Research data is all data that is generated in the course of research processes and forms the basis of scientific findings. Depending on the discipline, they are generated using different methods and are available in different formats. Examples are

• Measurement data and laboratory values
• audiovisual data
• Texts (including sources used)
• Survey and interview data as well as questionnaires and guidelines
• Objects from collections (including digital copies of the objects)

Tools for generating and processing data (e.g. code, scripts, algorithms, self-written special software) are also research data that should be documented and preserved to ensure the traceability of results.

Research data management (RDM) involves planning, implementing and documenting the organised handling of research data, from collection and analysis through to data protection - before, during and at the end of research projects. The aim of RDM is to ensure that data is utilised effectively and is accessible and verifiable in the long term, regardless of who generated the data. Where possible, research data should be made available to other researchers for subsequent use.

RDM is regarded by research funders as a regular element of research processes. Project applications must therefore generally contain statements on data management during the course of the project and on the long-term preservation of the data after the end of the project. A data management plan is usually expected. In addition, RDM is good scientific practice and in the researchers' own interests. RDM facilitates collaboration in teams and a later understanding of one's own data (months or years later).

We can advise you on which aspects of RDM you should consider in your research proposal and during the project. You can also find answers to your questions about research data management in the nationwide information portal forschungsdaten.info.