Funding

In recent years, numerous national and international funding programs have presented guidelines for research data management, such as the DFG in its "Guidelines on the Handling of Research Data"* and the "Guidelines for Safeguarding Good Research Practice."**

The basic requirement is to make research data, generated or processed with the support of public funds, available to the public free of charge and without restrictions, provided there are no overriding legal restrictions that must be observed.

Data management plans are also increasingly expected by third-party funders. They serve as an instrument for implementing systematized research data management throughout the entire course of a project.

You can find out more about the requirements of the individual funding organisation in the guide "Research Data Management Requirements of Funding Institutions".

Research Data Management Guidelines (selection):

• *Deutsche Forschungsgemeinschaft. 2015. “Guidelines on the Handling of Research Data.” https://www.dfg.de/download/pdf/foerderung/grundlagen_dfg_foerderung/forschungsdaten/guidelines_research_data.pdf.
• **Deutsche Forschungsgemeinschaft. 2022. ‘Guidelines for Safeguarding Good Research Practice. Code of Conduct’. https://doi.org/10.5281/zenodo.6472827.
• European Commission (2021). EU Grants AGA – Annotated Model Grant Agreement. EU Funding Programmes 2021-2027 (PDF)
• European Commission (2021). Horizon Europe Programme Guide (PDF)
• European Commission (2022). Information for ERC grantees (20.04.2022, version 4.1.) Open Research Data and Data Management Plans (PDF)
• OpenAIRE (2022). RDM in Horizon Europe Proposals. https://www.openaire.eu/rdm-in-horizon-europe-proposals.

Subject-Specific Recommendations

Numerous subject-specific recommendations and publications on the handling of research data address the cultures, demands, and needs of individual research areas (see DFG subject-specific recommendations on the handling of research data).

Publishers

Natural and life science publishers, in particular, maintain guidelines on the accessibility of publication-relevant data. These deal, for example, with the obligation to disclose data on request or parallel publication. Some publishers and platforms offer the option of co-publishing the data with the publication (“enhanced publication“); data journals are also growing in popularity as potential publication formats.

The data management plan (DMP for short) should be drawn up in the planning phase and revisited throughout the duration of the project. In the case of a larger research project, it is recommended that one person be designated who is responsible for planning and implementing research data management. All project participants should be made familiar with the contents of the DMP.

A data management plan is not a static document; it will be revised, supplemented, and adapted during the life of the project. The following points should be described in the DMP:

• What kind of data is used or created?
• How are the data generated (methods/tools/software)?
• What general conditions and factors apply (standards and policies of the discipline, the research institution or the funding institution)?
• What data formats will be available and what scope will the data have?
• How will data be organized and named?
• Where will the data be stored and how will the data be backed up regularly?
• How will sensitive data be protected?
• How and where will the data and the context of its creation be described and documented?
• How and where will the data be archived after the project ends?
• Will the data be published? If not, why? If yes, where and with what kind of license

There are many different templates and checklists for DMPs, including the DFG Checklist Regarding the Handling of Research Data (PDF), the Science Europe Guide (PDF) (for researchers p. 15-30), or the subject-specific CESSDA template (PDF) for the social sciences.

Based on the DFG checklist, the Research Data Management team at Freie Universität has created a completed sample DMP (PDF) and a blank template (RTF) that you can use as a guide.

Useful tools to create a plan are RDMO and DMPonline; these are freely available for use.

If you are writing a data management plan, you can contact us, and we will advise you on suitable templates and support you in filling in all points in the best possible way.

Reusable data can be searched in repositories, data journals, or search engines. The Registry of Research Data Repositories (re3data) provides a search facility for subject-specific repositories.

Data journals also publish data sets with brief descriptions (see also Data Journals at forschungsdaten.org).

In addition, numerous search engines provide reference systems for research data:

When reusing data, the appropriate licensing and referencing systems must always be observed (see Licenses).

The citation of all sources used in a research project according to established standards is one of the elements of good scientific practice. For guidance on citing data, see Cite your data on datacite.org or Citing data (in German) on forschungsdaten.info.

Freie Universität supports its members in selecting suitable tools in the context of working with large amounts of data, building databases, or a digital research environment for their project. You can find an overview under Services.