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11,506 results for “Dynamics”
HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility - Supplementary File 2
<p>This is a collection of files necessary to reproduce a subset of the results discussed in the manuscript entilted <em>HHIP’s Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility</em> by Deritei et al. (currently available on bioRxiv: <a href="https://www.biorxiv.org/content/10.1101/2024.09.05.611545v2.abstract">https://www.biorxiv.org/content/10.1101/2024.09.05.611545v2.abstract</a>).</p> <p>Files are read by the <em>dynmod</em> software (<a href="https://github.com/Ravasz-Regan-Group/dynmod">https://github.com/Ravasz-Regan-Group/dynmod</a>) to simulate the large Boolean model’s behaviors. These include the model in .dmms format (as well as .BooleanNet and .SBML formats, namely COPD_EMT_CellCycle_Apoptosis.dmms, COPD_EMT_CellCycle_Apoptosis_Fine.booleannet, the “in silico protocol” files for reproducing all figures (COPD_Figures.vex), re-running the validation experiments (Validation_Table_COPD_EMT_CellCycle_Apoptosis.vex) and running a python script that performs all validation statistical tests, generating a results list and regenerating Supplementary File 3 (Large Model Validation Script.py, ST5 - Large model validation.csv).</p> <p>The _EXP folder contains exported figures necessary for generating the main manuscript Figure 4 and Supplementary Figures 3,4 and 5.</p> <p> </p>
Nicotinamide Adenine Dinucleotide Molecular Dynamics
<p>Nicotinamide Adenine Dinucleotide. Molecular dynamics of NAD molecule is solvated with water (transparent) in UCSF Chimera software for 22 nanoseconds.<br>By Victor Padilla Sanchez, PhD - Washington Metropolitan University, President.<br>Email: drvictorpadilla@aol.com<br>Website: https://www.drvictorpadillasanchez.com</p>
Supporting Data for "Thermal Transport Through CTAB- and MTAB-Functionalized Gold Interfaces using Molecular Dynamics Simulations"
<p>This gzipped tar archive contains initial configurations and parameters used for the simulations in the manuscript:</p> <p>"Thermal Transport Through CTAB- and MTAB-Functionalized Gold Interfaces using Molecular Dynamics Simulations", by Sydney A. Shavalier, and J. Daniel Gezelter</p> <p>A note on naming conventions. All simulations have filenames with with two numbers - one that signifies simulation replica (1-5), and another that signified which step of equilibration/RNEMD was being performed. For example, lowmtab111_3opt4 would signify the third simulation replica of a low coverage MTAB system and a (111) gold facet, which was on its fourth equilibration step after optimization.</p> <p>The OpenMD simulation engine utilizes a number of file extensions that are present in this archive:</p> <p><strong>.omd</strong> : A combined MetaData and configuration file that is used to start a simulation <br><strong>.frc</strong> : a force field parameter file<br><strong>.eor</strong> : an 'end of run' or final configuration (same format as .omd)<br><strong>.stat</strong> : status file with instantaneous information about energies, temperatures, etc. These are generally large and have not been included, as they can be regenerated easily from the .omd file.<br><strong>.report </strong>: a post-simulation file containing thermodynamic averages from the .stat file<br><strong>.dump</strong> : a full trajectory file containing positions and velocities sampled at a 'sampleTime' specified in the .omd file. These are generally very large and have not been included, as they can be regenerated from the .omd file.<br><strong>.rnemd</strong> : Contains spatial information about temperatures, densities, etc. for simulations run under reverse non-equilibrium molecular dynamics</p> <p>Other data analyis or utility file extensions:</p> <p><strong> .pack</strong> : Files for creating systems with Packmol<br><strong> .z</strong> : Density \rho(z) for specific selected atom types<br><strong> .r </strong> : Density \rho(r) for specific selected atom types<br><strong> .p2z</strong> : Legendre Polynomial Correlation using z as reference axis<br><strong> .p2r</strong> : Legendre Polynomial Correlation using radial vector as reference axis<br> <strong>.chargez</strong> : Charge density as a function of z-axis<br> <strong>.charger</strong> : Charge density as a function of radius<br> <strong>.agr</strong> : Grace graphing package data<br><strong> .xyz </strong> : XYZ (Cartesian) coordinates for visualization<br> </p> <p>The archive is organized as follows:</p> <p> ./CTAB/111: Simulations of Au(111) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/111/RNEMD<br> ./CTAB/110: Simulations of Au(110) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/110/RNEMD<br> ./CTAB/100: Simulations of Au(100) functionalized with CTAB<br> final systems begin with "highctab"<br> RNEMD simulations are in ./CTAB/100/RNEMD<br> ./MTAB/111: Simulations of Au(111) functionalized with MTAB <br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/111/RNEMD<br> ./MTAB/110: Simulations of Au(110) functionalized with MTAB<br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/110/RNEMD<br> ./MTAB/100: Simulations of Au(100) functionalized with MTAB <br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/100/RNEMD<br> ./MTAB/NP/R10: Simulations of Au Nanoparticles (r = 10 angstroms),<br> functionalized with MTAB<br> final systems begin with "highmtab" or "lowmtab"<br> RNEMD simulations are in ./MTAB/NP/R10/RNEMD</p> <p>Systems that were run with metal polarizability turned on have 'fq' as part of their filenames.</p>
Molecular dynamic simulations of WT PAR2
<p>MD simulations data for WT PAR2</p>
Molecular dynamic simulations of D62A PAR2
<p>MD simulations data for D62A PAR2</p>
Molecular dynamic simulations of I39L PAR2
<p>MD simulations data for I39L PAR2</p>
Molecular dynamic simulations of I39V PAR2
<p>MD simulations data for I39V PAR2</p>
Pole Reversals and Delta Q Dynamics: A MEFI-Based Multiscale Analysis
<p>This study examines the phenomena of geomagnetic pole reversals through the Modified Einstein</p> <p>Field Interaction (MEFI) framework. By exploring Delta Q stabilization, resonance deviations, and</p> <p>their global impacts, the study demonstrates MEFI's potential as a universal model. This journal</p> <p>entry provides visual evidence and simulations to highlight the quantum, geophysical, and societal</p> <p>effects of pole reversals, underscoring MEFI?s predictive power.</p>
Stim Circuits and Data for "Tailoring Dynamical Codes for Biased Noise: The X3Z3 Floquet code"
<p>Example stim circuits used for numerical simulations (for P6, XYZ2, CSS and X3Z3 Floquet codes) and threshold data obtained. See arXiv:2411.04974 for the associated manuscript.</p>
Dynamics of tree-cavity occupancy: Data and code
<h3>Supplementary data and code to the paper entitled “Resource suitability drives low use of avian-excavated tree cavities: a multi-state occupancy dynamics approach”</h3> <p> </p> <p>The following list indicates the names of files included as supplementary data and code, as well as a brief description of their content. File names are given by headings in italic, followed by content description.</p> <p> </p> <p><em>CavNestMisiones.rds</em> </p> <p>Contains the data used to fit the model in file <em>MSODynModel.R</em>. It includes the following objects:</p> <p> <strong>y</strong> An array with dimensions 452x20x16 showing the observed cavity states for each of 452 cavities, a maximum of 20 visits per cavity per year, and 16 years. This array contains NAs prior to the first year of cavity registration, after cavity loss, and in any year*visit combination for which there was no data for the corresponding cavity.</p> <p><strong>origen </strong>A vector with length 452 with ‘1’ for excavated and ‘2’ for non-excavated cavities.</p> <p> </p> <p><em>MSODynModel.R</em></p> <p>Model code including preliminary data processing, JAGS code for the dynamic multi-state occupancy model, and call to JAGS to fit the model. Model code includes posterior predictive checking for goodness-of-fit.</p> <p> </p> <p><em>MSMO60k.Rdata</em></p> <p>JAGs output with MCMC samples from the posterior probability distribution of model parameters.</p> <p> </p> <p><em>SimCavs.R</em></p> <p>Code for simulating cavity use.</p> <p> </p> <p><em>SimCavsFunctions.R</em></p> <p>Functions used in <em>SimCavs.R.</em></p>
Exo-Metabolomics Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Exo-Metabolomics Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
Illumina Sequencing Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Illumina Sequencing Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study - Second part
<p>Second part of the data deposition for the paper "<strong>How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study</strong>", by Lemmens et al. Part one is availible via <a href="https://doi.org/10.5281/zenodo.13929049">10.5281/zenodo.13929049</a></p>
S3GM: Learning spatiotemporal dynamics with a pretrained generative model
<h1>Datasets of Kuramoto-Sivashinsky equation (KSE) and Kolmogorov flow.</h1> <h2>Description of KSE data:</h2> <p>Each file for KSE datasets contains 4 dimensions in the following order: (B*V)*T*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>V</td> <td>number of varying parameters</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 1 for KSE)</td> </tr> <tr> <td>values of parameter used to generate <strong>training </strong>dataset</td> <td>1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0</td> </tr> <tr> <td>values of parameter used to generate <strong>test </strong>dataset</td> <td>1.1, 2.5, 3.2</td> </tr> </tbody> </table> <h2>Description of Kolmogorov flow data:</h2> <p>Each file for Kolmogorov flow contains 5 dimensions inthe following order: (B*Re*K)*T*X*X*C. Details are listed in the following table:</p> <table> <tbody> <tr> <td>B</td> <td>number of varying initial conditions</td> </tr> <tr> <td>Re</td> <td>number of varying Reynolds numbers</td> </tr> <tr> <td>K</td> <td>number of varying source terms (controled by the value of k)</td> </tr> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 2 for Kolmogorov flow)</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>training </strong>dataset</td> <td>100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050</td> </tr> <tr> <td>values of Reynolds number used to generate <strong>test </strong>dataset</td> <td> <div> <div>50, 125, 575, 1100, 1500</div> </div> </td> </tr> <tr> <td>values of k used to generate <strong>training </strong>dataset</td> <td>2, 3, 4, 5, 6, 7, 8</td> </tr> <tr> <td>values of k used to generate <strong>test </strong>dataset</td> <td> <div> <div>2, 4, 6, 8</div> </div> </td> </tr> </tbody> </table> <h2>Description of ERA5 data:</h2> <p>Training and testing dataset for ERA5 contains 5 dimensions inthe following order: 1*T*X*X*C, which is manually collected from <a href="https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download">https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download</a>. <strong>Note that the quantities in the datasets are already rescaled</strong> (the scale factors are saved in the scalar_era5.npy file, which is a 4x2 array recording the means and stds for the 4 quantities we used). Details are listed in the following table:</p> <table> <tbody> <tr> <td>T</td> <td>number of temporal frames</td> </tr> <tr> <td>X</td> <td>spatial resolution</td> </tr> <tr> <td>C</td> <td>number of variables in solution (C = 4 for ERA5)</td> </tr> <tr> <td>time span for <strong>training </strong>dataset</td> <td>1979-2022</td> </tr> <tr> <td>time span for <strong>test </strong>dataset</td> <td> <div> <div>2023</div> </div> </td> </tr> </tbody> </table> <h2>Pretrained checkpoints:</h2> <p>The .zip file contains the pretrained checkpoints for KSE, Kolmogorov flow and ERA5. Within the .zip file, the folder'kse_v0' is the checkpoint for KSE, 'kol_v0' is the checkpoint for Kolmogorov flow, and 'era5_v0' is the checkpoint for ERA5.</p> <h1><em>Source code:</em></h1> <p>The source code is upload as Github repository in <a href="https://github.com/lzy12301/S3GM">https://github.com/lzy12301/S3GM</a></p>
Mid-Late Holocene coral calcification dynamics: Deciphering climatic and environmental effects
<p><span>Over the past four decades, a marked decrease in coral calcification has occurred across the world's tropical reefs, closely linked to climate change and human-activity impacts. However, how natural and human-induced factors influence coral calcification remains unclear due to limited understanding of the geological past. This study addresses this gap by investigating the calcification parameters of 82 <em>Porites</em> corals from the northern South China Sea, with growth periods covering distinct climatic epochs during the Mid-Late Holocene, including the Holocene Climate Optimum, 4.2 ka BP event, Medieval Climate Anomaly, Little Ice Age, and Current Warm Period. Our findings show a gradual increase in coral skeletal density towards the present, and varied linear extension and calcification rates between warm and cold phases and between pre- and post-industrial periods. This suggests that temperature plays a pivotal role in controlling coral calcification, with contingent influences from volcanic activity and solar radiation. Notably, the linear extension and calcification rates were significantly reduced during the Current Warm Period, suggesting a surpassing impact of contemporary human activities over the natural variability on coral calcification. This raises concerns about the future prospects of coral reefs in the face of ongoing climate change and increasing human-activity impacts.</span></p>
Insights into the DNA and RNA Interactions of Human Topoisomerase III Beta Using Molecular Dynamics Simulations
<p>hTOP3 simulations for both covalently and non-covalently bound DNA and RNA substrates. Simulation times = 300ns, with 1/ns per frame = 300 frames each.</p>
Role of individual and population heterogeneity in shaping dynamics of multi-pathogen shedding in an island endemic bat
<p>Dataset for the manuscript "<strong>Role of individual and population heterogeneity in shaping dynamics of multi-pathogen shedding in an island endemic bat</strong>", including information of the bat samples and infection data.</p>
Data from "Bending the course of evolution: how mutualistic interactions affect macroevolutionary dynamics of diversification in mimetic Ithomiini butterflies"
<p>Data and codes for the analyses from Chazot et al. "Bending the course of evolution: how mutualistic interactions affect macroevolutionary dynamics of diversification in mimetic Ithomiini butterflies". Check README for more information about the files. </p>
Replication package for "An Empirical Evaluation of Static, Dynamic, and Hybrid Slicing of WebAssembly Binaries"
<p>This is the replication package that accompanies the paper "An Empirical Evaluation of Static, Dynamic, and Hybrid Slicing of WebAssembly Binaries".</p> <p>It is structured as follows.</p> <p> - RQs.py is the script that generates the data that is included in the paper. For each research question, it generates statistics and plots.<br> - The .csv files and nok.txt are used by RQs.py. They contain the raw size and timing data for each slice. The headers of the .csv files indicate what each column represents.<br> - `sqlite-slices.csv` contains the raw data for RQ8, which is contained in the paper<br> - The .tar.gz files contain the source and the slices generated by each slicer, namely:<br> - `static_slices.tar.gz` contains the slices generated by CsE, the static slicer. For example, the file `static_slices/adpcm/adpcm_ah1_254_expr/static_adpcm.wat.slice` is the slice of the adpcm program taken with respect to the variable `ah1` at line `254` of the original `.c` program.<br> - `slice.tar.gz` contains slices from the dynamic slicers:<br> - slice-sce - the SCE slices<br> - slice-ces - the CES slices<br> - slice-cse - the CSE slices<br> - slice-sces - the SCES slices<br> - slice-cses - the CsES slices<br> - `src.tar.gz` includes the unsliced `C` and `wasm` source<br> - `bc.support.tar.gz` includes the complete `bc` source code <br> - `sqlite.tar.gz` contains the slices of RQ8, each in a directory named after the slicer used. For example, the file `sqlite/CES/avg.wat` contains the CES slice for the `avg` slicing criterion.<br> <br>In order to regenerate the data and plots that are in the paper, you should simply run:</p> <p>```<br>python RQs.py<br>```</p>
Data repository for study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate"
<p>Data for the study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate".</p> <p><strong>hindcasting_analysis</strong></p> <ul> <li>figures of the hindcasting exercise in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>baseline scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure and export restriction scenario -- <em>agrimate_baseline=2007-2009_export_restrictions=2007-2011_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> </ul> </li> </ul> </li> </ul> <p><strong>multibreadbasket_analysis</strong></p> <ul> <li>figures of the multibreadbasket analysis in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>simulations under historical climatic conditions with <number> as an identifier -- <em>agrimate_his-<number>.nc</em></li> <li>simulations under +2°C projection with <number> as an identifier -- <em>agrimate_2p0-<number>.nc</em></li> </ul> </li> </ul> </li> <li>processed_data <ul> <li>processed output data to easier/faster plot</li> </ul> </li> </ul> <p><strong>sensitivity_analysis</strong></p> <ul> <li>raw data and graphics as in <strong>main_output</strong> for different model parameters as given in Table F.1</li> </ul> <p> </p> <p> </p>
ScienceDex guides
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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.