Welcome

• Alun Ashton (PSI - Paul Scherrer Institut)

Welcome to connecting participants and quick housekeeping

Projects overview

• Andy Götz
• Patrick Fuhrmann (Deutsches Elektronen Synchrotron, DESY)

Describe data by scripts for future reuse

• Petr Čermák

There is a long-lasting discussion in the photon and neutron community how to properly describe the data and which metadata are useful. To fulfil the last letter in FAIR, our data needs to be reusable, which is often the most difficult task for large research infrastructures users. Petr Čermák will present an easy and convenient way of describing the data by user scripts. We will use publicly available data at PaNOSC ILL, treat them using open-source software and we will publish the scripts on GitHub repository. We will mirror the repository at Figshare to get citable entity and show how to use Binder to re-evaluate the data from any computer in the world even after 100 years. Such approach will completely describe treated data by their transparent evaluation. Referees of the upcoming publication can easily verify data treatment process, other scientists can easily learn how you are treating the data and what is most important – the data treatment process will work forever.

Tomography Case Study

• Kamel Madi

Neutron diffraction from Boro-carbon for efficient structural analysis and defect detection

• Mousumi Upadhyay Kahaly (ELI-ALPS)

Neutron scattering is considered to be a complimentary technique to electron microscopy which unveils detailed information on the defect structure in real space over tiny localised volumes in the specimen. Boron-doped diamond (BDD) is a conductive material and is considered as a potential candidate for electrode materials with large cell voltages. However the exact role of Boron and its location within the crystal has not been investigated so far. Within the scope of this PaNOSC user case, inelastic neutron scattering experiments and ab-initio calculations have been used to investigate the location-dependent response of defects in diamond, and BDD structures. Ab-initio tools from atomistic simulation environment (ASE) is used for obtaining structural and electronic properties, and relaxed nuclear positions. Based on these nuclear positions, neutron scattering is simulated with McStas code in well-known experimental environment. The origin of the diffraction peaks was identified, correlating them to individual system geometries. Our approach can correlate the appropriate ‘micro atomistic scenario’ among a manifold of possibilities to reproduce the observed ‘experimental macro features’.

TELBE Data Analysis workflow and the PaN training platform UX

• Jan-Christoph Deinert (HZDR)

Machine Learning-based Spectra Classification

• Yue Sun

Spectroscopy experiment techniques are widely used and produce huge amounts of data especially in facilities with very high repetition rates. At the European XFEL, X-ray pulses can be generated with only 220ns separation in time and a maximum of 27000 pulses per second. In experiments (e.g. SCS, FXE, MID, and HED) at European XFEL, spectral changes can indicate the change of the system under investigation and so the progress of the experiment. Immediate feedback on the actual status (e.g., time-resolved status of the sample) would be essential to quickly judge how to proceed with the experiment. The major spectral changes that we aim to capture are either the change of intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. Machine Learning (ML) opens up new avenues for data-driven analysis in spectroscopy by offering the possibility to quickly recognize such specific changes on-the-fly during data collection, and it usually requires lots of data that are clearly annotated. Hence, it is important that research outputs should align with the FAIR principles. For XFEL experiments, it is suggested to introduce NeXus data format standards in future experiments. In this work, we present an example to show how Neural Network-based ML can be used for accurately classifying the system state if data is properly provided. We demonstrate a solution to automatically find the regions (or bins) with high separability where the spectra classes differ significantly. By teaching individual neural networks for each bin and combining them with a weighting technique, a robust classification of any new spectral curve can be quickly obtained.

DOI, FAIR, an MX COVID-19 use case

• Frank von Delft (Diamond Light Source)

Introduction to the survey on the projects outcome adoption

• Patrick Fuhrmann (Deutsches Elektronen Synchrotron, DESY)
• Andy Götz

LEAPS perspective on projects outcome and sustainability

• Caterina Biscari (ALBA Synchrotron)

LENS perspective on projects outcome and sustainability

• Robert McGreevy

Questions and discussion, wrap-up

• Andy Gotz (ESRF)
• Patrick Fuhrmann (Deutsches Elektronen Synchrotron, DESY)