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49 results for “Monte Carlo simulation”
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Dataset of "A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure"
<p>3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
Example of reverse Monte Carlo simulation for fcc high-entropy alloy CrMnFeCoNi
<p>The data set contains the example of reverse Monte Carlo (RMC) simulation of EXAFS spectra collected for fcc high-entropy alloy CrMnFeCoNi.</p> <p>The simulation was performed by the EvAX code freely available from http://www.dragon.lv/evax/. </p> <p> </p>
CTAO Monte Carlo Simulations - Eventlists on DL2 data level - prod5
<p>Author: Cherenkov Telescope Array Observatory; Cherenkov Telescope Array Consortium<br>Contact: open-data@cta-observatory.org</p> <p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory (CTAO)</a> will be the next-generation gamma-ray observatory and is currently under construction on the island of La Palma (Spain) and near Paranal (Chile). <br>This repository provides access to reconstructed event information (DL2- and DL1-level, simulation parameters) from Monte Carlo simulations of the CTAO Northern Array (production 5).</p> <p>The Monte Carlo simulations for prod5 are described in <a href="https://arxiv.org/abs/2108.04512">arXiv:2108.04512</a>, the simulation telescopes models used in the telescope simulation program <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/">sim_telarray</a> and the configuration used in the air-shower code <a href="https://www.iap.kit.edu/corsika/index.php">CORSIKA</a> are available from the Zenodo archive for <a href="../record/6218687">CTA Prod5 Telescope Models</a>.</p> <p>MC events are calibrated and reconstructed using the <a href="https://ctapipe.readthedocs.io/">ctapipe</a> package and stored for the following data levels:</p> <ul> <li> R1-MC: simulated raw data (output from sim_telarray)</li> <li> <strong>DL1 (this repository)</strong>: telescope level data including images and image parameters</li> <li> <strong>DL2 (this repository)</strong>: reconstructed event parameters such as energy, <strong>direction</strong>, gamma/hadron discrimination parameters. Note that this repository only contains the direction information.</li> <li>DL3: selected events with associated instrument response functions (IRFs). Preliminary DL3-IRFs are available from [here](https://doi.org/10.5281/zenodo.5499839).</li> </ul> <p>For a description of the file format and data model, <a href="https://ctapipe.readthedocs.io/en/latest/user-guide/data_models/index.html">see ctapipe Data Model</a>.</p> <p>Data set description:</p> <ul> <li>using ctapipe version 0.17 (<a href="https://github.com/cta-observatory/ctapipe/releases/tag/v0.17.0">GitHub release page</a>, <a href="../record/7118605">Zenodo page</a>)</li> <li>CTAO Northern Array on La Palma for the Alpha configuration (4 large-sized telescopes, 9 mid-sized telescopes)</li> <li>Zenith angles of 20 deg, telescope pointing direction north and south</li> <li>Primary particles: photons, protons</li> <li>Contained information: DL1 image and parameter data, DL2 geometry, simulation truths</li> <li>For convenience, the same data is provided in a single, large file per particle type for the whole dataset which do not contain the low-level DL1 image information and a larger number of files including this information.</li> </ul> <p>We explicitly note that the products provided are preliminary and do not reflect the final performance of the CTA Observatory, neither are data structure or formats finalized. We also note that these data products are different to those used for the <a href="../record/5499840">CTAO Instrument Response Functions</a>.<br>In cases in which the data provided in this repository are used in a research project, we ask that the following acknowledgment is added in any resulting publication:</p> <p>"This research has made use of the CTA DL1 and DL2 Event lists provided by the CTA Observatory and Consortium (version prod5-DL2-release1-DL2)" and cite this repository in the reference section of your publication.</p> <p>We would like to thank the computing centers that provided resources for the generation of the Prod5 simulation set,<a href="../record/5499840"> click here for a list of service providers</a>.</p>
Simulation output for Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study
<p>This repository contains the (compressed) simulation output from <em>Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study</em>. On a Linux-based system, extract the contents with:</p> <pre><code class="language-bash">tar -xvzf raw_output_compressed.tar.gz </code></pre> <p>Please refer to the published article (https://doi.org/10.3389/fpsyg.2023.1253452) and the associated GitHub (<a href="https://github.com/carldelfin/stability-of-bivariate-correlations">github.com/carldelfin/stability-of-bivariate-correlations</a>) for additional information.</p>
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
Fracture data from Monte Carlo simulation and practical engineering cases
<p>This data is mainly used for inferring each fracture size and spatial pattern from its trace exposed on the rock mass outcrop.</p>
Monte-Carlo-simulated MR spectra of the rat brain and their quantification results obtained with QUEST and QUEST-MM jMRUI algorithms
<p>MC-simulated spectra of the rat brain along with the corresponding basis set (metabolite signals simulated using NMRScopeB from jMRUI) and the QUEST-MM, QUEST, QUEST(Met+Back) and QUEST(Met+MM) quantification results are stored in the MC_results folder.</p> <p>Results of an in-vivo rat brain MRS signal (SPECIAL, dead time t0 = 0.134 ms, TE = 2.8 ms, at 9.4 T) quantification performed with the QUEST-MM jMRUI algorithm (origin for MC-simulation) are stored in the folder rat_results.</p> <p>All files can be loaded to jMRUI software version 5 and later.</p>
Accompanying simulated data for "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Data and Code for: Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations
<p>This repository contains the datasets and scripts used to obtain the Figures of the paper "Determination of the Signal Fluctuation Threshold of the Temperature Ion Composition Ambiguity Problem Using Monte Carlo Simulations".</p> <p>The repository is organized as follows:</p> <p>- a) ISR model script</p> <p>- b) Script to visualize the two solutions of the TICA problem</p> <p>- c) Generator of uniformly selected parameters</p> <p>- d) Script to represent the space of parameters</p> <p>- Part 0) Monte Carlo simulation codes</p> <p>- Part I) Analysis of the efffect of the uncertainty on the initial guess</p> <p>- Part II) Analysis of the impact of the addition of parameters from the Plasma Line</p> <p>- Part III) Analysis of the information provided by the addition of each parameter</p> <p>- Part IV) Analysis of the effect of the uncertainty on the parameters added</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Monte Carlo simulations of octahedral spin ice
<p>Representative datasets for the paper <a href="https://arxiv.org/abs/2203.08834">Fragmented spin ice and multi-k ordering in rare-earth antiperovskites</a>.</p> <p>The contents of each file is as follows:</p> <ul> <li><code>loop.zip</code>: Loop-update simulations of dipolar spin ice</li> <li><code>singlespin.zip</code>: Single-spin-flip simulations of dipolar spin ice</li> <li><code>transition.zip</code>: High-resolution simulations of dipolar spin ice near ordering transitions</li> <li><code>exchange.zip</code>: Loop-update simulations of near-neighbour spin ice</li> <li><code>otsuka.zip</code>: Loop-string simulation of idealised octahedral ice</li> <li><code>autocorr.zip</code>: Raw Monte Carlo samples for estimating autocorrelation times</li> <li><code>mcif.zip</code>: mcif files representing the four ordered phases</li> </ul> <p>Each zip archive contains a <code>README</code> file explaining its contents and the file formats used.</p>
Derived data supporting the analysis of surface albedo changes from Mars 2020 observations: Probabilistic distribution of the Amplitude Spectral Densities of Supercam microphone recordings and Monte-Carlo dust devil simulations.
<p>These files contain derived data used in the analysis submitted for publication in Journal of Geophysical Research: Planets, entitled "Dust Lifting Through Surface Albedo Changes at Jezero Crater, Mars" by Vicente-Retortillo et al. The article was initially submitted on November 14, 2022, and the revised version on March 1, 2023.</p> <p>Files include the derived data and information needed to generate Figures 4 (Microphone_Data.mat and Plot_ASD_from_Microphone_Data) and 6 (remaining files) of the article.</p>
Monte Carlo simulation of water cylinder phantoms for Data Driven Gating (DDG) in single photon emission tomography
<p><strong>Objective</strong>: Multiple different algorithms have been proposed for Data Driven Gating (DDG) in Single Photon Emission Computed Tomography (SPECT) and have successfully been applied to Myocardial Perfusion Imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as the limitations and pitfalls of current methods are unknown.</p> <p><strong>Approach</strong>: We create a comprehensive set of phantoms that allow the characterization of DDG algorithms and perform Monte Carlo simulations, simulating the influence of different motion artifacts, view angles, moving feature diameters, contrasts, and count levels. We derive quantitative metrics from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps (LE) methods as sample DDG algorithms.</p> <p><strong>Main results</strong>: View angle, feature size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the Signal to Noise ratio, and the noise in the data.</p> <p><strong>Significance</strong>: We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI. </p>
Dataset for "Active buckling of pressurized spherical shells : Monte Carlo simulation"
<p>We study the buckling of pressurized spherical shells by <br> Monte Carlo simulations in which the<br> detailed balance is explicitly broken -- thereby driving the shell<br> active, out of thermal equilibrium.<br> Such a shell typically has either higher (active) or<br> lower (quiescent) fluctuations<br> compared to one in thermal equilibrium depending on how the detailed balance<br> is broken.<br> We show that, for the same set of elastic parameters, a shell<br> that is not buckled in thermal equilibrium can be buckled if turned<br> active. Similarly a shell that is buckled in thermal equilibrium<br> can unbuckle if turned quiescent.<br> Based on this result, we suggest that it is possible to experimentally design<br> microscopic elastic shells whose buckling can be optically controlled.</p>
Monte Carlo simulation of water cylinder phantoms for Data Driven Gating (DDG) in single photon emission tomography
Open the record for dataset details and reuse information.
New Methods in Monte Carlo Lightning Simulations: data for inhomogeneous simulations
<p>This is the ncdf file output from the WRF supercell simulation. This file is produced using the WRF version 3.9.1.1 with the storm electrification package. This data is used in the article "New Methods in Monte Carlo Lightning Simulations" to create an inhomogeneous microphysical profile for simulations.</p>
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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.