Source: Martin Braun
Research data is the backbone of every scientific endeavor. However, the collection, documentation, and publication of research data is only useful if it adheres to the principles of Good Scientific Practice (GSP). These principles ensure scientific integrity and the quality of research. A central component of GSP is the proper handling of research data, which plays an important role in research data management (RDM).
What Does Good Scientific Practice Mean in the Context of RDM?
The German Research Foundation (DFG) defines GSP as the principles that ensure that research is conducted in an ethically sound and transparent manner. For RDM, this means that data must be carefully collected, systematically documented and transparently processed. Researchers must ensure that their data is traceable and reproducible at all times. This is not only important for scientific integrity, but also for the long-term availability and usability of data.
The DFG Guidelines for Good Scientific Practice
In its code of conduct “Guidelines for Safeguarding Good Research Practice”, the DFG has defined important rules for handling research data. Universities and research institutions must implement these in order to comply with scientific standards. The relevant guidelines include, for example:
- Guideline 12 – Documentation: Research results must be documented accurately so that they remain verifiable.
- Guideline 17 – Archiving: Research data should be stored for at least ten years.
These guidelines promote the responsible handling of research data and help to ensure the verifiability and integrity of research.
Reproducibility and Long-Term Archiving as Central Principles
A central goal of good scientific practice is the reproducibility of research results. Only if data is carefully documented, stored in a structured way and archived for the long term can it be verified or reused by other researchers. Without systematic data management, there is a risk that valuable research data will be lost or can no longer be meaningfully interpreted. Long-term storage in recognized repositories is therefore an essential part of sustainable research.
Data Management Plans Are the Key to Success
Data management plans (DMPs) are a valuable tool for organizing the entire life cycle of research data. A DMP describes how data is stored, documented and archived during and after a project. It also defines responsibilities and regulates access rights. Many funding organizations now require a DMP as part of a research proposal. A well-thought-out plan not only facilitates data management in the research process, but also increases the reusability of data for future projects. The Research Data Management Organiser (RDMO) DMP tool is designed to help researchers who want to create a DMP.
Coscine: The Bridge Between Data and Good Science
The RWTH Aachen University offers a central solution for research data management with the Coscine platform. As described in our blog post Central RDM Services at RWTH: Coscine, Coscine supports researchers in managing their data in accordance with good scientific practice. Features such as standardized metadata, access management, and sustainable archiving help ensure transparency and reproducibility. Data sharing with Coscine also plays an important role: researchers can specify who can access their data to ensure secure and compliant sharing.
The Advantages of Structured RDM
The advantages of systematic RDM are manifold. Researchers benefit from greater efficiency, as well-organized data facilitates collaboration and reduces the effort required for further analysis. In addition, publishing data in repositories increases their visibility and citability, which in turn strengthens the scientific reputation of researchers.
Another advantage is the possibility of reusing existing data. Other researchers can access existing data sets and use them to address new questions.
Conclusion
Careful RDM is a central component of good scientific practice. It contributes significantly to the quality and integrity of scientific work. The DFG guidelines provide clear orientation for the responsible handling of research data and help ensure transparency and traceability.
Researchers who integrate structured RDM into their work contribute to the long-term availability and reusability of scientific data. This makes research data management an indispensable part of excellent research.
Find Out More
Do you have questions about RDM or good scientific practice? Our RDM team is always available to help and will support you in choosing the appropriate RDM services. Please do not hesitate to contact us – we will be happy to help!
Responsible for the content of this article is Hania Eid.
The following source served as the basis for this article:
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