Research data are a cornerstone of scientific knowledge and a key component of scholarly output. They are essential for ensuring the transparency and reproducibility of research processes and their results. Transparent access to research data is a fundamental aspect of good scientific practice. Making research data publicly available creates long-term value for science and enables broad reuse. It also increases the visibility of researchers and their work.
Research Data Management (RDM) refers to the conscious and systematic handling of research data and provides the foundation for meeting the goals outlined above. It encompasses all phases of the research process — from project planning and data collection to analysis, storage, sharing, and archiving. The aim of RDM is to ensure that data remain Findable, Accessible, Interoperable, and Reusable in accordance with the FAIR principles.
The University Library, in collaboration with the Computing Center and Office for Research, supports and advises members of RPTU on key questions and requirements related to research data and Research Data Management (RDM).
Together with the consortia of the National Research Data Infrastructure (NFDI) and the Nachwuchsring (Early Career Researchers’ Network), we aim to raise awareness of research data practices at all levels and provide guidance on various aspects of RDM.
Definition of research data
There is no uniform definition of research data. Here, it is used to describe results that are generated in the course of research and have the potential to be reused by others. Research data can be measurement results, results from computer simulations, graphics, images, surveys, tests, films, audio recordings, software and much more. A coherent collection of research data is called a research repository.
Research data in a project
In a project, the handling of data and its planning is visible in many aspects:
- Research proposal: depending on the funding agency, the creation of a data management plan is sometimes required.
- Data collection and processing: In most cases, raw data are created in the project, which are further processed. Data processing may be done by different people at different locations.
- Documentation and metadata: In order to reuse the data later, documentation of the data is necessary. This is usually done through metadata about the project itself, project participants, data collection and measurement parameters, and much more.
- Long-term archiving: When archiving data, the data to be backed up must be selected and backed up using a suitable procedure. The data formats used also play a role here if this data is to be reused in the future.
- Publication: Reusable data should be made available to the community in a suitable form. With information on the resulting publications, the link between data and articles can be established.
- Search: Already existing data can be searched for, e.g. to process them further or to compare them with the own results.
Reasons for publication
Comparison with other data or additional analysis are possibilities for the subsequent use of research data. Here, new results are obtained from the original data. Type and scope of this use are legally regulated in each individual case.
Added value of published research data:
- Better citability and greater visibility of one's own data. According to Piwowar & Vision (2013), publications are cited more frequently if the associated research data have been published.
- Better findability and accessibility to research data.
- Increased transparency of research
When acquiring public research funding (third-party funding, e.g. DFG or EU), it is necessary to make statements about resulting research data and their publication.