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 your data. These measures take the following questions into account: 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 reasonably organize such data sets? What standards can I use for my research? Where can I publish my data? What data can be made public?
RDM covers all data created 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 needs, e.g., laboratory values, measurement data, survey data, texts, videos, or notes from qualitative interviews.
It all 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 asking for a DMP or information about your data management and requiring you to organize your data FAIR (findable, accessible, interoperable, resuable). This is why RDM is important even if you do not want to choose to or cannot make your data publicly availbale, i.e., open.
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.