From Data Lake to Data Mesh. Implementing Distributed Research Data Management in CRC1382

Zenodo (CERN European Organization for Nuclear Research)

Abstract

Collaborative Research Centers (CRCs) generate large volumes of heterogeneous research data across multiple projects and disciplines. CRC1382 "Gut-Liver Axis" comprises 22 projects organized into three project areas (A, B, and Q), each contributing complementary perspectives on the interactions between the gut, liver, metabolism, immunity, and the microbiome. The interdisciplinary nature of this research requires data from diverse sources to be discoverable, understandable, and reusable across project boundaries. Traditional research data management often resembles a data lake, where data are centrally stored but can become difficult to govern, interpret, and reuse. To address these challenges, CRC1382 is adopting data mesh principles, a decentralized approach in which research groups retain ownership of their data while treating datasets as well-documented, reusable products. Shared standards and infrastructure ensure interoperability and reuse across the consortium. A central component of this strategy is the CRC DATAHUB, which provides a shared platform for dataset discovery, documentation, and governance while preserving project ownership. Researchers remain responsible for the scientific context and quality of their data, while metadata are centrally managed. This enables scalable research data management as projects evolve, new collaborations emerge, and consortium structures change. The Data Steward and Data Engineer support this process by maintaining the DataHub, harmonizing metadata, implementing governance standards, and facilitating data integration without taking ownership from researchers. This poster presents the motivation, design, and implementation of the CRC1382 DataHub and demonstrates how data mesh principles can be applied in a biomedical research consortium. It provides a practical model that can be adapted by other CRCs and collaborative research initiatives to strengthen research data management and FAIR data practices.

Authors 2

1. Catherine Gonzalez Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Lukas C. Bossert Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University