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Research Data Management (RDM)

Bib > Research > Handling Research Data - HSD University Library > Research Data Management (RDM)

​​​​​​​​​​​​​​​​​​​​​​​​​​​Research data management (RDM) refers to the process of transforming, selecting, and storing research data with the aim of ensuring that it remains accessible, reusable, and verifiable over the long term, regardless of who generated it. 

What are research data?

Research data refers to (digital) data generated during a research process or resulting from it. Depending on the research question, this data is produced using various methods, such as measurements, surveys, and source analysis. It forms the foundation of scientific work and documents its results. This gives rise to a discipline- and project-specific understanding of research data, with varying requirements for its processing and management.


The difference between primary and secondary data:

  • Primary data is data that is generated during collection or digitization without any further processing.
  • Secondary data has already been processed or further analyzed.

Since research data is necessary for verifying the research results based on it, its preservation is a recognized component of good scientific practice.

During the research process, research data passes through various stages at each of which measures can be taken to maintain the scientific validity of the data, ensure its accessibility to third parties for evaluation and analysis, and safeguard the chain of evidence. The data lifecycle illustrates these stages.


Click here for reusable materials and an accessible description of the graphic:data Lifecycle [PDF file, 36 KB].


RDM is relevant for researchers when applying for external funding. Funding agencies, such as the DFG, the EU, various academic journals, and professional associations, require the disclosure of RDM as part of the application process. A sustainable RDM therefore has direct and indirect benefits for the researchers themselves as well as for the scientific community.​​

Why is research data management important?

  • Publishing in data repositories increases the visibility of your own research.
  • When inquiries are received during the review process for a publication, the data has already been prepared by management.
  • The risk of data loss is minimized through RDM measures such as data documentation, data backup, and appropriate long-term archiving (LTA). This ensures that data can still be used decades later.
  • Duplicate work (such as having to spend time re-familiarizing oneself with the data) can be avoided by properly documenting and organizing the data. ​
  • There are funding sources (such as Horizon 2020) that are available exclusively to projects that can provide a data management plan.
  • Additional funding requirements regarding the handling of research data are already addressed by a professional research data management (RDM) system.

The University Library supports you - as scholars, researchers, and authors at Düsseldorf University of Applied Sciences - in close collaboration with the Office of Research and Transfer, addressing any questions or needs you may have regarding RDM.​​


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