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    Comparison of PRNGs to QRNGs using Monte Carlo Pi estimation
    (2024-09-17) Lebedev, Anton; Möslein, Annika; Ivanyshyn Yaman, Olha; Rajan, Del; Intallura, Philip
    This collection contains the source code and processed data used to investigate effects of the random number source on the outcomes of simple Monte Carlo simulations. Investigation was carried out using Quantum Dice's quantum random number generator (QRNG) and various sequential and parallel pseudo-random number generators (PRNG). PRNGs include: "minimum standard" linear congruential generator (minstd_rand0), Mersenne Twister generator (mt19937), multiplicative recursive generator (Tina's random number generator library mrg5s), "yet another random number generator" (Tina's random number generator library yarn5s) in parallel and sequential executions. The C++ code is used to generate pi estimates through sampling of [0,1]x[0,1] or using Buffon's needle experiment utilising various RNGs. Additional code is used to generate and store point distributions on [0,1]x[0,1] to asess their homogeneity and uniformity as means to explain observations made when analysing distributions of the pi data. Raw data was collected using Snakemake workflows provided in the dataset. Accumulation of raw data into provided CSV files was done using an accompanying jupyter notebook. Distributions of the estimated pi-values were analysed using a simple sign-test, t-test, distribution fitting and likelihood-based hypothesis testing incorporated in the SciPaperVisualisation notebook. Additionally correlations within the pi-datasets and effects of rounding errors were analysed and the results are included in the same notebook. Further the homogeneity of the point-distribution on the unit square obtianed with QRNGs and PRNGs was analysed to explain the differences observed in the pi-data.
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    Data Supporting - Simulating micelle self-assembly to assess potential for viscosity build in surfactant formulations
    (2023-11-18) Anderson, Richard
    .agr files corresponding to figures 4, 5, 6, 7 and 8 in the published article. These .agr files can be used to plot the observed behaviours in Xmgrace. Each .agr file contains the raw data in the plots and this can be extracted for use in other plotting algorithms. .csv file containing the data presented in table 3 in the published article.
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    Data Supporting Publication in Frontiers in Soft Matter - Simulating micelle self-assembly to assess potential for viscosity build in surfactant formulations
    (2023-11-18) Anderson, Richard; Bray, David
    Data relating to the publication of the publication entitled "Simulating micelle self-assembly to assess potential for viscosity build in surfactant formulations" Summary of data available: [1] Nagg_data_repository.zip - contains the raw data for aggregation behaviour over time. [2] .agr files corresponding to figures 4, 5, 6, 7 and 8 in the published article. These .agr files can be used to plot the observed behaviours in Xmgrace. Each .agr file contains the raw data in the plots and this can be extracted for use in other plotting algorithms. [3] Example input files for DL_MESO required to complete simulations in the article [4] shape.zip - contains the raw data obtained for average shape metrics
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    NEMOLite2D Benchmark Suite 1.0
    (2017) Ford, Rupert; Appleyard, Jeremy; Porter, Andrew; Liu, Hedong
    NEMOLite2D is a stand-alone, 2D shallow-water model based on the free-surface component of the NEMO ocean model. This benchmark suite comprises several different versions of this finite-difference Fortran code, written with the aim of exploring the effect of various optimisations on performance. Serial, OpenMP and OpenACC versions are included. The suite is provided as a gzipped tar file. See the README within it for more details.