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This study presents a framework for developing adaptable research data management systems (RDMS). The approach supports FAIR data principles, enhancing scientific discovery and collaboration in materials science.

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Area of Science:

  • Materials Science
  • Data Management
  • Scientific Computing

Background:

  • Digitalization in research necessitates versatile data management systems.
  • Existing systems range from document repositories to factographic environments.
  • Materials science data requires robust management for reproducibility and integration.

Purpose of the Study:

  • To introduce a methodological approach for stepwise development of research data management systems (RDMS).
  • To illustrate the approach using the MatInf Research Data Management System (RDMS).
  • To facilitate FAIR-compliant data infrastructures for materials science.

Main Methods:

  • Combining a graph-based STAR (Statefulness, Traceability, Aim, Result) paradigm.
  • Integrating the SET (Standardisation, Extraction, Testing) methodology.
  • Stepwise development from document-oriented to factographic environments.

Main Results:

  • A framework for adaptive RDMS design is proposed.
  • The approach supports the consolidation of research outputs into unified datasets.
  • Demonstrated pathway towards FAIR-compliant data infrastructures.

Conclusions:

  • Adaptive RDMS design accelerates scientific discovery.
  • Enhanced data management supports collaborative research in large-scale projects.
  • The framework promotes reproducibility, re-use, and integration of heterogeneous materials science data.