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49 results for “Monte Carlo simulation”
Python code for Monte Carlo Simulation of dynein stepping
<p>This project is described in: Three-color single-molecule imaging reveals conformational dynamics of dynein undergoing motility (2020) and can be found on BioRxiv: (link to follow)</p> <p>Here, we provide the custom python code that is used simulate the stepping of dynein based on experimental data. Details on how the code works are given in the script itself. Moreover, we provided a pdf, which shows plots of all the input data.</p> <p>There are three different python scripts:</p> <ul> <li>Monte-Carlo-simulation_Dynein-stepping.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_flexible-ring-position.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_fixed-ring-angle.py</li> </ul> <p>And six folders with different experimental input data:</p> <ol> <li>Experimental-data-to-run-simulation_MT</li> <li>Experimental-data-to-run-simulation_MT_MTBD-distance-independent-angle</li> <li>Experimental-data-to-run-simulation_MT_no-forward-bias</li> <li>Experimental-data-to-run-simulation_MT_no-leading-trailing</li> <li>Experimental-data-to-run-simulation_MT_no-left-right</li> <li>Experimental-data-to-run-simulation_MT_no-stepping-bias</li> </ol> <p>The first python script can be used to generate the stepping movies (Supplementary Movies 3-10). The second python script is used to generate stepping traces and all other plots. The last python script is a special version of number one and two as it simulates the stepping of dynein for a fixed stalk-microtubule angle. It can generate stepping movies as well as stepping traces and all other plots.</p> <p>In order to simulate stepping of dynein for a wild-type condition, the first experimental dataset should be used as input (this dataset is also used for the fixed angle simulation). The other five datasets are used to simulate stepping of dynein when specific rules are ignored:</p> <ul> <li>for an on-axis distance-dependent bias to take more forward than backward steps (dataset #3),</li> <li>a distance-dependent bias to close the gap between the motor domains along the on- and off-axis when taking a step (dataset #4 and 5, respectively), </li> <li>a higher probability for the trailing domain instead of the leading domain to take the next step (dataset #6), and </li> <li>the relative movement between AAA ring and MTBD (for fixed angle see comments above and for MTBD on-axis distance independent angle changes dataset #2)</li> </ul>
Dataset for "Verifying Monte Carlo simulations of diffusion tensor cardiovascular magnetic resonance using a finite volume method"
<p>This dataset contains the results of random walk and finite volume simulations of diffusion in cardiac tissue. The data was used for the work presented at the 8th World Congress of Biomechanics in 2018.</p>
Data for 'Simulating Photodynamic Therapy for the Treatment of Glioblastoma using Monte Carlo Radiative Transport'
<p>Files added include the data used to make each data plot within the paper.</p> <p>Figure 5:</p> <p>pen_depth_plot.py</p> <p>pen_depth_plot_y.py</p> <p>pen_depth_plot_z.py</p> <p>jmean_run2NB.dat</p> <p>o21_full_run2NB.dat</p> <p>rhokap_run2NB.dat</p> <p>tumour.dat</p> <p> </p> <p>Figure 6:</p> <p>tumour_percentage.py</p> <p>tumkill_full_994.dat</p> <p>tumkill_full_1.dat</p> <p> </p> <p>Figure 7:</p> <p>temp_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 8:</p> <p>power_run2NB.dat</p> <p>s0_slice_run2NB.dat</p> <p>o23_slice_run2NB.dat</p> <p>o21_slice_run2NB.dat</p> <p>temp_slice_run2NB.dat</p> <p> </p> <p>Figure 9:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run11NB.dat</p> <p>percent_left_run12NB.dat</p> <p>percent_left_run13NB.dat</p> <p> </p> <p>Figure 10:</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run3NB.dat</p> <p>percent_left_run4NB.dat</p> <p> </p> <p>Figure 11:</p> <p>max_temp_time_run1NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p> </p> <p>Figure 12:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run10NB.dat</p> <p>max_temp_time_run10NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 13:</p> <p>percent_left_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run9NB.dat</p> <p>max_temp_time_run9NB.dat</p> <p> </p> <p>Figure 14:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run14NB.dat</p> <p>percent_left_run15NB.dat</p> <p> </p> <p>Figure 15:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run56NB.dat</p> <p>percent_left_run62NB.dat</p> <p>max_temp_time_run56NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p>
Raw data for 'Analysing binding stoichiometries in NMR titration experiments using Monte Carlo simulation and resampling techniques'
<p>Repository of the raw data and SupraFit binaries (for Windows, Linux and macOS) for the research article:</p> <p><strong>Analysing binding stoichiometries in NMR titration experiments using Monte<br> Carlo simulation and resampling techniques</strong></p> <p> </p> <p>It contains the SupraFit nigthly builds 2.5.98 for Windows (<em>SupraFit-nightly-2.5.98-x86_x64-Windows.zip</em>), Linux (<em>SupraFit-nightly-2.5.98-x86_64-Linux.tar.gz</em>) and macOS (<em>SupraFit-nightly-2.5.98-macOS.dmg</em>).</p> <ul> <li> <p><em>Simulated Experiments.suprafit</em> contains the simulated experimental data</p> </li> <li> <p><em>Simulated 1_1 Experiment.suprafit</em> contains the raw data of the 1:1 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 1_1_1_2 Experiment.suprafit</em> contains the raw data of the 1:1/1:2 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 2_1_1_1 Experiment.suprafit </em>contains the raw data of the 2:1/1:1 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>Simulated 2_1_1_1_1_2 Experiment.suprafit </em>contains the raw data of the 2:1/1:1/1:2 data set after several statistical post-processing analyses were performed</p> </li> <li> <p><em>MC-simulation 1_1_1_2 Experiment.suprafit </em>contains the raw data for Figure 4 in the article</p> </li> </ul>
Dataset for I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021)
<p>This dataset contains data associated to plots published in the paper I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021), https://doi.org/10.1103/PhysRevE.103.L061303</p>
Monte Carlo ray tracing code and simulations for photons scattered by an optically thin slab
<p>This directory contains code and data used to simulate photon path length distributions in an optically thin slab.<br> This material was used to prepare the manuscript "Photon Path Distributions in Optically Thin Slabs" by Quentin Libois and Anthony B. Davis, submitted to Optics Express</p>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
Lattice kinetic Monte Carlo model to simulate RNA polymerase II clusters
<p>This data set includes Python scripts (numerical simulation and analysis) and already generated simulation data for RNA polymerase II clusters. RNA polymerase II particles as single lattice sites and chromatin with regulatory region as connected polymer.</p>
Raw data of Monte Carlo (MC) simulations of the transfer Cr flux during abyssal peridotites marine alteration
<p>Raw data of Monte Carlo (MC) simulations were performed, and this MC model is used to quantitatively assess the net transfer flux of Cr between abyssal peridotites and seawater. Given that there are large uncertainties in the parameters. Two thousand MC model runs were performed using randomly selected values of model variables.</p>
Lattice kinetic Monte Carlo model to simulate RNA polymerase II clusters during stem cell differentiation
<p>This data set includes Python scripts (numerical simulation and analysis) and already generated simulation data for RNA polymerase II clusters during stem cell differentiation. It includes the whole data to recreate panels.</p>
Solar wind ENA precipitation in the Martian atmosphere: Monte Carlo simulation results
Open the record for dataset details and reuse information.
data and code for Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing
<p>Data and radiative transfer code used for the manuscript "Machine Learning-based Denoising of Surface Solar Irradiance simulated with Monte Carlo Ray Tracing". See Readme for details</p>
Inelastic scattering of electrons in water from first principles: Cross sections and inelastic mean free path for use in Monte Carlo track-structure simulations of biological damage
<p>Modeling the inelastic scattering of electrons in water is fundamental, given their crucial role in biological damage. In Monte Carlo track-structure (MC-TS) codes used to assess biological damage, the energy loss function (ELF), from which cross sections are extracted, is derived from different semi-empirical optical models. Only recently have first <em>ab initio</em> results for the ELF and cross sections in water become available. For benchmarking purposes, in this work, we present <em>ab initio</em> linear-response time-dependent density functional theory calculations of the ELF of liquid water. We calculated the inelastic scattering cross sections, inelastic mean free paths, and electronic stopping power and compared our results with recent calculations and experimental data showing a good agreement. In addition, we provide an in-depth analysis of the contributions of different molecular orbitals, species, and orbital angular momenta to the total ELF. Moreover, we present single-differential cross sections computed for each molecular orbital channel, which should prove useful for MC-TS simulations.</p>
Monte Carlo simulation of the 2018 World Cup
<p>Generated using </p> <pre> </pre> <p><a href="https://doi.org/10.5281/zenodo.1284221">https://doi.org/10.5281/zenodo.1284221</a></p> <p> </p> <p> </p>
Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in a shop/bar
<p>Data from new Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in shop and bar environments. For background and previous results see V. Vuorinen et al. Safety Science 130 (2020): 104866.</p> <p>The files are collected to a tar file that includes all data files used for analysis (binary files with NumPy data type .npy) and a readme.</p>
Neutronic analysis of the fusion reactor ARC: Monte Carlo simulations with the Serpent code
<p>The input files of the spatially-uniform and non-uniform neutron sources used for the Monte Carlo simulations with the Serpent code and the CAD files for the geometry definition of the fusion reactor ARC used in the framework of the work published in the paper "Neutronic analysis of the fusion reactor ARC: Monte Carlo simulations with the Serpent code" (in Fusion Science and Technology). In the repository there are also the Python files used for the post processing of the results and the output files of Serpent with the main results of the neutronic simulations.</p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Datasets for numerical Monte Carlo simulations of Diclofenac bio-degradation in a soil-water system
<p>This record contains files and main instructions to repeat the numerical simulations of Diclofenac bio-degradation is a soil-water system. The text file Readme.docx contains a description of the overall content of this record, which is organized in two separate folders.</p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
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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)
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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.