Research data management (RDM) supports you in making research data accessible, understandable, and reusable at any point in the research process by planning, organizing, securing, and documenting the data. These measures address to answer: What data formats should I use for my research? Where can I securely store and archive my data? How can other researchers find and access my data? What documentation is needed to make sense out of the data sets? What standards can I use for my research? Where can I publish my data? What data can be made open?
RDM encompasses all data generated in the entire research process: raw data, sources, work data, scripts and code, or workflows. It is not only important for quantitative or experimental data, but supports all disciplines that define data according to their subject-specific needs, e.g., laboratory values, measurement data, survey data, texts, videos, or notes from qualitative interviews.
It starts with the creation of a data management plan (DMP), a structured document or template that helps you consider all aspects already at the beginning of your project. More and more funders are requiring a DMP or information about your data management and asking you to make your data FAIR (findable, accessible, interoperable, resuable). This is why RDM is important even if you do not choose to make your data publicly available.
We offer workshops in research data management in cooperation with the working group on research data management (AG Forschungsdatenmanagement). For individual support please contact firstname.lastname@example.org.