Managing complexity in research data management for heterogeneous biotechnological data: a perspective on interacting...

Area of Science:

• Biotechnology research
• Bioeconomy and healthcare innovation

Background:

• Biotechnology generates vast amounts of data, necessitating robust research data management (RDM).
• Challenges in RDM include heterogeneous data formats, metadata complexity, data protection, and governance.
• Archiving and storing diverse, often proprietary, data and ontologies across domains is difficult.

Purpose of the Study:

• To evaluate the effectiveness of an institutional repository for RDM in biotechnology.
• To assess the alignment of centralized RDM infrastructure with researchers' practical needs.
• To identify requirements for sustainable RDM in complex biotechnology research environments.

Main Methods:

• A use case study involving 50 international, cross-discipline biotechnology researchers.
• Implementation of RDM using an institutional repository for experimental, modeling, and process engineering data.
• Analysis of user expectations and practical research application challenges.

Main Results:

• Centralized repository infrastructure provided a technical basis for FAIR data handling.
• Existing RDM solutions did not fully meet researchers' practical application expectations.
• Generic frameworks and standardized workflows required continuous, discipline-specific, human-mediated support.

Conclusions:

• Sustainable RDM in biotechnology requires integrating discipline-specific support into research workflows.
• Data stewards are essential intermediaries for optimizing RDM support structures.
• Aligning RDM with researchers' evolving needs is critical for complex biotechnology research.