MSE 2024
Lecture
25.09.2024
Digital transformation in materials science: implementing NFDI-MatWerk solutions for collaborative research and data management
HT

Hanna Tsybenko

Forschungszentrum Jülich GmbH

Tsybenko, H. (Speaker)¹; Menon, S.²; Guzmán, A.A.¹; Chen, F.³; Grünwald, K.⁴; Brinckmann, S.¹; Hickel, T.²; Dahmen, T.³; Hofmann, V.¹; Chmielowski, M.⁵; Mohrbacher, J.⁶; Schwaiger, R.¹
¹Forschungszentrum Jülich; ²Max–Planck-Institut für Eisenforschung GmbH, Düsseldorf; ³Deutsches Forschungszentrum für Künstliche Intelligenz, Saarbrücken; ⁴RWTH Aachen University; ⁵Friedrich-Alexander-Universität Erlangen-Nürnberg, Fürth; ⁶Albert-Ludwigs-Universität Freiburg, Freiburg im Breisgau
Vorschau
21 Min. Untertitel (CC)

The ongoing digital transformation in materials science and engineering requires a common digital infrastructure based on overarching elements and software tools. This project unfolds as a user journey and demonstrates the implementation of different tools and solutions developed by the members of the NFDI-MatWerk consortium to solve a real scientific problem today.

The scientific goal is to compare the elastic moduli of a reference aluminum alloy, which implies using three different scientific workflows and inducing exchange between three research groups, replicating the real-world collaborative research processes. At the same time, the research groups use a set of existing digital solutions for experimental research data management (PASTA-ELN), simulation workflow execution (Pyiron), and image processing workflow execution (Chaldene). Data sharing was achieved by exchanging the Jupyter Notebook files containing computational workflows and the ELN archives with experimental data in a compatible format.

To better comply with the FAIR principles, the generated data and metadata were stored in the research data management platform Coscine as well as in a GitLab repository. In addition, the metadata from the generated JSON files and Jupyter Notebooks was mapped to the MatWerk ontology and converted to human-readable YAML files.

The user journey provided valuable insights from the scientists' perspectives. In addition to the scientific results, the main outcomes are lessons learned and improvement ideas, such as machine-readable experimental protocols, standardized workflow representation, and automated metadata extraction.


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