Depending on the discipline in which you work, research data can be very diverse. According to a basic definition, research data are created through scientific research, are the basis of research and document the research results.

Research data can be found, for example, in surveys, chemical models, geospatial information, software code, brain cell reconstructions and behavioral experiments.

In a broad working definition, research data is any information object used or generated in the course of research. The term "research data" also includes other things that we generate as part of our research project, such as code to process or analyze our data, workflows and tools, results and visualizations. These are the so-called "non-data objects".

With all the different types of data that researchers generate, they need to take care of it. Researchers need to plan the experiments and surveys that will generate the data, they need to store the data somewhere, they need to make sure that their colleagues or collaborators have access to it, they need to make sure the data is backed up, and they need to think about what happens to the data at the end of the project.

All these aspects are essential for research data management, RDM for short. If the project and its data are well managed, the researchers can also retrieve relevant information more easily in the future and better understand what has actually been done in the project. Thereby, by introducing RDM to the project, the researchers make it more transparent.

However, the perspective of FDM always goes beyond the current research project: if the data is handled, processed and documented well, it is possible to use the same data again later, either by the authors themselves or by other researchers.

It is therefore a central goal of RDM that research data are available after the end of the project, i.e. are made reusable.