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308 results for “Dynamical systems”
Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"
<p>Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"</p>
Dataset_2020_Biogeosciences_Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems
<p>Dataset associated to the publication: Erktan, A., Rillig, M.C., Carminati, A., Jousset, A., Scheu, S. (2020) Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems, Biogeosciences, accepted the 27/08/2020.</p>
Simulation of Systems-of-Systems Dynamic Architectures
<p>Video presentation of the paper entitled "Simulation of Systems-of-Systems Dynamic Architectures" of the Undergraduate Research on Software Engineering Competition in the context of the Brazilian Symposium on Software Engineering</p>
The influence of dynamic topography, climate, and tectonics on the Nile River source-to-sink system – Model input data
<p>Input data for Badlands models used in 2020 Honours thesis at the University of Sydney.</p>
Data from: A geology and geodesy based model of dynamic earthquake rupture on the Rodgers Creek-Hayward-Calaveras fault system, California
<p>The Hayward fault in California's San Francisco Bay area produces large earthquakes, with the last occurring in 1868. We examine how physics-based dynamic rupture modeling can be used to numerically simulate large earthquakes on not only the Hayward fault, but also its connected companions to the north and south, the Rodgers Creek and Calaveras faults. Equipped with a wealth of images of this fault system, including those of its 3D geology and 3D geometry, in addition to inferences about its interseismic creep rate pattern and rock-friction behavior, we use a finite-element computer code to perform 3D dynamic earthquake rupture simulations. We find that the rock properties affect the locations and amount of slip produced in our simulated large earthquakes. Crucial factors that control rupture behavior in our modeling are the earthquake nucleation locations, the fault geometry, and the data that reveal where the fault system is creeping or locked. Our findings suggest that large Rodgers Creek-Hayward-Calaveras-Northern Calaveras (RC-H-C-NC) fault-system earthquakes may result from dynamic rupture that starts in a locked part of the fault system, but is then stopped by the creeping parts, leading to high magnitude-6 earthquakes; or, from dynamic rupture that starts in a locked part of the fault system, then cascades through some of the creeping parts, leading to magnitude-7 earthquakes.</p>
Data from: Transient recovery dynamics of a predator–prey system under press and pulse disturbances
Background: Species recovery after disturbances depends on the strength and duration of disturbance, on the species traits and on the biotic interactions with other species. In order to understand these complex relationships, it is essential to understand mechanistically the transient dynamics of interacting species during and after disturbances. We combined microcosm experiments with simulation modelling and studied the transient recovery dynamics of a simple microbial food web under pulse and press disturbances and under different predator couplings to an alternative resource. Results: Our results reveal that although the disturbances affected predator and prey populations by the same mortality, predator populations suffered for a longer time. The resulting diminished predation stress caused a temporary phase of high prey population sizes (i.e. prey release) during and even after disturbances. Increasing duration and strength of disturbances significantly slowed down the recovery time of the predator prolonging the phase of prey release. However, the additional coupling of the predator to an alternative resource allowed the predator to recover faster after the disturbances thus shortening the phase of prey release. Conclusions: Our findings are not limited to the studied system and can be used to understand the dynamic response and recovery potential of many natural predator–prey or host–pathogen systems. They can be applied, for instance, in epidemiological and conservational contexts to regulate prey release or to avoid extinction risk of the top trophic levels under different types of disturbances.
Data from: Mechanical sensitivity reveals evolutionary dynamics of mechanical systems
A classic question in evolutionary biology is how form–function relationships promote or limit diversification. Mechanical metrics, such as kinematic transmission (KT) in linkage systems, are useful tools for examining the evolution of form and function in a comparative context. The convergence of disparate systems on equivalent metric values (mechanical equivalence) has been highlighted as a source of potential morphological diversity under the assumption that morphology can evolve with minimal impact on function. However, this assumption does not account for mechanical sensitivity—the sensitivity of the metric to morphological changes in individual components of a structure. We examined the diversification of a four-bar linkage system in mantis shrimp (Stomatopoda), and found evidence for both mechanical equivalence and differential mechanical sensitivity. KT exhibited variable correlations with individual linkage components, highlighting the components that influence KT evolution, and the components that are free to evolve independently from KT and thereby contribute to the observed pattern of mechanical equivalence. Determining the mechanical sensitivity in a system leads to a deeper understanding of both functional convergence and morphological diversification. This study illustrates the importance of multi-level analyses in delineating the factors that limit and promote diversification in form–function systems.
Data from: Environmental fluctuations restrict eco-evolutionary dynamics in predator-prey system
Environmental fluctuations, species interactions and rapid evolution are all predicted to affect community structure and their temporal dynamics. Although the effects of the abiotic environment and prey evolution on ecological community dynamics have been studied separately, these factors can also have interactive effects. Here we used bacteria–ciliate microcosm experiments to test for eco-evolutionary dynamics in fluctuating environments. Specifically, we followed population dynamics and a prey defence trait over time when populations were exposed to regular changes of bottom-up or top-down stressors, or combinations of these. We found that the rate of evolution of a defence trait was significantly lower in fluctuating compared with stable environments, and that the defence trait evolved to lower levels when two environmental stressors changed recurrently. The latter suggests that top-down and bottom-up changes can have additive effects constraining evolutionary response within populations. The differences in evolutionary trajectories are explained by fluctuations in population sizes of the prey and the predator, which continuously alter the supply of mutations in the prey and strength of selection through predation. Thus, it may be necessary to adopt an eco-evolutionary perspective on studies concerning the evolution of traits mediating species interactions.
Data from: The rate of environmental fluctuations shapes ecological dynamics in a two-species microbial system
Species interactions change when the external conditions change. How these changes affect microbial community properties is an open question. We address this question using a two-species consortium in which species interactions change from exploitation to competition depending on the carbon source provided. We built a mathematical model and calibrated it using single-species growth measurements. This model predicted that low frequencies of change between carbon sources lead to species loss, while intermediate and high frequencies of change maintained both species. We experimentally confirmed these predictions by growing co-cultures in fluctuating environments. These findings complement more established concepts of a diversity peak at intermediate disturbance frequencies. They also provide a mechanistic understanding for how the dynamics at the community level emerges from single-species behaviors and interspecific interactions. Our findings suggest that changes in species interactions can profoundly impact the ecological dynamics and properties of microbial systems.
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 2(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset contains data used for individual particle detection, and is a companion of the main dataset (10.5281/zenodo.10083568). The files need to be downloaded and merged into the given folder structure. Put the folder "1Pix" along with the folder "10Pix" to the folder in "210812/Data-FIS/matlab/2_Experiment/exp/".</p><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)
<p><strong>Background</strong></p><p>The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research</p><p> </p><p><strong>Description of the dataset</strong></p><p>This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.</p><p>The following data is included</p><ul><li>individual experimental data and results in the folders<ul><li><strong>210812</strong> (10 µm, coarse sand, low-flow)</li><li><strong>220727</strong> (1 µm, coarse sand, low flow)</li><li><strong>220803</strong> (3 µm, coarse sand, low-flow)</li><li><strong>220818</strong> (1 µm, fine sand, low-flow)</li><li><strong>220901</strong> (1 µm, coarse sand, high-flow)</li></ul></li><li><strong>Comparison</strong> (comparing individual results of the experiments)</li><li><strong>Scripts</strong> (contains the individual matlab scripts)</li><li><strong>labbook.xlsx</strong> (contains metadata on the experiments, which are read out in the matlab scripts)</li></ul><p> </p><p><strong>Description of the code</strong></p><p>The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.</p><ul><li>Main scripts to analyze experimental data<ul><li><strong>Experiment_Main.m </strong>Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV</li><li><strong>Experiment_Main_Compare.m </strong>Comparison of individual experiment results</li></ul></li><li>FIS-dataset<ul><li><strong>FIS_Cal_Individual.m: </strong>Realizes individual calibrations of one experiment</li><li><strong>FIS_Cal_Result.m: </strong>Merges individual calibrations of one experiment</li><li><strong>Experiment_FIS.m: </strong>Load data of one experiment, detect interfaces. Followed by<ul><li><strong>Experiment_FIS_1pix</strong>: Individual particle detection (for 10 µm experiment, no binning)</li><li><strong>Experiment_FIS_10pix</strong>: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)</li></ul></li><li><strong>Experiment_FIS_10pix_compare.m: </strong>Compare results of particle cloud analysis for all experiments.</li></ul></li><li>Fluo-data<ul><li><strong>Fluo_Cal.m </strong>Realizes calibration for Fluorometer devices</li></ul></li><li>PIV-dataset<ul><li><strong>PIV_individual.m </strong>Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)</li><li><strong>PIV_merge.m </strong>Merge<strong> </strong>9 individual results of PIV for a result for one experiment</li></ul></li><li>Profiler-dataset<ul><li><strong>Profiler.m </strong>Analyses data from bedform profiling (merging individual measurements after the experiment)</li><li><strong>Profiler_Compare.m </strong>Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)</li><li><strong>Profiler_Time.m </strong>Analyses temporal change of bedform elevation during the experiment</li></ul></li></ul><p> </p><p><strong>Disclaimer</strong></p><p>The data and code are provided as is without any warranty.</p><p> </p><p><strong>Funding</strong></p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p>
Figure 7 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Figure 5 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).
Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.
Figure 3 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 3 Example of nectar (in yellow on the left side) and pollen (in blue on the right side) spatial and temporal distribution through the season. In each snapshot, a brighter colour indicates a higher amount of the resource in the polygon. A total of 12 snapshots were taken every 30 days, starting on day 15 of the simulation.
Figure 2 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 2 The total mass of floral resources (i.e. sugar and pollen) in the studied landscape available to bees in all the simulations. The mass of floral resources was calculated, based on the production and phenology of the individual plant species comprising the habitats present in the studied landscape and the landscape composition. Pollen availability started on simulation day 20 and nectar was available from day 39.
Figure 1 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 1 Components in ALMaSS landscape model. The blue arrow represents the access to landscape information at a 1 m2 resolution. In this example, one element has woody habitats, while the other is an arable field. The information about each element depends on its type and the temporal factors described in the green boxes. The orange box shows some of the factors derived from the landscape element type, its management and the weather.
Figure 6 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103
Figure 6 Results of the implementation of different scouting and foraging strategies on the performance of model colonies in terms of nectar collection. For each scouting strategy (i.e. distance, quality or random), four different foraging strategies (i.e. distance, energy efficiency, quality and random) were tested. The total amount of sugar collected, the mean number of daily foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.
Data for WarpX milestone ECP-ADSE06.FY21.2: Assessment of dynamic load-balancing strategies on available exascale systems.
<p>This dataset includes the inputs, outputs, job submission scripts, and data analysis scripts used to prepare the WarpX milestone report for "Assessment of dynamic load-balancing strategies on available exascale systems." The code versions of WarpX, AMReX, and PICSAR used are stored in the outputs file for each run. </p>
Chaotic Dynamics in a Two-Droplet Pilot Wave System: A Numerical Simulation
<p>Data set and results for our university modeling project.</p>
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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.
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DANDI Archive for NWB datasets
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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.