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373 results for “stochastic”
Subset of stochastically generated interacting molecules for CH GAP interatomic potential
<p>This is a subset of the dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>This subset contains the interacting molecules generated stochastically in a following manner. The subset is generated using active learning and uncertainty-based configuration selection. We started with randomly chosen pairs of CH-containing molecules from the QM9 database up to 7 carbon atoms. The probability of selecting the molecules is set based on the energy and size of the molecule. The probability is lower as the energy above the convex hull is higher. A bigger size of the molecule also lowers the probability to favor the inclusion of small structures. Then, we estimate the uncertainties for the new structures based on how far away from the existing interacting molecules in the training set they are (in configuration space), and identify those with the largest expected errors. This way, we generated about 3k structures. </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>
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 from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part II: adjoint frequency response analysis, stochastic models, and synthesis
<p>Meta data updated after publication.</p> <p> </p>
Data from: Process-based modelling of nonharmonic internal tides using adjoint, statistical, and stochastic approaches. Part I: statistical model and analysis of observational data
<p>Meta data updated after publication.</p> <p> </p>
Stochastic synaptic plasticity underlying compulsion in an addiction model
<p>Data set for article published in Nature: Pascoli 2018 https://doi.org/10.1038/s41586-018-0789-4</p>
Data analysis results for: "MoDLE: High-performance stochastic modeling of DNA loop extrusion interactions"
<p>Due to technical issues we are unable to upload the updated version of this dataset on Zenodo.<br> <br> The latest version of this dataset can be found on the NRID research data archive at DOI <a href="https://doi.org/10.11582/2022.00056">10.11582/2022.00056</a>.</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>
AI-SPRINT GPU STochastic Scheduler
<p>This repository includes the source code and the datasets used to evaluate the GPU STochastic Scheduler developed in the context of the AI-SPRINT project. The corresponding results are included in the AI-SPRINT project deliverable "D3.3 - Second release and evaluation of the runtime environment".</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>
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>
Extremely stochastic connectivity of island mangroves
Studies of mangrove population connectivity have focused primarily on global to regional scales and have suggested potential for long-distance connectivity, with archipelagos serving as stepping stones for trans-oceanic dispersal. However, the contribution of propagule dispersal to connectivity is still largely unknown, especially at local-scale. Identifying fine-scale propagule dispersal patterns unique to individual island systems is important to understand their contribution to global species distributions, and to select appropriate sizes and locations for mangrove conservation in archipelagos. Using population genetic methods and a release-recapture method employing GPS drifting buoys, we investigated the spatiotemporal scale of propagule dispersal of Rhizophora stylosa, one of the widely distributed mangrove species in the Indo-West Pacific. This study sought to quantify intra- and inter-island connectivity and to assess their contributions to oceanic scale dispersal of R. stylosa from the Ryukyu Archipelago, which spans over 545 km in southwestern Japan. Using 7 microsatellite markers, we tested 354 samples collected from 16 fringing populations on 4 islands. We identified 3 genetic populations, indicating distinct genetic structures comprising 3 distinguishable bioregions (genetic clusters). The western end of the archipelago receives relatively frequent migration (m &gt; 0.1), but is genetically isolated from other sites. Based on genetic migration rates, we found that the central area of the archipelago serves as a stepping stone for southwestward, but not northeastward dispersal. On the other hand, with in-situ drifting buoys, we did not confirm prevailing dispersal directionality within the archipelago, instead confirming local eddies. Some buoys trapped in those eddies demonstrated potential for successful beaching from another island. A large portion of buoys were carried predominantly northeastward by the Kuroshio Current and drifted away from the coastal areas into the Pacific, contrary to local migrations. We found that the spatiotemporal scale of propagule dispersal is limited by the distance between islands (&lt; 200km), propagule viability duration, and fecundity. Over all, recruitment does not occur frequently enough to unify the genetic structure in the archipelago, and the Ryukyu Archipelago is isolated in the center of the global mangrove distribution.
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
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>
WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Earthquake Stochastic Event Set for Europe (ISO3166 code)
<p>Stochastic event set of earthquake ground motion sets, and respective event catalogue. The whole event set covers 10.000 years.<br> Ground motion provided as PGA and spectral components for [0.2s, 0.5s, 1.0s, 2.0s]. <br> csv files follow xyz-structure. Each column (except 1 and 2) represent an earthquake.<br> longitude/latitude/{Magnitude}_PGA{PGAmax}/...</p> <p>For spectral component, the final results were filtered removing events with maximum groundmotions <0.001g. </p>
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
<p>Model, data, and a script to a paper of respective name</p>
Effect of stochastic deformation on the vibration characteristics of a tube bundle in axial flow: code and data
<p>These files accompany the following publication:</p> <p>Dolfen, H., Vandewalle, S., & Degroote, J. (2023). Effect of stochastic deformation on the vibration characteristics of a tube bundle in axial flow. Nuclear Engineering and Design, 411, 112412. <a href="https://doi.org/10.1016/j.nucengdes.2023.112412">doi:10.1016/j.nucengdes.2023.112412</a>.</p> <p>In this publication the effect of a stochastic bow deformation on the vibration characteristics of a tube bundle was investigated. The Monte Carlo and generalized Polynomial Chaos (gPC) method were used. For the latter method, the <a href="https://chaospy.readthedocs.io/en/master/">chaospy Python-package</a> was used. Further dependencies include the numpy, scipy and matplotlib Python packages. The gPC was benchmarked with the Monte Carlo method on a steady CFD case. The the gPC was used on an FSI case used to extract the output quantity of interest, the vibration characteristics. This FSI case was run in the open-source code <a href="https://github.com/pyfsi/coconut">CoCoNuT</a>. This code developed at Ghent University is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p> <p>The archive includes scripts to set-up the steady CFD case as well as the FSI case, the used version of CoCoNuT and some post-processing scripts. ReadMe files are provided to explain the files, and what adjustments are likely needed to make it work on a different system. For CoCoNuT to work, the 'coconut' folder should be added to the PYTHONPATH environment variable. For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>.</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Soft trade-offs and the stochastic emergence of diversification in E. coli evolution experiments
<p>Laboratory experiments of bacterial colonies (e.g., <em>Escherichia coli</em>) under well-controlled conditions often lead to evolutionary diversification in which (at least) two ecotypes, each one specialized in the consumption of a different set of metabolic resources, branch out from an initially monomorphic population. Empirical evidence suggests that, even under fixed and stable conditions, such an "evolutionary branching'' occurs in a stochastic way, meaning that: (i) it is observed in a significant fraction, but not all, of the experimental repetitions, (ii) it may emerge at broadly diverse times, and (iii) the relative abundances of the resulting subpopulations are variable across experiments. Theoretical approaches shedding light on the possible emergence of evolutionary branching in this type of condition have been previously developed within the theory of "adaptive dynamics''. Such approaches are typically deterministic –or incorporate at most demographic or finite-size fluctuations which become negligible for the extremely large populations of these experiments– and, thus, do not permit to reproduce the empirically observed large degree of variability. Here, we make further progress and shed new light on the stochastic nature of evolutionary outcomes by introducing the idea of "soft'' trade-offs (as opposed to"hard'' ones). This introduces a natural new source of stochasticity which allows one to account for the empirically observed variability as well as to make predictions for the possibility of evolutionary branching to be observed, thus helping to bridge the gap between theory and experiments.</p>
AT3D-PART2: Stochastically Generated Clouds
<p>The extinction fields are in netCDF format. They can be read with xarray using python, for example.</p> <p>Associated with:</p> <p>Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-44, in review, 2023</p> <p>Data Generated using:</p> <p>Loveridge, J., Levis, A., Aides, A., Forster, L., and Holodovsky, V.:</p> <p>Atmospheric Tomography with 3D Radiative Transfer, v4.1.2,</p> <p>Zenodo [code], https://doi.org/10.5281/zenodo.7062466, 2022.</p> <p> </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.