The digital transformation sweeping across every domain has catapulted research data into the spotlight. Research data has become pivotal for acquiring knowledge and innovating new technologies as industries lean into the digital age. This sea change presents unprecedented challenges for the workforce, policy makers, funders, educational institutions and others. Central to these challenges is the need to equip everyone with these necessary skills:

Research data management in the biomedical sector becomes ever more essential. It encompasses a vast array of processes, guidelines, and principles to ensure data's utility, security, and integrity. As we navigate this digital frontier, understanding and mastering biomedical research data management will prove pivotal for researchers and professionals alike.

The problem with research data: For research data to become actionable, the user has to find relevant (meta-) data, to understand the context in which the data were created/collected, and under what conditions the data can be used. This is also true for research data subsequently created/collected by the user. The FAIR principles and the concept of the research data lifecycle outlined below will provide guidance to researchers/users on how to make best use of research data.

Key components of biomedical research data management are:

FAIR stands for "F"indable, "A"ccessible, "I"nteroperable and "R"eusable. Performing research data management according to FAIR principles enables researchers and data users to make research data findable, accessible and reusable, while adhering to legal and ethical requirements.

Findable: Data must be easily locatable. Assigning persistent identifiers and ensuring rich metadata enable others to find these datasets.

Accessible: Data should be stored in formats and on platforms where authorised users can retrieve it, even long after its creation. Proper authentication and authorisation protocols ensure that data remains both secure and accessible.

Interoperable: Ensuring consistent data formats, standards, and vocabularies, so datasets can be seamlessly integrated. This becomes pivotal when merging data from diverse sources or utilising varied software and tools.

Reusable: Beyond its initial purpose, data should be structured and documented in a manner that allows others to confidently use it for subsequent research. Clear licensing and data dictionaries play crucial roles here. 

Find further information on the FAIR principles here.

The 5 central steps of the data life cycle [handbook on training concepts in research data managemen/Berman et al. 2018].

Clean
Processing: Leveraging tools like ETL (Extract, Transform, Load) processes and data wrangling techniques, raw data is cleansed and transformed into a usable format.

Use/Reuse
Analysis: Harnessing statistical software, algorithms, and analytical models, insights are derived from the processed data. Re-use: Existing datasets can become invaluable assets for future research, minimising redundant efforts and fostering a culture of collaboration.

Publish
Sharing: Using data repositories or platforms, datasets are shared with other researchers, following FAIR principles.

Preserve/Destroy
Archiving or Destruction: Based on regulatory and institutional guidelines, data is either archived for potential future use or destroyed securely.

Biomedical data repositories e.g. the NFDI4Health German Central Health Study Hub offer structured storage solutions with built-in tools for data search, validation, and analysis.

Protecting data in the biomedical field isn't merely about preventing unauthorised access. It also involves ensuring that personal patient data remains anonymous and that ethical standards are met, while adhering to regulatory guidelines such as GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act).

1. Forschungsdatenportal für Gesundheit (FDPG): Central contact point for researchers who want to carry out a research project with routine data from German university medicine and other affiliated locations.

2. Use & Access Committees: These committees evaluate the intent behind data access requests. Their aim is to balance open science with ethical research practices.

3. Embracing and understanding these facets of Biomedical RDM ensures that researchers not only comply with global standards but also pave the way for innovative, ethical, and impactful research.