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.

Abstract Cascade is a full hadron level Monte Carlo event generator for ep, γp and p p ̄ processes, which uses the CCFM evolution equation for the initial state cascade in a backward evolution approach supplemented with off-shell matrix elements for the hard scattering. A detailed program description is given, with emphasis on parameters the user wants to change and common block variables which completely specify the generated ... Title of program: CASCADE 1.00/01 Catalogue Id: ADPK_v1_0 Nature of problem High-energy collisions of particles at moderate values of x are well described by resummations of leading logarithms of transverse momenta (alphas ln Q^2)^n, generally referred to as DGLAP physics. At small x leading-logs of longitudinal momenta, (alphas ln x)^n, are expected to become equally if not more important (BFKL). An appropriate description valid for both small and moderate x is given by the CCFM evolution equation, resulting in an unintegrated gluon density A(x,kt,,qbar), which is also ... Versions of this program held in the CPC repository in Mendeley Data ADPK_v1_0; CASCADE 1.00/01; 10.1016/S0010-4655(01)00438-6 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Abstract A general-purpose, self-adapting Monte Carlo (MC) event generator (simulator) Foam is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain into small cells. The cells can be n-dimensional simplices, hyperrectangles or a Cartesian product of them. The grid of cells, called “foam”, is produced in the process of the binary split of the cells. The choice of the next cell to be divided and the po...

Title of program: Foam++, version 2.05 Catalogue Id: ADMC_v3_0 [ADRG]

Nature of problem Monte Carlo simulation or generation of unweighted (weight equal 1) events is a standard problem in many areas of research. It is highly desirable to have in the program library a general-purpose numerical tool (program) with a MC generation algorithm featuring built-in capability of adjusting automatically the generation procedure to an arbitrary pattern of singularities in the probability distribution. Our primary goal is simulation of the differential distribution in the multiparticle Lorentz ...

Versions of this program held in the CPC repository in Mendeley Data admc_v1_0.tar; Foam, version 1.01; 10.1016/S0010-4655(00)00047-3 admc_v2_0.tar; FoamF77, version 2.05; 10.1016/S0010-4655(02)00755-5 admc_v3_0.tar; Foam++, version 2.05; 10.1016/S0010-4655(02)00755-5

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

This article describes a new algorithm for generating correlation matrices with specified eigenvalues. The algorithm uses the method of alternating projections (MAP) that was first described by Neumann. The MAP algorithm for generating correlation matrices is both easy to understand and to program in higher-level computer languages, making this method accessible to applied researchers with no formal training in advanced mathematics. Simulations indicate that the new algorithm has excellent convergence properties. Correlation matrices with specified eigenvalues can be profitably used in Monte Carlo research in statistics, psychometrics, computer science, and related disciplines. To encourage such use, R code (R Core Team) for implementing the algorithm is provided in the supplementary material.

Researchers simulating covariance structure models sometimes add model error to their data to produce model misfit. Presently, the most popular methods for generating error-perturbed data are those by Tucker, Koopman, and Linn (TKL), Cudeck and Browne (CB), and Wu and Browne (WB). Although all of these methods include parameters that control the degree of model misfit, none can generate data that reproduce multiple fit indices. To address this issue, we describe a multiple-target TKL method that can generate error-perturbed data that will reproduce target RMSEA and CFI values either individually or together. To evaluate this method, we simulated error-perturbed correlation matrices for an array of factor analysis models using the multiple-target TKL method, the CB method, and the WB method. Our results indicated that the multiple-target TKL method produced solutions with RMSEA and CFI values that were closer to their target values than those of the alternative methods. Thus, the multiple-target TKL method should be a useful tool for researchers who wish to generate error-perturbed correlation matrices with a known degree of model error. All functions that are described in this work are available in the fungible R library. Additional materials (e.g., R code, supplemental results) are available at https://osf.io/vxr8d/.

Model complexity.

This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the methodology presented in the article titled "Statistical Mirroring-Based Ordinalysis: A Sensitive, Robust, Efficient, and Ordinality-Preserving Descriptive Method for Analyzing Ordinal Assessment Data," authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing.

The validation process of ordinal data analysis methods (estimators) has the following specifications: 

• Simulation process: Monte Carlo simulation. • Data generation distributions: categorical, normal, and multivariate model distributions. • Data analysis: - Classical estimators: sum, average, and median ordinal score. - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation.
• Evaluation metrics: - Overall estimates average. - Overall estimates median. - Efficiency (by statistical absolute meanic deviation method). - Sensitivity (by entropy method). - Normality, Mann-Whitney-U test, and others.

Abstract A Monte Carlo generator of the final state of hadrons emitted from an ultrarelativistic nuclear collision is introduced. An important feature of the generator is a possible fragmentation of the fireball and emission of the hadrons from fragments. Phase space distribution of the fragments is based on the blast wave model extended to azimuthally non-symmetric fireballs. Parameters of the model can be tuned and this allows to generate final states from various kinds of fireballs. A facultative o...

Title of program: DRAGON Catalogue Id: AEDK_v1_0

Nature of problem Deconfined matter produced in ultrarelativistic nuclear collisions expands and cools down and eventually returns into the confined phase. If the expansion is fast, the fireball could fragment either due to spinodal decomposition or due to suddenly arising bulk viscous force. Particle abundances are reasonably well described with just a few parameters within the statistical approach. Momentum spectra integrated over many events can be interpreted as produced from an expanding and locally thermali ...

Versions of this program held in the CPC repository in Mendeley Data AEDK_v1_0; DRAGON; 10.1016/j.cpc.2009.02.019

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Comprehensive dataset of casi 81,906 Monte Carlo-simulated X-ray diffraction patterns from the cellulose 200 lattice. The dataset was generated through Monte Carlo simulation based on established X-ray diffraction physics, incorporating cell wall geometries typical of wood anatomy - from circular fibers to polygonal tracheids - and accounting for the full range of crystallographic and anatomical parameters that influence diffraction patterns. Each simulated pattern required multiple nested Monte Carlo iterations (approximately 10 million per pattern), making the generation of such a dataset computationally intensive and time-consuming.

This repository includes detailed results from Monte Carlo simulations carried out as part of the final year project entitled "Generation Adequacy in a Highly Renewable Power System". Each file contains a "Case" column which describes the scenario investigated, indicating the variation in each scenario relative to the reference case (REF).

Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a "bootstrapping uncertainty structure selection" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each .db file contains 100 DFT calculations, which were generated using iteration cycles. Each iteration cycle is referred to as a generation (marked with gen key in the database) and each database contains 10 generations where each generation consists of 10 training structures. See more details in the paper.

Monte Carlo estimates of Lyapunov susceptibilities.

Simulated dataset TTToHadronic_TuneCP5_13TeV-powheg-pythia8 in GEN-SIM-DIGI-RAW format (see CMS Monte Carlo production overview) for 2018 collision data. This dataset is used as the input in ML studies.

See the description of the simulated dataset names in: About CMS simulated dataset names.

This simulated dataset corresponds to the collision data that was collected by the CMS experiment in 2018 and it was released in the context of data science sample production.

Monte Carlo estimates of correlation lengths.

Simulated dataset MinBias_TuneCUETP8M1_13TeV-pythia8 in GEN-SIM format (see CMS Monte Carlo production overview) for 2015 collision data. Events are sampled from this dataset and added to simulated data to make them comparable with the 2015 collision data, see the guide to pile-up simulation.

See the description of the simulated dataset names in: About CMS simulated dataset names.

Monte-Carlo and cycle-expansion estimates of free-energy densities.

Monte-Carlo and cycle-expansion estimates of skewness for a number of neighbors N_n=2.

PRM Calibration Dataset for LLM Reasoning Reliability

This dataset provides a calibration dataset with success probabilities of LLMs on mathematical reasoning benchmarks. Each example includes a question, a reasoning prefix, and the estimated probability that the model will produce a correct final answer, conditioned on the prefix. Success probabilities are estimated via Monte Carlo sampling (n=8) using LLM generations with temperature 0.7.

  📂 Available Datasets… See the full description on the dataset page: https://huggingface.co/datasets/young-j-park/prm_calibration.

Core HTTK data sets – includes chemical specific tables with physico-chemical properties, physiological data, and data generation meta-data.

Inclusive gamma at NLO QCD for pT(gamma) above 200 GeV. Samples are generated for different pT ranges. MSTW2008 NLO +41 sets.