Research Data Management

Science is becoming increasingly digitalised and both the quantity and heterogeneity of information and data are growing steadily. Research requires technical and professional support for data management plans, for the storage and backup of small and large amounts of data and for legal issues. Research data management encompasses all measures relating to the handling of predominantly digital data throughout the entire research process.

According to the "Guideline for Handling Research Data at Darmstadt University of Applied Sciences", research data is all data that is generated during the research process or is the result of it. The range of research data is broad. Depending on the discipline, this includes, for example, measurement results, indexing data from scientific collections, radiological images, study surveys, digital copies of historical sources, cell cultures, microscope images, material samples, documentation of archaeological finds, questionnaires, interviews or observations.

In order to ensure the subsequent use of the data, the context of its creation and the tools used, especially software, must be documented.

Research data management starts with the planning of a project and includes data collection, data documentation (metadata), naming and file organisation as well as the allocation of access rights and continues with secure storage during the research process through to publication and subsequent long-term archiving of the data beyond the actual research process. Research data management serves several purposes. For example, sensible research data management enables research results to be replicated and thus to be reproducible and verifiable. This promotes transparency in the sciences. Research data management also allows data that has already been collected to be used for new research questions. This means that data that is often laboriously generated can be utilised for further research.

Research data management can also be helpful for researchers, as many project-related aspects are processed in a structured manner and are quickly available to both funding organisations and the researcher's own institution. Good scientific practice includes storing the data for at least 10 years; some data should or must also be stored long-term.  

To ensure the subsequent use of the data, the context of its creation and the tools used must be documented. This is supported by a data management plan in which you systematically describe how you handle your data during the project. This is important to ensure that your data can be used sensibly at a later date or by third parties. 

In order to fulfil good scientific practice, it makes sense to save and archive research data. The DFG recommends storing data for 10 years. However, if the data is particularly outstanding, it should be kept for the long term, i.e. beyond an indefinite full stop of time. 

Repositories for research data can be found in the Registry of Research Date Repositories and on the repository list of the German Initiative for Network Information (DINI).