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What is a Data Management Plan? In this article, Dr. Gessner explains everything important about the research data management plan.
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What is a data management plan?

A data management plan (DMP) is a document that describes the intended management of research data. This includes activities during the research process as well as after completion of the project. The plan contains all information necessary to describe and document the collection, processing, storage, archiving and publication of research data. The scope of a DMP can vary from a few paragraphs to several pages.


A data management plan consumes resources in its creation and offers many advantages at the same time.

A data management plan:

  • creates a firm basis for a uniform handling of data in the research process
  • facilitates the understanding of your own data
  • facilitates the coordination between project partners
  • helps to identify potential problems at an early stage and to outline solutions for them
  • defines responsibilities
  • regulates access rights
  • helps to avoid data duplication, data loss and security loopholes
  • can be (sometimes obligatory) part of a grant application

A useful initial consideration and argument for a data management plan is to think backwards, i.e. where and how should the data be archived or published? These considerations make it necessary to set the course in the data management workflow early on, e.g. with regard to formats, standards, metadata, licenses, etc.

Components of a data management plan

Depending on the size of the project and the variety of data, data management plans vary greatly. It is important to consider recommendations and specifications of third parties, e.g. funding agencies or employers, when preparing them. The most frequently used components of DMPs are:

  • Project title, project duration and research question(s)
  • Name of person responsible for the data management
  • Reuse of existing data
  • Data to be collected:
    • Description of the data to be collected, data types and formats
    • Expected memory capacity requirements
    • Data collection methods, hardware and software used
  • Data organization:
    • Data storage
    • Backup
    • Folder structure
    • File naming conventions
    • Documentation and metadata
  • Legal aspects, for example:
    • Data protection
    • Copyright
  • Data exchange and access:
    • In the project
    • With external partners and service providers
  • Long-term preservation and archiving
  • Data Publication
  • Costs of data management
  • Quality assurance
  • Access and re-use

The variety of research data and of the way they are handled determines the length of a data management plan. It should be short, specific and approved by all project participants. An incomplete DMP is better than none at all. Changes to the plan are not unusual and updates therefore necessary. Ideally, a data management plan evolves in a dynamic way: i.e. it is continuously updated and expanded during the course of the project and thus advances from a sketch to a detailed documentation of the data management process (active Data Management Plan) and thus contributes to the reusability of the data.

Funder requirements

In Germany, data management plans are already required by many research funders when submitting proposals. Research funders such as the European Commission (EC), the German Research Foundation (DFG), the Volkswagen Stiftung (VW Foundation) and the Federal Ministry of Education and Research (BMBF) are increasingly demanding: a data management plan to be provided at the start of funding (EC), information on how to handle collected research data (DFG) and – depending on the funding guideline – a plan for the use of project results or a detailed data management plan with the funding proposal (BMBF, VW Foundation).


To create a data management plan, a large number of tools and templates exist. Platforms such as DMPTool, RDMO, and DMPonline guide researchers through a questionnaire and assist with completion. In addition, funding agencies, universities, and research centers often provide their own templates that are customized to their own requirements and services.

This text is an excerpt (p. 53 to 55) from Biernacka, Katarzyna, Bierwirth, Maik, Buchholz, Petra, Dolzycka, Dominika, Helbig, Kerstin, Neumann, Janna, Odebrecht, Carolin, Wiljes, Cord, & Wuttke, Ulrike. (2020). Train-the-Trainer Concept on Research Data Management (3.0). Zenodo..

© This work by Hendrik Gessner is licensed under CC BY 4.0.
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