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
- Trained machine-learning models for molecular absorption-spectrum prediction(2026-10-04) Dhanpal, Siddharth; Elliott, Peter; Trevisanutto, Paolo Emilio; Elena, Alin Marin; TEOBALDI, GilbertoThis record contains five trained machine-learning models for predicting molecular absorption spectra and associated spectrum-normalization constants from molecular atomic structure and three-dimensional ground-state electron density. The following model files are provided: cnn_model_spectra.pt — a three-dimensional convolutional neural network (CNN) for predicting normalized molecular absorption spectra from ground-state electron-density grids. cnn_model_normconst.pt — a three-dimensional CNN for predicting the spectrum-normalization constant from ground-state electron-density grids. Its matching training configuration is required to convert model outputs to the original spectrum-sum units. mace_model_spectra.pth — trained MACE model parameters for predicting normalized molecular absorption spectra from atomic species and Cartesian coordinates. schnet_model_spectra.pt — a SchNet checkpoint bundle for predicting normalized molecular absorption spectra from atomic species and Cartesian coordinates. dimenetpp_model_spectra.pt — a DimeNet++ checkpoint bundle for predicting normalized molecular absorption spectra from atomic species and Cartesian coordinates. The SchNet and DimeNet++ files contain trained weights, model identifiers, architecture settings and training configuration. These bundles allow the architectures to be reconstructed using the associated software; they require the repository code and its dependencies for inference. The models were developed using molecules from the QM7 dataset (http://quantum-machine.org/datasets/) and electronic-structure calculations of ground-state electron densities and absorption spectra. Spectrum predictions are evaluated over a 900-bin energy window from 0.0 to 0.4495 atomic units, with a spacing of 0.0005 atomic units, and are normalized to unit sum within this window. The normalization CNN predicts the sum of the corresponding 900 reference-spectrum values, enabling recovery of the calculated spectrum's intensity scale when used with a consistently normalized spectral prediction. The density-to-spectrum CNN learns a surrogate mapping from ground-state electron density to molecular spectral response. SchNet, DimeNet++ and MACE learn structure-to-spectrum mappings from atomic species and coordinates. These models support comparison of density-based and structure-based representations for molecular spectroscopy. Model definitions, preprocessing procedures, inference scripts, training and data-split documentation, normalization conventions and examples are available in the associated software repository: https://github.com/stfc-ai4s/QM7-Absorption-ML These trained models are released to support inference, reproducibility, reuse and comparison of machine-learning approaches for molecular spectroscopy within the scope of the associated study.
- Dataset supporting the publication: "Vibrational spectroscopy and computational studies of polyhalogenated ferrocenes" published in Molecules (2026).(2026) Parker, Stewart; Hughes, Keith; Butler, IanThis dataset supports the publication: Vibrational spectroscopy and computational studies of polyhalogenated ferrocenes", (Stewart F. Parker, Keith H. Hughes and Ian R. Butler, Molecules(2026)). The dataset consists of a file (README_Parker_Haloferrocenes_dataset.txt) that describes the content of the dataset, three zip files: "A-Haloferrocenes_Experimental_spectra.zip", "B-Haloferrocenes_CASTEP.zip" and "C-Haloferrocenes_AbINS.zip". A-Haloferrocenes_Experimental_spectra.zip contains the solid-state infrared, Raman and inelastic neutron scattering spectra. B-Haloferrocenes_CASTEP.zip contains the input and output files for the CASTEP calculation (geometry and vibrational transition energies) of the complete unit cell of the haloferrocenes at the experimental lattice parameters. C-Haloferrocenes_AbINS.zip contains contains the ASCII files generated by the AbINS utility from the .phonon files of the CASTEP output. and an image file: Haloferrocenes_TOC.jpg
- Input parameters, code and synthetic data for High-order spectral deconvolution of complex x-ray distributions using machine learning(2026-09) Brainthra, Anandaeaswaran; Armstrong, C. D; Scott, Graeme; Thiyagalingam, Jeyan; Rajeev, Paramel Pattathil; Villarini, BarbaraThe data associated with this paper is as follows: input parameters to generate spectra, initialise spectrometer designs and ml model, code for data generation, training and testing and trained models and generated results. Extract the las-highorder-deconvolution.tar archive. A readme file is included containing detailed information about the included files and their roles, installing the provided python libraries and usage on how to perform training and evaluation.
- Massively Parallel Workflows for Image-based Simulation using code_saturne(2026) Jones, Harriet; Moulinec, Charles; Le Houx, James; Rolfo, StefanoThis dataset contains the computational data for the STFC Ada Lovelace Centre funded project “Image-based simulation using code_saturne”. New image-based simulation workflows have been developed, using the computational fluid dynamics solver code_saturne (version 9.0.0). These workflows provide an automated end-to-end meshing and simulation process, with no user input needed after the initial configuration. Cases were run on on the Scientific Computing Application Resource for Facilities (SCARF) and on the UK’s Tier 1 Computing Facility, ARCHER2. The computational cases covered include three test geometries at varying resolutions (cube with cubic void, cube with tetromino voids, and cube with spherical voids) as well as the results for strong and weak scaling.Validation is done against DOLFINx 0.9.0. Full details can be found in the README files enclosed.
- Excitation Energy Dependence of Ultrafast Jahn–Teller Switching in Mn(III) Acetylacetonate(2026) Phelps, Ryan; Paine, Binoy; Eng, Julien; Penfold, Thomas; Johansson, Olof; Green, AliceThis dataset contains the experimental transient absorption spectroscopy (TAS) data supporting the publication "Excitation Energy Dependence of Ultrafast Jahn–Teller Switching in Mn(III) Acetylacetonate". The dataset comprises wavelength-dependent transient absorption measurements of Mn(acac)₃ in ethanol and dichloromethane following photoexcitation between 380 and 940 nm. Additional computational files used to support our interpretation is provided
- Secondary data supporting the publication - 3D-printed fused silica polished to sub-nanometre roughness for lightweight telescope mirrors(2026-08) Atkins, Carolyn; Bissell, Lawrence; Civitani, Marta M.; Vecchi, Gabriele; Lister, Gregory; Sisana, DavideThis dataset provides optical microscope images of the Lightweight Samples (52mm diameter, optically flat) detailed in the publication "3D-printed fused silica polished to sub-nanometre roughness for lightweight telescope mirrors" by Bissell, L. et al. (2026). The images support the quantification of porosity (bubbles) in the centre of the 3D printed fused silica substrates and represent a 35.2mm x 26.4mm field of view.
- Benchmarking of CFD modelling of a reactor vessel auxiliary cooling system (RVACS)(2026-07) He, Jundi; He, Shuisheng; Shaver, Dillon; Macpherson, Graham; Merzari, Elia; Wang, Wei; Liu, Bo; Cartland-Glover, Greg; Lim, Oliver; Houghton, Tim; Katsamis, Constantinos; Sigournay, Chris; Kyritsopoulos, IoannisThis dataset provides benchmark computational-fluid-dynamics (CFD) results for forced, mixed and natural convection flows in a simplified Reactor Vessel Auxiliary Cooling System (RVACS), generated for the Collaborative Computational Project in Nuclear Thermal Hydraulics (CCP-NTH). The geometry represents the RVACS as planar inlet and outlet channels separated by a baffle plate and connected at the bottom, with heated inner and cooled outer walls. Three-dimensional direct numerical simulations (DNS) were performed with the low-Mach-number spectral-element solver Nek5000 on ARCHER2 and provide the high-fidelity reference solution. 2/3-D Reynolds-averaged Navier-Stokes (RANS) simulations were contributed by participating organisations using engineering CFD approaches, including Code\_Saturne, FEAT and Ansys Fluent, with a range of turbulence models, wall treatments, mesh resolutions and steady or unsteady solution strategies. The dataset contains post-processed results from both DNS and RANS calculations for the forced convection, mixed convection and natural convection cases. It includes mean vertical velocity, mean temperature and turbulent kinetic energy profiles extracted along twelve uniformly spaced horizontal probe lines through the RVACS domain. It also includes wall shear stress and wall heat flux distributions along the hot wall, cold wall and baffle surfaces. DNS fields were averaged in time and in the transverse direction before extraction, while RANS results were extracted directly from the two-dimensional solutions; unsteady RANS data were time averaged after reaching statistically stationary behaviour. These data support comparison of turbulence modelling strategies for buoyancy-affected separated flows and passive reactor cooling applications.
- Cold atom source characterisation from a 2D Magneto-Optical Trap (MOT)(2026-07-10) Hussain, KamranMeasuring an incoming cold-atomic beam produced by a two-dimensional magneto-optical trap(MOT) using fluorescence spectroscopy and time-of-flight to probe the atomic beam characteristics. The datasets are split into two folders: raw data from the experiment and processed data used to produce the plots for publication (https://doi.org/10.48550/arXiv.2607.09604), where the experiment is described, and the data-processing methodology is detailed in Appendix B of the publication.
- CALPHAD-MolarVolume Database(2026) Gholamisheeri, MasumehCALPHAD-MolarVolume Database This repository contains parameters for analytical models of the Coefficient of Linear Thermal Expansion (CLTE) for molar volume calculations in elemental metals and binary alloys. The parameters were determined by least squares regression and Leave One Out (LOO) cross-validation. This dataset is for the publication "Molar volume prediction in unary and binary alloy metals using cross-validated regression models".
- Data supporting the publication - Laser Remelting for Reduced Porosity on Additively Manufactured Aluminium Mirrors(2026-07) Atkins, Carolyn; West, Joshua; Van Hooreweder, Brecht; Sun, Wenjuan; Tammas-Williams, Samuel; Oyarzun, Valentina; Westsik, Marcell; Chahid, Younes; Kraus, Magdalena; McPhee, Scott; Brzozowski, William; Cayzer, Nicola; Laidlaw, Fraser; Meshram, Sameer Dayanand; Ordnung, Daniel; Celik, Berk Baris; Smet, Michel; Sinico, Mirko; Harris, Michael; James, StephenThis collection supports the publication “Laser Remelting for Reduced Porosity on Additively Manufactured Aluminium Mirrors” by providing the lightweight mirror design files and the metrology data detailed in the paper. Metrology data includes optical microscopy, scanning electron microscopy, and surface roughness measurements. This publication was presented at SPIE Astronomical Telescopes and Instrumentation 2026 in Copenhagen, Denmark under the paper ID 14154-199.