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45 results for “Stochastic dynamics”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Data from: Dynamics of mixed-ploidy populations under demographic and environmental stochasticities
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Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system
<p>Understanding indirect interspecific effects (IIEs) on population dynamics is key for predicting community dynamics. Yet, empirically teasing apart IIEs from other interactions and population drivers is data-demanding. We used stochastic population models parameterized with long-term vital rate time series to simulate population trajectories and examine IIEs in a high-arctic vertebrate trophic chain: Svalbard reindeer, its scavenger (Arctic fox), and a migratory fox prey (barnacle goose). Reindeer carcass supply shaped fox abundance fluctuations, subsequently affecting goose fluctuations. Yet reindeer and goose population growth rates were only weakly correlated, probably due to stochasticity, density dependence, and life history traits. However, by isolating the effects of individual processes within our simulation model, we demonstrate the presence of strong IIEs on goose population fluctuations and extinction probability. Thus, we highlight the long-term impact of species interactions, including IIEs, on species coexistence and communities, beyond immediate effects and short-term fluctuations.</p>
Dynamical ising dataset for the paper Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
<p>This dataset provide the evolution in time for the magnetizaion in the 2D Ising model evolved with Gluber dynamics for a lattice of size 64 x 64.</p>
Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection
<p>Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.</p>
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>
Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)
<p>This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.</p> <p> </p> <p> </p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome
<p>This repository contains the trajectory data from "Stochastic motion and transcriptional dynamics of pairs of distal DNA loci on a compacted chromosome".</p> <p><strong>Data sets</strong></p> <p>We provide data sets for the following constructs and imaging conditions, where we give the name of the construct and the MS2-parS genomic separation in kb:</p> <ul> <li>data_line0.csv: parS-homie-evePr-PP7, 58 kb</li> <li>data_line1.csv: parS-homie-evePr-PP7, 82 kb</li> <li>data_line2.csv: parS-homie-evePr-PP7, 88 kb</li> <li>data_line3.csv: parS-homie-evePr-PP7, 149 kb</li> <li>data_line4.csv: parS-homie-evePr-PP7, 190 kb</li> <li>data_line5.csv: parS-homie-evePr-PP7, 595 kb</li> <li>data_line6.csv: parS-homie-evePr-PP7, 3.3 Mb</li> <li>data_line3_5s.csv: parS-homie-evePr-PP7, 149 kb, 5 second time interval</li> <li>data_line0_nohomie.csv: parS-lambda-evePr-PP7, 58 kb</li> <li>data_line3_nohomie.csv: parS-lambda-evePr-PP7, 149 kb</li> </ul> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as csv files consisting of 13 columns. The column headers are:</p> <ul> <li>cell_id: a unique cell index</li> <li>time_point: the time frame</li> <li>x_blue: x-coordinate of the locus in the blue channel (units in nm)</li> <li>y_blue: y-coordinate of the locus in the blue channel (units in nm)</li> <li>z_blue: z-coordinate of the locus in the blue channel (units in nm)</li> <li>x_green: x-coordinate of the locus in the green channel (units in nm) </li> <li>y_green: y-coordinate of the locus in the green channel (units in nm)</li> <li>z_green: z-coordinate of the locus in the green channel (units in nm) </li> <li>x_Rij: x-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>y_Rij: y-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>z_Rij: z-component of the aberration corrected 3D distance vector connecting the blue and green loci (units in nm)</li> <li>red: intensity in the red channel (a.u.)</li> <li>state: inferred state using a 3-state HMM, with entries 0 (O_off), 1 (P_off), 2 (P_on)</li> </ul>
Animal data associated with: Pattern dynamics and stochasticity of the brain rhythms
<p>Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored, is the actual structure of the waves–their shapes and patterns over finite timescales. The data published here are used to study brain wave patterning in different physiological contexts using two independent approaches: the first is based on quantifying stochasticity relative to the underlying mean behavior, and the second assesses "orderliness'' of the waves' features. The corresponding measures capture the waves' characteristics and abnormal behaviors, such as atypical periodicity or excessive clustering, and demonstrate coupling between the patterns' dynamics and the animal's location, speed, and acceleration. Specifically, patterns of θ, γ, and ripple waves recorded in mice hippocampi and observed speed-modulated changes of the wave's cadence, an antiphase relationship between orderliness and acceleration, as well as spatial selectiveness of patterns, are derived from the data. The results offer a complementary–mesoscale–perspective on brain wave structure, dynamics, and functionality.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
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Animal data associated with: Pattern dynamics and stochasticity of the brain rhythms
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Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection
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Data from: The effect of demographic correlations on the stochastic population dynamics of perennial plants
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Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system
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Data and code for: Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells
<p>This dataset contains experimental data and custom R code for likelihood-based model-fitting associated with the manuscript "Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells". In particular, we estimate the per-cell establishment probability (i.e. probability that a single cell gives rise to a large population) of a resistant strain, in the presence of antibiotics at concentrations below its standard minimum inhibitory concentration. The experiments are conducted here with <em>Pseudomonas aeruginosa</em>, while the model and methods can be applied more generally.</p>
Data from: Stochastic population dynamics and life-history variation in marine fish species
We examined whether differences in life-history characteristics can explain interspecific variation in stochastic population dynamics in nine marine fish species living in the Barents Sea system. After observation errors in population estimates were accounted for, temporal variability in natural mortality rate, annual recruitment, and population growth rate was negatively related to generation time. Mean natural mortality rate, annual recruitment, and population growth rate were lower in long-lived species than in short-lived species. Thus, important species-specific characteristics of the population dynamics were related to the species position along the slow-fast continuum of life-history variation. These relationships were further associated with interspecific differences in ecology: species at the fast end were mainly pelagic, with short generation times and high natural mortality, annual recruitment, and population growth rates, and also showed high temporal variability in those demographic traits. In contrast, species at the slow end were long-lived, deepwater species with low rates and reduced temporal variability in the same demographic traits. These interspecific relationships show that the life-history characteristics of a species can predict basic features of interspecific variation in population dynamical characteristics of marine fish, which should have implications for the choice of harvest strategy to facilitate sustainable yields.
Data from: Stochasticity and infectious disease dynamics: density and weather effects on a fungal insect pathogen
In deterministic models of epidemics, there is a host abundance threshold, above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, {\it Entomophaga maimaiga}, that infects the gypsy moth, {\it Lymantria dispar}. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on {\it E. maimaiga} epizootics (epidemics in animals). We then used AIC analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects, or only density-dependence effects, do a poor job of explaining the data. Density-dependent transmission in our best model produces a host-density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.
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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.