Introduction to RDM

Research data undergo various processes, which can be illustrated using the research data lifecycle. This lifecycle serves as a conceptual tool to support research data management. Not all steps are always followed, nor are they always followed in the same order. Here, we would like to present a six-stage model of the data lifecycle [1]. Detailed information about each stage can be found at the respective link.

Ideally, research data management (RDM) should begin even before the application and initiation of a research project, with the planning phase (Step 1: Planning the research project). A recommended step during planning is the creation of a data management plan (DMP). Additionally, it is useful to consider file naming conventions and storage structures at this stage, to avoid chaos later on. Conducting a search for already existing research data should also be considered in this step.

Once the research project is planned and has started, data is collected as part of the work (Step 2: Collecting data). Data collection can look very different depending on the field and methodology applied. It is important not only to document the methodology but also to document the research data and any processing that may occur. Metadata play a crucial role in this context.

During the preparation and analysis of the data (Step 3: Preparing and analyzing data), documentation of all working steps should be maintained to ensure transparency and reproducibility.

Research data themselves represent a valuable resource, which can be made available and citable in the form of a data publication (Step 4: Sharing and publishing data). If careful documentation has already been maintained during data collection and preparation, this will make work much easier at this stage at the latest.