Several factors motivate the implementation of research data management:

• Certain funding institutions require RDM strategies (such as a data management plan) as a prerequisite for funding (see article on data management plans).
• By adhering to RDM processes from the outset, researchers not only facilitate their own future reinterpretation of their data but also reduce the effort required to prepare data for reuse by third parties.
• Reproducibility and traceability of research findings depend on well-documented RDM practices (RatSWD, 2023, p. 8).
• The risk of data loss is minimized through RDM measures such as data protectionData protection includes measures against the unlawful collection, storage, sharing, and reuse of personal data. It is based on the right of individuals to self-determination regarding the handling of their data and is anchored in the General Data Protection Regulation (GDPR), the Federal Data Protection Act (Bundesdatenschutzgesetz), and the corresponding laws of the federal states. A violation of data protection regulations can lead to criminal consequences. Read More, data securityData security encompasses all preventive physical and technical measures aimed at protecting both digital and analog data. Data security ensures data availability and safeguards the confidentiality and integrity of the data. Examples of security measures include password protection for devices and online platforms, encryption for software (e.g., emails) and hardware, firewalls, regular software updates, and secure deletion of files. Read More, systematic documentation, and long-term archiving (LZA).
• Good RDM practices support the implementation of the FAIR principles.

However, research data management does not necessarily mean that data must be openly accessible. There may be legal (e.g., usage rights) or ethical reasons against Open Science that must be carefully considered.

Evidence in Data Affairs

Introduction to Research Data Management