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.
- Accelerator Science and Technology Centre
- Central Laser Facility
- The Hartree Centre
- ISIS Neutron and Muon Source
- National Quantum Computing Centre
- Particle Physics Department
- RAL Space
- Scientific Computing Department
- Technology Department
- United Kingdom Astronomy Technology Centre
Recent Submissions
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
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 .
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.
Dataset supporting the publication: "Computational and spectroscopic characterisation of thianthrene" in Royal Society Open Science 2024.
(2024-02-18) Parker, Stewart; Rushworth, Rachel; Sarter, Mona; Pascariu, Matei
This dataset supports the publication: Computational and spectroscopic characterisation of thianthrene, (Rachel H. Rushworth, Matei Pascariu, Mona Sarter and Stewart F. Parker, Royal Society Open Science (2024)).
The dataset consists of a README file (README_Parker_Thianthrene_dataset.txt), two zip files: "A-Experimental_spectra", "B-CASTEP". and an image file: Thianthrene_Mode_at_862cm.jpg.
The README describes the contents of the archive.
A-Experimental_spectra.zip contains the DSC data and the infrared, Raman and inelastic neutron scattering spectra.
B-CASTEP.zip contains the input and output files for the calculation of the complete unit cell and the C2v symmetry isolated molecule.
Data associated with manuscript "Demonstration of stable, long-term operation of a nanosecond pulsed DPSSL operating at 10 J, 100 Hz"
(2025-01-31) De Vido, Mariastefania; Quinn, Gary; Clarke, Danielle; Luke, McHugh
Data on the characterisation of DiPOLE 10 J, 100 Hz laser system.
V2_3D dataset of the X-ray Computed Tomography and plugin graph results
(2023) Chahid, Younes; Packer, Chris; Tawfik, Ahmed; Keen, Joel; Brewster, Nick; Beardsley, Mat; Morris, Katherine; Bills, Paul; Blunt, Liam; Atkins, Carolyn; Tammas-Williams, Samuel
V2: This dataset is an updated version of the previous Part 2/2 (http://dx.doi.org/10.5286/edata/901). This version includes updated Part 2.c graph outputs.
Part 1/2: The first half of the dataset is composed of the Avizo plugin, which can be found in the DOI http://dx.doi.org/10.5286/edata/900
Part 2/2: This second half of the dataset is composed of the X-ray computed tomography data used in the study and their outputs. 2.a is composed of the XCT region of interest (ROI) showing the individual micro drilled holes, previously measured in the SEM. 2.b contains the isolated cube ROI used in the porosity analysis. 2.c contains the plugin graph outputs for each hole, when using the Avizo plugin.
The XCT data in 2.a and 2.b is a subvolume of the full scan of an additive manufactured flexure containing a reference pin with micro drilled holes, measured in the scanning electron microscope (SEM). The scan settings included a voltage of 145 kV, filament current of 41 µA, number of projections of 1650 and a voxel size of 8.5 µm × 8.5 µm × 8.5 µm. After reconstruction, the scan was converted from 32 bit to 8-bit, aligned to the CAD and resampled, resulting in a voxel size change to 9.4 µm × 9.4 µm × 8.8 µm. Detailed use of the XCT scan dataset and resulting plugin graphs can be found in the study titled "Development of a modular system to provide confidence in porosity analysis of additively manufactured components using X-ray computed tomography".
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
Long-term Continuous Monitoring of Methane Emissions at an Oil and Gas Facility using a Multi-open-path Laser Dispersion Spectrometer
(2023-12-01) Weidmann, Damien
A method for methane emissions monitoring at industrial facility level was developed based on a high precision multi-open-path laser dispersion spectrometer.
This dataset contains the methane path-averaged concentrations spatially distributed over the facility under study, together with local meteorology data.
The dataset contains 3 months of continuous (24h/7d) measurements at an operational gas processing and distribution facility.
Files are in the NetCDF format. Each files contains the same 8 variables:
- TIME_STAMP_DAYS - The time stamp for each measurement expressed in Julian Days.
- CH4_CONC_PPM - The path averaged methane concentration in ppm.
- CH4_CONC_ERROR_PPM - The measurement random error on the path integrated methane concentration in ppm.
- PRESS - The local atmospheric pressure in Torr.
- TEMP - The local atmospheric temperature in K.
- WIND_DIR_DEG - The local wind direction in degrees from true North.
- WIND_MAG_M_S - The local wind velocity magnitude in m/s.
- RETRO_ID - ID integer identifying the retro-reflector used for a particular open-path.
Datasets and figures exploring x-ray imaging performance analytically for manuscript titled: "X-ray detector requirements for laser-plasma accelerators"
(2023-12) Armstrong, C. D
Data used in each figure for the manuscript titled: "X-ray detector requirements for laser-plasma accelerators". Figure files are each self contained .py scripts requiring Python version 3.8.5+, and standard scientific libraries Numpy, Matplotlib and Scipy to execute the scripts. Relevant cross-section/spectral data is included for persistent plotting as well as analytical functions described in the manuscript.
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.