eData: the STFC Research Data Repository

Welcome to eData, the digital archive that collects, preserves, and makes available research data produced or collected by STFC staff.

 

Departments in eData

Select a department to browse its collections.

Recent Submissions

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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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IR and 2D-IR data from "Distinctive signatures and ultrafast dynamics of Bronsted sites, silanol nests and adsorbed water in zeolites revealed by 2D-IR spectroscopy", Chemical Science , 2025
(2025-03-15) Donaldson, Paul
Data for the 2D-IR and FT-IR spectra shown in each Figure of the manuscript "Distinctive signatures and ultrafast dynamics of Bronsted sites, silanol nests and adsorbed water in zeolites revealed by 2D-IR spectroscopy", Donaldson, Hawkins and Howe, Chemical Science, 2025 The FT-IR spectra are in .csv format The 2D-IR spectra are in tab-separated variable format Refer to the .txt 'notes' in the main folder and each sub-folder for further information
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Low energy phonons in single crystal ZrW2O8
(2025-02) Ewings, Russell
Inelastic neutron scattering data on ZrW2O8 at several temperatures, in Horace software sqw format. Also Matlab m-file that can be used with these data files to generate plots from the paper of the same name as the submission
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Data for "Heat-induced degradation of compressor performance on the Gemini laser at high repetition rate", A Sahoo et al
(2025) Symes, Daniel
Data used to create plots shown in the published figures
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SORS spectra of preservation fluids through different glass-type containers
(2025-02) Mosca, Sara; Matousek, Pavel
Spatially Offset Raman Spectra of 10 different types of preservation fluids. The Raman spectra were collected using a handheld commercial SORS instrument (Resolve, Agilent Technologies, Oxfordshire, UK) through a historic jar (labelled 'JAR') and modern glass (labelled 'vial'). The labels of the mockup solution are the following (can be found in each file): A) Glycerol 5% in water B) Glycerol 35% in water C) Glycerol 65% in water D) Industrial methylated spirits (IMS) EtOH 95% MetOH 3% in water E) EtOH 70% in water F) EtOH 70%, MetOH 5% in water G) EtOH 70%, MetOH 10% in water H) Formaldehyde 4% in water I) Formaldehyde 1% in water J) Formaldehyde 1%, EtOH 70% in water Four different datasets are provided consisting of the following: 1. SORS spectra pre-processed by external automatic routine (as described in the paper) (SORS-EXTERNAL PP-ALLTogheterdiffdaydifferentGLASS.csv) 2. SORS spectra internally pre-analysed with RESOLVE instrument (Agilent) (SORS-RESOVED PP-ALLTogheterdiffdaydifferentGLASS.csv) 3. Raw Zero spectra only (ZERO-ONLY-ALLTogheterdiffdaydifferentGLASS.cvs) 4. Raw Offset spectra only (OFFSET-ONLY-ALLTogheterdiffdaydifferentGLASS.csv)
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Experimental and Computational Investigation of the Emission and Dispersion of Fine Particulate Matter (PM2.5) During Domestic Cooking - Dataset
(2024) Jones, Harriet; Kumar, Ashish; O'Leary, Catherine; Dillon, Terry James; Rolfo, Stefano
This dataset contains the computational and experimental data for the STFC Air Quality Network project "Experimental Validation of Lagrangian Stochastic Methods Targeting Indoor Air Quality". In this project, 12 minute stir frying experiments were conducted in the DOMESTIC kitchen laboratory, and the spatial particulate matter (PM) concentrations during these experiments was measured using a range of low-cost sensors. The data from these experiments was then used as validation data for computational fluid dynamics simulations of stir frying in DOMESTIC, which were performed using the finite volume solver code saturne (version 8.0.3). Cases were run on the Scientific Computing Application Resource for Facilities (SCARF). The experimental data includes the PM, humidity and temperature readings from the sensors, in the form of csv files, as well as the data from two anemometers (also as csv files), the experimental times and dates, and the cooking protocols, and a schematic of the kitchen laboratory along with a list of sensor locations. The computational data contains the setup and outputs for all cases. Full details can be found in the README files enclosed. Licence applied to the data files: Creative Commons Attribution 4.0 International (CC-BY). Licence applied to the software files: BSD 3-Clause
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Dataset supporting the publication: "Pagodane – Solution and Solid-state Vibrational Spectra" published in Physchem (2024).
(2024) Parker, Stewart; Mason, Hannah; Wilson, Campbell; Jackson, Adam
This dataset supports the publication: Pagodane – Solution and Solid-state Vibrational Spectra, (Stewart F. Parker, Hannah E. Mason and Campbell T. Wilson, Physchem (2024)). The dataset consists of a README file (README_Parker_Pagodane_dataset.txt), two zip files: "A-Experimental_spectra", "B-CASTEP". and an image file: Pagodane.jpg. The README describes the contents of the archive. A-Experimental_spectra.zip contains the solution and solid-state infrared, Raman and inelastic neutron scattering spectra. B-CASTEP.zip contains the input and output files for the CASTEP calculation of the complete unit cell at the experimental lattice parameters, with optimised lattice parameters and the D2h symmetry isolated molecule.
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Data for 'A scintillating fiber imaging spectrometer for active characterisation of laser-driven proton beams', J. K. Patel et al., High Power Laser Science and Engineering, 2024
(2024) Patel, Jesel Kumar
This dataset contains the raw experimental data, relevant calibration data, source code, configuration files, simulation data and analysis scripts which are associated with the research published in the article 'A scintillating fiber imaging spectrometer for active characterisation of laser-driven proton beams', J. K. Patel et al., High Power Laser Science and Engineering, 2024. The data are organised into Section datasets corresponding to sections of the main article to which the contained data are associated. Each Section dataset is organised to separate raw data, calibration data, configuration files, source code, analysis scripts (and intermediate analysis files) and generate figures. Each dataset also contains a dedicated README.txt file which describes the purpose and content of each of the files in the dataset in detail. The main article and associated supplementary material contain the details of the experimental and simulation setups and should be the main point of reference. The additional files 'fibre_readout.py', 'util.py' and '__init__.py' are used by other analysis scripts in multiple datasets, so are included just once in the root directory to avoid duplication. The analysis scripts and interactive notebooks were run using Python 3.9.19 and the following additional packages are required throughout the analysis: - numpy-1.24.3 - matplotlib-3.8.0 - pandas-2.0.3 - scipy-1.9.3 - scikit-image-0.19.3 - tifffile-2023.8.30 - uproot 4.3.7
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Experimental and simulation dataset for a Multi-modal imaging detector proof of concept and associated paper
(2024-10) Armstrong, C. D
Dataset associated with the Review of Scientific Instruments article titled "Simultaneous co-axial multi-modal inspection using a laser driven x-ray and neutron source" published in 2024. Dataset includes all relevant raw data, a detailed shotsheet providing necessary metadata for the experimental work, python files to generate each figure in the manuscript, and simulation input decks used in the manuscript. The directory "./data/" contains image files (.tif) generated with a Hamamatsu Image Intensifier system and neutron time-of-flight traces (.csv) generated with a PMT and Teledyne Lecroy oscilloscope, and the shotsheet (.xlsx) with pertinent experimental information. Each figure (excluding figure 3, which is an experimental layout diagram) is generated using python (version 3.8.5) and associated standard scientific libraries (numpy, matplotlib, PIL, pandas, scipy), the files to generate each figure have been included in the DOI. Figure 6 includes simulation data generated with G4 Beamlines version 3.08, the necessary input deck is included (./simulation/TAW_bimodal_detector.g4bl) as well a directory for the neutron simulation (./simulation/3MeV_neutrons/) and x-rays (./simulation/200keV_x-rays/), within each directory is the necessary "trackFile.txt" which defines the spectral content of the each simulated beam and "totalEnergy.txt" which is the output file from the simulation. The totalEnergy.txt for each simulation is read by the fig6_generate.py and contains two columns with the detector volume (world, pixels 1-3250, etc.) and the energy deposited in each. Installation instructions for G4 Beamlines can be found at the website: https://www.muonsinc.com/Website1/G4beamline .
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Datasets and figures exploring VHEE on CLARA for manuscript titled: "Potential of CLARA Test Facility for VHEE"
(2024-09) Angal-Kalinin, Deepa; Boogert, Stewart; Jones, James
Data and scripts used for the figures and analysis performed in the paper "Potential of CLARA Test Facility for VHEE". Input file for BDSIM and the elegant tracking code are included, along with a Mathematica notebook to recreate some of the plots used in the paper.