Tips & tricks
Grant Applications

RESEARCH DATA MANAGEMENT IN GRANT PROPOSALS

Many research funders are by now demanding that applications for grants provide information on the handling of data likely to be generated in the projects concerned. How is the data saved, processed and stored? For many applicants it is sometimes unclear exactly what the funders’ expectations are and to what extent the level of detail of this information influences the assessment of the applications. We recommend that you always write a brief statement on the handling of research data obtained in the project.

On their research data website, the German Research Foundation (DFG) has compiled subject-specific recommendations for dealing with research data.

The following questions and answers relate primarily to applications to the DFG, but may also apply to other grant applications.

General information about grant applications

RDM incentives

Costs of Research Data Management

EU applications specifics - Horizon 2020

Applicants must state in the grant application whether they are participating in the Open Research Data Pilot. If you don’t want to participate (opt-out) you must justify this decision. Also in this case you must keep the data safe for at least 10 years. We strongly recommend participating in the Data Pilot, because in the next funding program the requirements of this program are expected to be mandatory for all applications.

Participation in the Data Pilot requires the submission of a Data Management Plan (DMP) within the first six months of the project's lifetime, outlining the intended use of the research data obtained in the project. We are happy to assist you in writing the DMP.

For the publication of data it is particularly important that it can be understood and reused by third parties. Therefore, the data must adhere to the FAIR principles:

Findable: Metadata and data should be easy to find for both humans and computers.

Accessible: It must be clear, how the data can be accessed.

Interoperable: The data must be integrateable with other data and interoperable with applications or workflows for analysis, storage, and processing.

Re-usable: Metadata and data should be well-described so that they can be replicated and/or combined in different settings.