.de/</a>) is available here. The file names include information about the corresponding plot in the paper, the simulation name, and the relevant diagnostic variable. Additionally, the uploaded data includes the high-resolution array used to produce stochastic perturbation.</p> </div> </div> </div> </div> </div> </div>
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>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
Revisiting the impacts of Stochastic Multicloud model on the MJO using low-resolution ECHAM6.3 atmosphere model
<p>There are the corresponding source codes and input data used to run the numerical experiments. Analysis scripts and model results are also included.</p>
Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling
<p>This dataset and the scripts accompany the manuscript "<strong>Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling</strong>". The manuscript is published in the Journal Climatic Change.</p>
Transcriptional stochasticity reveals multiple mechanisms of long non-coding RNA regulation at the Xist – Tsix locus
<p>Data and codes for Figure reprodicbility, image processing and demo.</p>
Support data for article "Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic"
<p>Support data for article</p> <p>V. Kovtun, K. Grochla, V. Kharchenko, M. A. Haq, and A. Semenov, “Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic,” Scientific Reports, vol. 13, no. 1. Springer Science and Business Media LLC, Dec. 20, 2023. doi: 10.1038/s41598-023-49007-2.</p> <div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p> </div>
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>
Data and code from: Species interactions drive continuous assembly of freshwater communities in stochastic environments
<p>Understanding the factors driving the maintenance of long-term biodiversity in changing environments is essential for improving restoration and sustainability strategies in the face of global environmental change. Biodiversity is shaped by both niche and stochastic processes, however the strength of deterministic processes in unpredictable environmental regimes is highly debated. Since communities continuously change over time and space -- species persist, disappear or (re)appear -- understanding the drivers of species gains and losses from communities should inform us about whether niche or stochastic processes dominate community dynamics.<br>Applying a nonparametric causal discovery approach to a 30-year time series containing annual abundances of benthic invertebrates across 66 locations in New Zealand rivers, we found a strong \hl{negative} causal relationship between species gains and losses directly driven by predation indicating that niche processes dominate community dynamics. Despite the unpredictable nature of these system, environmental noise was only indirectly related to species gains and losses through altering life history trait distribution. Using a stochastic birth-death framework, we demonstrate that the negative relationship between species gains and losses can not emerge without strong niche processes. Our results showed that even in systems that are dominated by unpredictable environmental variability, species interactions drive continuous community assembly. </p>
ScienceDex guides
Understand access before you commit
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.