Re­search Data Man­age­ment

Research Data Management (RDM) encompasses the organization, protection, accessibility, use and documentation of research data throughout its entire life cycle, from the planning of the project to the subsequent use of the data. It supports quality and sustainability efforts and is helpful in identifying legal and ethical aspects right from the start of the project. 

Research data is still accessible and usable after the end of a project. This promotes both innovation and the ability to cite research and contributes to more successful applications for third-party funding.

The following topics relate to RDM:

• Data documentation (metadata)

• Data organization (file formats, data exchange)

• Data storage and archiving
• Legal and ethical aspects (copyright, data protection, licensing)
• Data publication (repository, data centers, persistent identifier [PID])
  

Depending on the funding body and funding program, information on the handling of research data must be provided or a Data Management Plan (DMP) must be submitted. The aim of these requirements is to ensure the quality and findability of research data. For further information on the criteria of the various research funding bodies and the creation of a data management plan, please contact Research Support.

A Data Management Plan (DMP) describes the handling of research data that is produced or used during the course of a project and beyond. The DMP contains rules that are agreed and applied within the project team. It helps to systematically plan and transparently implement data management.

Not all data must or may be published. This may be the case if patenting is planned, contract research is involved or sensitive data is being worked with. A DMP can therefore also include reasons why the data should not be published.

While all sensitive data is considered personal data, not all personal data is sensitive data. The graphic below from CC digitallaw illustrates this difference.

A helpful tool for the legal aspects of research data is the DM LawTool from CC digitallaw.

The aim of FAIR data is that research data should be optimized for both humans and machines and be accessible without barriers. For this reason, the FAIR principles were defined:

• Findable
• Accessible
• Interoperable
• Re-Usable

The aim of the FAIR principles is to make databases accessible for new usage scenarios and to increase their reusability, provided this is legally, ethically and technically feasible.