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Bridging Interdisciplinary Research Data Management and Data Science through Modular Research Processes
Transparent and reproducible research in large interdisciplinary projects, such as the Cluster of Excellence ROOTS, depends on combining two perspectives that are usually considered separately: the stewardship concerns of Research Data Management (RDM) and the analytical concerns of Data Science. This paper introduces Modular Research Processes (MRP), a framework representing a research endeavour as distinct, referable products and procedures described at several layers of granularity, expressing resolution rather than a hierarchy of authority. MRP provides a shared representation in which RDM and Data Science converge, focusing on the point where (materialised) products are FAIR-ready, while their procedural counterparts are not (yet) - a discrepancy that motivates the development of systematic descriptions and, where applicable, recommendations. We organise the framework's value into three application fields: illustration and communication; documentation and organisation; as well as comparison and recommendation. Our examinations and activities are based on the Data Management and Data Science Platform (DMDSP), which is an organisational component of the ROOTS project. We present the DMDSP as an example of how such a bridge can be shaped, aligned with an existing ecosystem, filled with collaborative method development, and sustained through training. Beyond the platform, it is MRP that is the transferable contribution, with a claim to cross-disciplinary and cross-project application. We report on two archaeological analyses that have been reconstructed as first proofs of concept, techniques for early empirical feedback on the conditions for adoption of an MRP-related tool, and a research roadmap anchored in this beginning.
