From Interdisciplinary Research Data Management to Reproducible Research in Practice

The proliferation of heterogeneous data in large-scale interdisciplinary projects, such as the Cluster of Excellence ROOTS, requires strategies ensuring FAIR research outcomes (data, code), and thus supporting scientific collaboration. This paper introduces the early conceptual framework and activities for the Data Management and Data Science Platform (DMSP), established to bridge Research Data Management (RDM) and Data Science in the archaeology-focused research project ROOTS. This work introduces the Modular Research Pipeline (MRP), which integrates the lifecycle perspective of RDM with the procedural stages of the Knowledge Discovery in Databases (KDD) pipeline as a core concept of the platform. MRP provides a flexible framework for structuring research into reusable procedures and products, accommodating the iterative nature of research and the need for structured human input at critical junctures. Additionally, levels of granularity and fields of activity in the DMSP perspective on interdisciplinary research are layed out. Key activities include fostering a trust-based culture, developing analytical methods that combine data science with domain knowledge through collaborative use-cases, and aligning the DMSP with the existing RDM ecosystem (recommendations, existing services and so on). The DMSP's next steps of implementation at this early stage are organised across four strategic areas: people, research, infrastructure, and further concept development. By combining lifecycle-oriented stewardship with a broad data science perspective, the DMSP aims to transform interdisciplinary research practices within ROOTS into processes that are transparent, reproducible and yield sustainably FAIR outcomes.