🟠Newsletter - April 2025

Biomedical Research Infrastructure and the Imperative for Robust Research Data Management

Biomedical Research Infrastructure and the Imperative for Robust Research Data Management

Disclaimer The views expressed here are those of the author in a professional capacity as a Data Steward and Research Data Manager at the Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW) at the 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 today operates in an increasingly data-intensive environment. Advances in genomic sequencing, high-resolution imaging, single-cell analysis, and computational biology have transformed the field, creating unprecedented volume and complexity.

Harnessing this data for meaningful scientific discovery depends critically on the infrastructure and practices to manage, store, share, and preserve it. Research Data Management (RDM) sits at the heart of this infrastructure, ensuring data remains reliable, accessible, and usable now and in the future.

Robust RDM is not merely a technical requirement but an essential foundation for maintaining biomedical research's integrity, efficiency, and impact.

Biomedical Research Infrastructure: A Data-Intensive Landscape

Biomedical research infrastructure must accommodate a rapidly growing range of data types and scales. Genomic data alone can encompass millions of variants per sample, while imaging datasets may span terabytes, and longitudinal clinical data introduces additional layers of complexity.

Without well-designed storage, organization, and access systems, this data can become fragmented, inconsistent, or lost. Properly managed, however, it becomes a powerful asset that drives discovery, supports collaboration, and improves health outcomes.

A key challenge for the biomedical research community is ensuring infrastructure development keeps pace with this accelerating data generation.

The Integral Role of Research Data Management in Biomedicine

At the core of biomedical research infrastructure lies Research Data Management. RDM is the systematic planning, handling, documentation, storage, sharing, and preservation of data throughout the research lifecycle.

Effective RDM practices in biomedical research enable:

• Data Quality and Accuracy: Maintaining reliable, complete, and consistent datasets.

• Accessibility and Reusability: Ensuring others can readily find, understand, and use data.

• Reproducibility: Supporting the validation and replication of experiments.

• Ethical and Legal Compliance: Protecting sensitive information and respecting privacy regulations.

• Data Security: Safeguarding against unauthorized access or data breaches.

• Collaboration and Data Sharing: Facilitating interdisciplinary and international cooperation.

• Long-Term Preservation: Ensuring data remains usable for future research questions.

Inadequate RDM can result in data loss, errors, ethical breaches, and wasted research investments, ultimately undermining the credibility and impact of biomedical science.

Key Challenges in Implementing Effective RDM

Data Volume and Complexity

The sheer volume and heterogeneity of biomedical data present significant management challenges. Data must be standardized, documented, and curated to remain usable across projects, institutions, and timeframes.

Potential approaches include:

• Developing standardized metadata schemas and data formats.

• Using advanced algorithms, machine learning, and automation to assist in curation and quality control.

• Implementing clear data management plans at the outset of research projects.

Interoperability

Lack of interoperability between systems, formats, and institutions remains a critical barrier to data sharing and reuse.

Solutions include:

• Adopting universal or domain-specific standards for data exchange and metadata.

• Participating in collaborative initiatives to harmonize practices across the research community.

• Designing infrastructure with interoperability as a core requirement.

Data Security and Privacy

Biomedical research often involves sensitive human data, requiring stringent privacy protections and security measures.

Best practices include:

• Implementing encryption, access controls, and secure authentication.

• Anonymizing or pseudonymizing data where appropriate.

• Ensuring compliance with data protection regulations such as GDPR.

• Providing training to researchers on ethical and legal obligations.

Infrastructure and Funding

Building a robust data infrastructure demands sustained hardware, software, and skilled personnel investment.

Positive developments include:

• Increased recognition by funding agencies of the need for dedicated RDM support.

• Institutional commitments to developing secure, scalable data platforms.

Challenges remain:

• Securing adequate, long-term funding.

• Ensuring equitable access to infrastructure and expertise across institutions and countries.

The Path Forward

The future of biomedical research depends on confronting these challenges directly. Prioritizing robust RDM is essential for compliance or operational efficiency and ensuring that research data can fulfill its potential to advance science and improve human health.

By investing in data management infrastructure, adopting standardized practices, ensuring interoperability, safeguarding privacy, and fostering a culture of responsible data stewardship, the biomedical research community can maximize the value of its data assets.

Effective RDM is not a peripheral concern but central to building a trustworthy, collaborative, and impactful research ecosystem.