🟡Newsletter - April 2025
Monthly Newsletter
Biomedical Research Infrastructure and the Imperative for Robust Research Data Management
Disclaimer The views expressed here are my own in my professional capacity as a Data Steward and Research Data Manager at the Novo Nordisk Foundation Center for Stem Cell Medicine – reNEW, University of Copenhagen. They do not necessarily represent the official views of reNEW Copenhagen or the UCPH Faculty of Health and Medical Sciences.
Introduction
Biomedical research is evolving in a profoundly data-intensive environment. Advances in genomics, high-resolution imaging, single-cell analysis, and computational biology have transformed the field—producing unprecedented data volumes and complexity.
Harnessing this data for discovery depends on the infrastructure and practices used to manage, store, share, and preserve it. Research Data Management (RDM) is central to this effort, ensuring that biomedical data remains reliable, accessible, secure, and reusable throughout its lifecycle.
Robust RDM is not just a technical necessity; it is also a cornerstone of research integrity, efficiency, and long-term impact.
Key Challenges in Biomedical RDM
1. Data Volume and Complexity
Biomedical datasets often span terabytes to petabytes, from genome sequences to time-lapse imaging.
Without standardization and documentation, these datasets risk becoming fragmented or unusable. Approaches:
Use standardized metadata schemas and formats.
Implement automated quality control and curation tools.
Create DMPs at the start of every project to guide workflows.
2. Interoperability Gaps
Disparate systems and formats hinder data sharing and reuse across projects and institutions. Approaches:
Adopt community or domain-specific data standards.
Participate in harmonization initiatives such as GA4GH and ELIXIR.
Design infrastructure with interoperability as a core principle.
3. Data Security and Privacy
Many biomedical datasets contain sensitive personal or clinical information. Approaches:
Apply encryption, fine-grained access controls, and secure authentication.
Use anonymization or pseudonymization techniques where appropriate.
Ensure compliance with GDPR and other applicable regulations.
4. Infrastructure and Funding Limitations
Investment in hardware, software, and skilled personnel is essential but often underfunded. Approaches:
Advocate for RDM support in funding proposals.
Leverage institutional and national infrastructure platforms.
Ensure equitable access to infrastructure across research groups.
Local Context: reNEW and UCPH
At reNEW Copenhagen and the University of Copenhagen, biomedical research increasingly depends on large-scale imaging, sequencing, and multi-omics datasets. Key needs include:
Scalable, secure storage integrated with UCPH-approved platforms such as ERDA.
Interoperable data pipelines to enable collaboration across reNEW’s partner institutions.
Training and support for researchers to implement FAIR and compliant data management from project start.
Practical Recommendations
Integrate RDM Early – Include RDM planning at the grant application stage.
Adopt Standards – Use domain-relevant metadata and file formats.
Ensure Security by Design – Build privacy and security measures into infrastructure from the outset.
Leverage Institutional Resources – Utilize UCPH and Danish national data services for storage, curation, and sharing.
Foster a Data Stewardship Culture – Provide training and recognition for good RDM practices.
Looking Ahead
The future of biomedical research depends on data that is findable, accessible, interoperable, and reusable. By investing in robust RDM infrastructure and cultivating a culture of responsible data stewardship, we can maximize the scientific, ethical, and societal value of biomedical research.
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