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<p>The digitalisation of research requires data management systems capable of supporting a broad spectrum of usage scenarios, ranging from document-oriented repositories to fully factographic environments. This paper introduces a methodological approach for the stepwise development of such systems, illustrated by the <i>MatInf</i> Research Data Management System (RDMS). The proposed framework combines a graph-based <b>STAR</b> paradigm—emphasising <b>S</b>tatefulness, <b>T</b>raceability, <b>A</b>im, and <b>R</b>esult—with the <b>SET</b> methodology, which enables systematic <b>S</b>tandardisation, <b>E</b>xtraction, and <b>T</b>esting of research data. Together, these principles provide a pathway towards FAIR-compliant data infrastructures, facilitating reproducibility, re-use, and integration of heterogeneous materials science data. By demonstrating the gradual consolidation of research outputs into unified datasets, this study highlights how adaptive RDMS design can support accelerated scientific discovery and enhance collaborative research in large-scale projects.</p><p><b>Scientific Contribution:</b> This work develops a methodological framework for the progressive evolution of research data management systems in materials science, moving from document-oriented to graph-based and ultimately factographic approaches.</p>

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Evolve with your research: stepwise system evolution from document-driven to fact-centric research data management in materials science

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Victor Dudarev,
  • Alfred Ludwig

摘要

The digitalisation of research requires data management systems capable of supporting a broad spectrum of usage scenarios, ranging from document-oriented repositories to fully factographic environments. This paper introduces a methodological approach for the stepwise development of such systems, illustrated by the MatInf Research Data Management System (RDMS). The proposed framework combines a graph-based STAR paradigm—emphasising Statefulness, Traceability, Aim, and Result—with the SET methodology, which enables systematic Standardisation, Extraction, and Testing of research data. Together, these principles provide a pathway towards FAIR-compliant data infrastructures, facilitating reproducibility, re-use, and integration of heterogeneous materials science data. By demonstrating the gradual consolidation of research outputs into unified datasets, this study highlights how adaptive RDMS design can support accelerated scientific discovery and enhance collaborative research in large-scale projects.

Scientific Contribution: This work develops a methodological framework for the progressive evolution of research data management systems in materials science, moving from document-oriented to graph-based and ultimately factographic approaches.