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127 results for “monte carlo”
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>
Quasi-two-dimensional ferromagnetism in the triangular magnet EuAl12O19: Monte Carlo Calculations
<p>This repository contains all classical Monte Carlo calculations relevant for EuAl12O19 material. All calculations were conducted using the UppASD code.</p> <p>Contents:</p> <ul> <li>in the root directory - all plots published in the Calculations section of the paper, together with underlying data</li> <li><code>ab_plane_nodip</code> - a directory containing calculations for <em>M || H || [100]</em> and <em>M || H || [210]</em>, showing that the orientation of spins within the ab-plane does not have an effect on the magnetization curves. Calculated without dipolar interaction</li> <li><code>dm_effect</code> - a directory containing magnetization curve calculations for a) no Dzyaloshinskii-Moriya interaction (<code>dm_effect/0</code>), b) strong Dzyaloshinskii-Moriya interaction of <em>D = J/2</em> (<code>dm_effect/0.00125</code>)</li> <li><code>fit_nodip</code> - a directory with calculations related to J (exchange interaction energy), K (single-ion anisotropy energy) fitting -- <strong>without</strong> dipolar interaction</li> <li><code>fit_dip</code> - a directory with calculations related to J, K fitting -- <strong>with</strong> dipolar interaction, <strong>source of data for plots in the paper</strong></li> </ul> <p><strong>Note</strong>: all directories were archived to .tar.gz files to comply with the file number limit.</p> <h3>MvsB curves</h3> <p>Each directory contains a set of magnetization calculations for various parameters and follows the same strict directory structure: UppASD calculations necessary for the MvsB magnetization curves are found in subdirectory <code>Hsweep/$J/$K/$axis/$T/$H</code> where:</p> <ul> <li><code>$J</code> is the value of exchange interaction energy (in mRy)</li> <li><code>$K</code> is the value of single-ion anisotropy energy (in mRy)</li> <li><code>$axis</code> is the direction of both the external field and the magnetization measurement (either <code>100</code>, <code>210</code> or <code>001</code>)</li> <li><code>$T</code> is the temperature (in Kelvins)</li> <li><code>$H</code> is the strength of the external field (in Teslas)</li> </ul> <p>Each UppASD calculation contains a set of input files (<code>inpsd.dat</code>, <code>posfile</code>, <code>momfile</code>, <code>jfile</code>, <code>kfile</code>) and output files (<code>*.out</code>).</p> <p>Furthermore, files <code>Hsweep/$J/$K/$axis/$T/MvsB.dat</code> contain the data for specific MvsB magnetization curve. Files <code>Hsweep/$J/$K/MvsB-[$axis].png</code> contain plots of MvsB magnetization curves for a specific axis and all available temperatures.</p> <h3>MvsT curves</h3> <p>A similar directory structure exists for calculations necessary for the MvsT curves - <code>Tsweep/$J/$K/$axis/$T</code> where the parameters have the same meaning as in the Hsweep directory.</p> <p>Files <code>Tsweep/$J/$K/$axis/MvsT.dat</code> contain the data for a specific MvsT magnetization curve. Files <code>Tsweep/$J/$K/MvsT.png</code> contain plots of MvsT curves for all available axes.</p> <p><strong>H = 0.1 T for all MvsT measurements</strong>.</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>
Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services
<p>This is the original dataset, where white spaces are used to separate words of all languages. This is however not the best method of analyzing some Asian languages. Please refer to the following new version for enhanced, character-based analysis results:</p> <p>Zhi Quan Zhou. (2018). Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services (Version 2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1230139</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>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the half filled square lattice, on a (U,B)-grid
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>The simulations are done at half filling on a square lattice, with the following parameters:</p> <ul> <li>Lattice sizes: 4x4, 6x6, 8x8, 10x10, 12x12, periodic boundary conditions</li> <li>Trotter discretizations: 0.1 and 0.2</li> <li>Inverse temperature beta = 10.0</li> <li>48 values for the on-site interaction U from 0.0 to 10.0</li> <li>48 values for the magnetic field (in z-direction) B from 0.0 to 4.0</li> <li>10000 warmup sweeps, 30000 measurement sweeps</li> </ul> <p>The following data from equal time measurements are available:</p> <ul> <li>Charge-Charge Correlation (next neighbors)</li> <li>Greens Function (n.n.)</li> <li>Magnetization</li> <li>Double Occupancy</li> <li>Kinetic Energy</li> <li>Total Energy</li> <li>Spin-Spin Correlation (n.n.)</li> <li>Spin-Spin Correlation (only ZZ) (n.n.)</li> <li>Ferromagnetic Structure Factor (ZZ)</li> <li>Antiferromagnetic Structure Factor (ZZ)</li> </ul> <p>The data are available as a hdf5 archive. The python script 'extract.py' illustrates the access with h5py. Relevant QUEST input parameters are provided in the group 'parameters' within the archive.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Quadratic Monte Carlo 2024
<p>We introduce five novel types of Monte Carlo (MC) moves that brings the number of moves of ensemble MC calculations from three to eight. So far such calculations have relied on affine invariant <em>stretch</em> moves that were originally introduced by Christen (2007), <em>walk</em> moves by Goodman and Weare (2010) and <em>quadratic</em> moves by <a href="https://zenodo.org/records/8038144">Militzer (2023)</a>. Ensemble MC methods have been very popular because they harness information about the fitness landscape from a population of walkers rather than relying on expert knowledge. Here we modified the affine method and employed a simplex of points to set the stretch direction. We adopt the simplex concept to quadratic moves. We also generalize quadratic moves to arbitrary order. Finally, we introduce <em>directed</em> moves that employ the values of the probability density while all other types of moves rely solely on the location of the walkers.</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>
Data for the study "Electronic excited states in deep variational Monte Carlo"
<p>This set of files contains the raw data generated by the study titled:</p> <p>'Electronic excited states in deep variational Monte Carlo'.</p> <p>The data for each results section (Sections II A - D in the main paper) is located in the respective *.tar.gz file.</p> <p> </p> <p> </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>
Data for: Quantum Monte Carlo study of the role of p-wave interactions in ultracold repulsive Fermi gases
<p>Dataset to reproduce the figures of the article</p> <p>G. Bertaina, M. G. Tarallo, and S. Pilati, <em>Quantum Monte Carlo Study of the Role of $p$-Wave Interactions in Ultracold Repulsive Fermi Gases</em>, Phys. Rev. A <strong>107</strong>, 053305 (2023).</p> <p>https://link.aps.org/doi/10.1103/PhysRevA.107.053305</p> <p>https://arxiv.org/abs/2212.09150</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>
Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments --part II
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Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments -- part I
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Monte Carlo simulation of water cylinder phantoms for Data Driven Gating (DDG) in single photon emission tomography
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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.