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127 results for “monte carlo”
Monte Carlo Analysis Code and Raw Data
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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>
Data for: Thermodynamics of a dilute Bose gas: a path-integral Monte-Carlo study
<p>Path-integral Monte-Carlo results of the thermodynamics of a homogeneous dilute Bose gas. Data used to plot the figures for internal energy, pressure, isothermal compressibility and contact parameter.</p>
Author Name Disambiguation using Markov Chain Monte Carlo (MCMC)
<p>This repository contains the files processed from the <a href="https://zenodo.org/record/5675801#.Y2APcOzMJhE">Aminer-534K</a> Knowledge graph for the master's thesis on Author Name Disambiguation.</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>
QMC Raw Data for Stable Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble
<p><strong>Data Summary</strong></p> <p>Raw data of 'A Stable, Recursive Auxiliary Field Quantum Monte Carlo Algorithm in the Canonical Ensemble: Applications to Thermometry and the Hubbard Model'.</p> <p>Random seeds are generated using the default Julia RNG, with the seed number being 1234+the file ID.</p> <p>For more details, please check README.md on the GitHub repository.</p> <p> </p> <p><strong>Fidelity_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for fidelity measurements that correspond to plot_fidelity.ipynb</li> <li>The random seeds for numerator measurements ('Fidelity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>Purity_(CE|GCE)_Lx6Ly6_U(2.0|4.0).zip</strong></p> <ul> <li>Raw data for purity measurements that correspond to plot_purity.ipynb</li> <li>The random seeds for numerator measurements ('Purity(CE|GCE)_num.*') are 5678+the file ID</li> </ul> <p><strong>StructFactGCE_Lx6_Ly6_U2.0.zip</strong></p> <ul> <li>Raw data for the momentum distribution and structural factor measurements that correspond to plot_nk.ipynb and plot_Cq.ipynb</li> </ul>
Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"
<p>Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"</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>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
The average transmitted wave in random particulate materials: Monte-Carlo data
<p><em>A Julia repo designed to replicate and validate wave scattering from a plate filled with randomly distributed particles. The code allows you to explore the effects of different parameters such as scatterer type, volume fraction, and frequency on wave scattering behavior.</em></p> <ol> <li> <p>Make sure you have Julia v1.6.7 or later, installed on your system.</p> </li> <li> <p>Open your preferred Julia environment (REPL or Jupyter Notebook).</p> </li> <li> <p>Navigate to the downloaded code directory within Julia.</p> </li> <li> <p>Load the necessary packages included in loaddata.jl.</p> </li> <li> <p>To load the data, modify the code's parameters using the provided flags:</p> </li> </ol> <p> </p> <ul> <li><code><code>is_sound_soft</code><span>: Set to </span><code>true</code><span> for sound-soft scatterers, and </span><code>false</code><span> for hard scatterers.</span></code></li> <li><code><code>is_two_wavenumbers</code><span>: Set to </span><code>true</code><span> for two wavenumbers, and </span><code>false</code><span> for three wavenumbers.</span></code></li> <li><code><code>is_low_freq</code>: Set to <code>true</code> for low frequency ka = 0.04, and <code>false</code> for frequenc ka = 0.36 or ka = 0.62.</code></li> <li><code><code>is_low_volfrac</code>: Set to <code>true</code> for 5% volume fraction, and <code>false</code> for 25% volume fraction.</code></li> </ul> <p> </p> <p>Run the code. The simulation will load the appropriate dataset based on your selected parameters and display the results.</p>
Monte Carlo based dataset to train a deep learning based partial volume correction for 177Lu-SPECT imaging
<p>This dataset contains pairs of random activity distribution and Monte Carlo simulated SPECT reconstructions used for the training of a deep learning based partial volume correction method. More detailed information on the creation of the data set, the partial volume correction and the use of the data can be found in the publication listed below and in the readme file.</p> <p> </p> <p>Publication: Will be updated, when published.</p>
Data from: Infectious disease dynamics inferred from genetic data via sequential Monte Carlo
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Data from: Ab initio grand canonical Monte Carlo calculation of grain boundary composition and structure
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Solar wind ENA precipitation in the Martian atmosphere: Monte Carlo simulation results
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Data for "A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the UniformElectron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study"
<p>Phaseless AFQMC data (input and output) for "A Phaseless Auxiliary-Field Quantum Monte Carlo Perspective on the UniformElectron Gas at Finite Temperatures: Issues, Observations, and Benchmark Study" </p> <p> </p> <p>data: contains raw qmc data</p> <p>figures: contains analysed data + plotting scripts.</p>
Data from "Linked Coupled Cluster Monte Carlo"
<p>We consider a new formulation of the stochastic coupled cluster method in terms of the similarity transformed Hamiltonian. We show that improvement in the granularity with which the wavefunction is represented results in a reduction in the critical population required to correctly sample the wavefunction for a range of systems and excitation levels and hence leads to a substantial reduction in the computational cost. This development has the potential to substantially extend the range of the method, enabling it to be used to treat larger systems with excitation levels not easily accessible with conventional deterministic methods.</p>
Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo
<p>Data and Python Scripts required to produce the figures in:</p> <p>Understanding and Improving the Efficiency of Full Configuration Interaction Quantum Monte Carlo</p> <p>http://arxiv.org/abs/1601.00865</p> <p>Reanaylisis requires pyhande (part of the HANDE package) available from:</p> <p>https://github.com/hande-qmc/hande.git</p> <p>To reproduce the figures by reanalysing the data from scratch modify sys.path.append() in ./bin/Efficiency.py and in ./figure4/figure4.py. To point to hande_top_level_dir/tools.</p> <p> </p>
EOS-DATA-2016-01: Monte Carlo Pseudo Events for the Decays Bbar -> P mu X_nubar, P = D,pi
<p>This archive EOS-DATA-2016-01 contains pseudo events of the decays</p> <p> Bbar -> P mu X_nubar</p> <p>where X_nubar is either nubar or (nubar nu nubar), and P = D or pi. The archive<br /> complements the publication EOS-2016-01. The events are stored as binary files<br /> in the HDF5 format.</p>
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>
Data for Unbiasing fermionic quantum Monte Carlo with a quantum computer
<p>Wavefunctions and Hamiltonians from "Unbiasing fermionic quantum Monte Carlo with a quantum computer"</p>
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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)
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.