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127 results for “monte carlo”
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>
A 3D kinetic Monte Carlo study of streamer discharges in CO2
<p>The dataset contains compilable files and input files for the paper "A 3D kinetic Monte Carlo study of streamer discharges in CO2".</p>
Dataset and figure generator for Variational Monte Carlo approach applied to the model describing WSe2 homo-bilayer
<p>This data set contains post-processed data obtained from variational Monte-Carlo approach for Hubbard model with complex, spin and direction dependent phase. This model is believed to properly describe the eseential features of WSe2 twisted homo-bilayer. The python notebook included, allows to generate figures regsarding formation of Mott insulating phase and spin ordering.</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>
Multi-Level Monte Carlo Models for Flood Inundation Uncertainty Quantification - Dataset
<p>Dataset used for the analysis of Multi-level Monte Carlo methods for flood inundation uncertainty quantification. This includes:</p> <ol> <li>Flood model simulations for Dyce, Glasgow and Inverurie across three resolutions (5m/10m/20m).</li> <li>Data for violin plot figures with code.</li> </ol>
Thermodynamics of the metal-insulator transition in the extended Hubbard model from determinantal quantum Monte Carlo
<p>This zip archive contains the DQMC data sets for the \beta=1/T resolved double<br> occupancy, internal energy per site, antiferromagnetic structure factor and<br> charge density wave structure factor obtained with the ALF Code.<br> The computations have been carried out on a square lattice at half filling for<br> the Hubbard model, the U-V model and the long-range Coulomb(LRC)-Hubbard model.<br> Every model is computed at fixed U/t=1.9.</p> <p>The naming convention of the csv-files is the following:<br> V0.0 = Hubbard model<br> V0.x = U-V model with V/t=0.x<br> Vcx.x = LRC-Hubbard model with V_C/t=x.x<br> <br> - dat_docc_{\Delta\tau}_V{x}.csv = double occupancy at \Delta\tau={0.05, 0.1, 0.2}<br> - dat_docc_{\Delta\tau}_V{x}_err.csv = corresponding statistical error<br> - dat_energy_0.1_V{x}.csv = internal energy at \Delta\tau=0.1<br> - dat_energy_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_saf_0.1_V{x}.csv = antiferromagnetic structure factor at \Delta\tau=0.1<br> - dat_saf_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_scdw_0.1_V{x}.csv = charge density wave structure factor at \Delta\tau=0.1<br> - dat_scdw_0.1_V{x}_err.csv = corresponding statistical error</p> <p>The structure of each file is to be read column-wise:<br> \beta, L=8, L=10, L=12, L=16, L=18, L=20</p> <p> </p>
Monte Carlo experiment data & IEEE 118 bus data
<p>The data files contain Monte Carlo experiment data and IEEE 118 bus data: The data generated through Monte Carlo experiments include four different spatiotemporal residual structures: no correlation, only weak spatial (cross) correlation, only temporal (auto) correlation, and weak spatial-temporal correlation; The IEEE 118 bus data contains 118 voltage observations sampled for 1000 times.</p>
DFT-based Monte Carlo Trajectories for Surface Segregation of Disordered FCC AuCuPd Alloys
<p>A zip file containing 600 subfolders named as "mc_run_$idx"". Each subfolder contains an "analysis.json" and "run_state.json" containing an analysis of the surface segregation output and describing the simulation (the composition of the bulk slab, whether the simulation encountered any errors, etc). Optionally also includes a "job.out" with job logs.</p>
DFT-based Monte Carlo Trajectories for Surface Segregation of Disordered FCC AgAuCu Alloys
<p>A zip file containing 600 subfolders named as "mc_run_$idx". Each subfolder contains an "analysis.json" and "run_state.json" containing an analysis of the surface segregation output and describing the simulation (the composition of the bulk slab, whether the simulation encountered any errors, etc). Optionally also includes a "job.out" with job logs.</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>
Properties of Carbon-Oxygen White Dwarfs From Monte Carlo Stellar Models
<p>MESA inlists and models associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2016ApJ...823...46F">Fields et al. (2016)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/0004-637X/823/1/46">10.3847/0004-637X/823/1/46</a></p>
2D Ising model Monte Carlo configurations for the paper Entropy from Machine Learning
<p>Monte Carlo configurations for the 2D Ising model on a periodic 20x20 lattice used in the paper <em>Entropy from Machine Learning.</em></p> <p>Contains:</p> <ul> <li>20000 configurations for each temperature in the range T=1.0 to T=4.0 in 0.1 intervals</li> <li>40000 configurations at T=Tc</li> <li>README.md file with information about reading into Python</li> </ul> <p>See repository <a href="https://github.com/rmldj/ml-entropy">github.com/rmldj/ml-entropy.</a></p>
SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019,
<p>SPAWN data files need for histograms of publication "Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights" by Verena A. Neufeld and Alex J. W. Thom (doi.org/10.1021/acs.jctc.8b00844), J. Chem. Theory Comput., 15,1, 127-140, 2019. This compliments the data set at doi: 10.17863/CAM.30358 (copy these SPAWN files here into relevant folder at histograms_toc_figs_1_2/H2O_3_ccpVDZ/).</p>
Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures
<p>This dataset is associated with "<a href="https://doi.org/10.1063/5.0221221">Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures</a>" by L. Galliano, R. Rende and D. Coslovich. It includes data and workflow to reproduce the figures of the manuscript.</p> <p><strong>Requirements</strong></p> <p>A working installation of Julia (tested with version 1.10.4) is the only software required for this workflow. You can install Julia by following the instructions on the <a href="https://julialang.org/downloads/">Julia website</a>.</p> <p><strong>Dataset</strong></p> <p>Some of the simulations for this project ran for more than a week on a cluster. The Trajectories folder contains results from these simulations, which are necessary to reproduce the figures in the paper. Each folder includes a summary file detailing the simulation parameters and other files containing measured quantities such as potential energy, average acceptance, and parameter values. These folders also contain results from postprocessing the original simulation trajectories carried out with <a href="https://framagit.org/atooms/postprocessing">atooms-postprocessing</a>.</p> <p><strong>Instructions</strong></p> <p>To fully reproduce the analysis from postprocessed data, use the convenience script make at the root of the data set:</p> <p><code>./make all</code></p> <p>This comprises two main steps:</p> <ul> <li><code>./make setup</code> will set up the software environment by checking the Julia installation and installing the required packages in a virtual environment.</li> <li><code>./make workflow</code> will execute the scripts to perform the workflow from the preprocessed trajectory files in the Trajectories folder.</li> </ul> <p>There is also a <code>./make clean</code> option, which uninstalls the virtual environment and removes the results of the workflow.</p> <p><strong>Notes</strong></p> <ul> <li><em>Figure 3</em>: This figure is the result of a two-dimensional simulation that requires the complete Policy-guided Monte Carlo code. Consequently, it is not reproduced by this workflow.</li> <li><em>Figure formats</em>: The Julia scripts generate figures in SVG format. After producing the figures, you can use the <code>./make pdf</code> option to convert them to PDF files. This option requires a working installation of Inkscape, which can be downloaded from <a href="https://inkscape.org">Inkscape website</a></li> </ul> <p><strong>Changelog</strong></p> <ul> <li>1.0.0: initial submission</li> </ul>
A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout
<p>This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.</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
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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.