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23 results for “Continuum model”
Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities
<p>Numerical data used to make Figures in the manuscript entitled "Machine learning predicts earthquakes in the continuum model of a rate-and-state fault with frictional heterogeneities". We provide the data to create Figures 1 to 4 from the main text and Figures S1 to S9 from the supplementary information. We also provide Python scripts to plot them.</p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Dataset for: "Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Modeling the Hα Emission Surrounding Spica using the Lyman Continuum from a Gravity-darkened Central Star</em>", accepted to the Astrophysical Journal. The tar.gz archive file contains an example Cloudy script and associated data. The complete listing of the files included in this archive file are given here: </p> <p> ReadMe Documentation file<br> example_cloudy_input_file.txt Cloudy script<br> spica_i=100_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=110_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=116_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=120_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=130_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=140_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=150_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=160_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=170_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=180_V5_solar.ascii Spica input stellar spectral energy distribution<br> spica_i=90_V5_solar.ascii Spica input stellar spectral energy distribution<br> </p> <p><strong>System requirements:</strong> The input script and SED files correspond to Cloudy code version 17.02:<br> Codebase: <a href="https://trac.nublado.org/">https://trac.nublado.org/</a><br> Primary documentation: Ferland et al. (<a href="https://ui.adsabs.harvard.edu/abs/2017RMxAA..53..385F/abstract">2017RMxAA..53..385F</a>; arXiv:<a href="https://arxiv.org/abs/1705.10877">1705.10877</a>)<br> </p> <p>Additional documentation provided in the ReadMe file.</p>
Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors
<p>Data supporting the manuscript published in PRX Energy titled "Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors". Jupyter notebook and included data for recreating the figures in the paper and for additional analysis.</p>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></p>
Paranal Airglow Line And Continuum Emission (PALACE) model: data and code of v1.0
<p>This data and code release is related to the article "PALACE v1.0: Paranal Airglow Line And Continuum Emission model", which has been published in Geoscientific Model Development, Vol. 18, 2025 (https://doi.org/10.5194/gmd-18-4353-2025).<br>The release consists of three .zip files:<br>PMD.zip: 436 ASCII files with data that were used to build and evaluate the model. More details in PMD/pmd_README.txt.<br>PALACE.zip: Python/Cython code for the calculation of the model. More details in PALACE/README.txt.<br>test.zip: Test output of the code for the default parameters (.dat file in alternative ASCII format).</p>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</p>
Data for Coupling Covariance Matrix Adaptation with Continuum Modeling for Determination of Kinetic Parameters Associated with Electrochemical CO2 Reduction
<p>This data set contains digitized and tagged polarization and partial current density data for 18 datasets of CO<sub>2</sub> reduction to H<sub>2</sub> and CO over Ag catalysts, as well as 8 datasets of CO<sub>2</sub> reduction to HCOO<sup>-</sup>, CO, and H<sub>2</sub> over Sn catalysts. We analyze this data using a coupled continuum modeling and covariance matrix adaptation approach for which the codebase is provided in DOI: 10.5281/zenodo.7866195.</p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE)
<p>CONTINUUM HYDROLOGICAL MODEL SAMPLE DATA FOR WRF 24 OCTOBER 2021 (APOLLO MEDICANE CASE). Data are ready for publication on MyDewetra platform</p>
Scripts developed to modelling the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery.
<p>The zipfile contains the scripts developed in the paper on the modelling of the nocturnal ecological continuum of the State of Geneva, Switzerland, based on high-resolution nighttime imagery, as published in Remote Sensing Applications: Society and Environment journal (RSASE - Elsevier). </p> <ul> <li>Scripts developed for the extraction of light sources from night orthophotography ; [SAFE Software Inc. (2017). FME Desktop Esri Edition.Version 2017.1.]</li> <li>ModelBuilder developed for the visibility modelling of light sources ; [ESRI (2017). ArcGIS Desktop Pro.Version 2.1.]</li> </ul>
Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics (data for figures)
<p>This is the data used to create the figures in the article:</p> <h4>Contrasting Views of the Electric Double Layer in Electrochemical CO<sub>2</sub> Reduction: Continuum Models vs Molecular Dynamics</h4> <div>Evan Johnson and Sophia Haussener</div> <div>The Journal of Physical Chemistry C <strong>2024</strong> <em>128</em> (25), 10450-10464</div> <p>DOI: 10.1021/acs.jpcc.4c03469</p> <p>See the file "Naming conventions" for the file names and column/row meanings. </p>
Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"
<p>This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework".</p> <p>Below are the details of which file is required to produce which figure:</p> <p> </p> <p><strong>Figure 5 (De_dam_fields.pdf) </strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a></p> <p> </p> <p><strong>Figure 6 (convergence.pdf)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a></p> <p>Contains 4 files, each one for a different temporal resolution (delta t).</p> <p> </p> <p><strong>Figure C1 (convergence2.pdf, appendix)</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a></p> <p>Each contains 4 files, one for each temporal resolution (delta t).</p> <p> </p> <p><strong>Figure 7 (CPU_time.pdf)</strong></p> <p>No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.</p> <p> </p> <p><strong>Figure 8 (T_h.pdf)</strong></p> <p><strong>Left panels, a, c, e</strong></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a></p> <p><a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a></p> <p>Each contains 4 files, one for each healing time (T_h)</p> <p><strong>Right panels, b, d, f</strong></p> <p>comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz</p> <p>Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)</p> <p> </p> <p><strong>Figure 9 (comp_ddam_We_0_001.pdf)</strong></p> <p>comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 10 (comp_ddam_We_0_1.pdf)</strong></p> <p>comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz</p> <p>One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)</p> <p> </p> <p><strong>Figure 11 (discussion.pdf)</strong></p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz</p> <p>u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz</p> <p> </p> <p><strong>SI movie</strong></p> <p>SI_movie.tar.gz</p>
M Dwarf Flare Continuum Variations on One-Second Timescales: Calibrating and Modeling of ULTRACAM Flare Color Indices
<p>These files contain the ULTRACAM light curves, the ULTRACAM flare color indices, the APO/DIS spectral data, and the NMSU 1-m U-band photometry from "M Dwarf Flare Continuum Variations on One-Second Timescales: Calibrating and Modeling of ULTRACAM Flare Color Indices", Kowalski, A. F., Mathioudakis, M., Hawley, S. L., Wisniewski, J. P., Dhillon, V. S., Marsh, T. R., Hilton, E. J., & Brown, B. P., 2016, Astrophysical Journal in press. See the included ReadMe file for detailed information on the files.</p>
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
Turbulent and laminar flow in karst conduits under unsteady flow conditions – interpretation of pumping tests by discrete conduit-continuum modeling
<p>Due to the duality in terms of (1) the groundwater flow field and (2) the discharge conditions, flow patterns of karst aquifer systems are complex. Estimated aquifer parameters may differ by several orders of magnitude from local (borehole) to regional (catchment) scale because of the large aquifer heterogeneity and anisotropy. One approach to deal with this scale problem in karst aquifers is the application of large-scale experiments such as long-term high-abstraction conduit pumping tests, stimulating measurable groundwater drawdown in both, the karst conduit system as well as the fissured and fractured aquifer matrix. The numerical discrete conduit-continuum modeling approach MODFLOW-2005 Conduit Flow Process Mode1 (CFPM1) is employed to simulate laminar and non-laminar conduit flow, induced by large-scale experiments, in combination with Darcian matrix flow. Effects of large-scale experiments were simulated for idealized settings. Subsequently, diagnostic plots and analyses of different fluxes are applied to interpret differences in the simulated conduit drawdown and general flow patterns.</p> <p>The main focus is set on the question to which extent different conduit flow regimes will affect the conduit as well as the matrix drawdown depending on the hydraulic properties of the conduit system, i.e. conduit diameter and relative roughness In this context, CFPM1 is applied to investigate the significance of turbulent flow consideration for the simulation of karst conduit flow. This work quantifies the relative error that results from assuming laminar conduit flow for the interpretation of a synthetic large-scale pumping test in karst.</p> <p>https://doi.org/10.1002/2017WR020658</p> <p> </p>
Simulation data for: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox
<p>This dataset includes the simulation data needed to create the plots for a manuscript: Semi-continuum modelling of unsaturated porous media flow to explain the Bauters' paradox. All data can be reproduced using the code of the semi-continuum model available in https://doi.org/10.5281/zenodo.6837742</p> <p>The readMe file was not included in Version 3 (all simulation data are the same as in version 3).</p>
Data from: Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: an approximate Bayesian computation approach
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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.