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03 Data Management Plans

What?

A data management plan (DMP) describes the intended treatment of research data. This includes activities during the research process as well as after completion. The DMP contains all information describing and documenting the collection, preparation, storage, archiving and publication of research data. In length, a DMP can vary from a few paragraphs to several pages.

When writing a DMP or project proposal...

  1. Contact your RDM team. We offer review of DMP and advise on available tools.
  2. Budget the cost of research data management in your proposal. This tool may help with estimations.

Motivation

A data management plan ties up resources in its creation while providing many benefits. A data management plan:

  • creates a binding foundation for consistent handling of data in the research process
  • facilitates the understanding of one's own data
  • facilitates coordination between project partners
  • helps to identify potential problems at an early stage and outlines solutions for them
  • defines responsibilities
  • regulates access rights
  • helps to avoid data duplication, data loss and security gaps
  • is usually part of a funding application

Data management plan

CC-BY 4.0: Scriberia

Thinking from the end

A good preliminary consideration for a data management plan is to think the process backwards: where and how should the data be archived or published? These considerations make it necessary to set the course early on in the data management workflow, for example, with regard to supported formats, standards, metadata, licenses, etc.

Content

Data management plans vary widely depending on the size of the project and the variety of data. It is important to consider recommendations and requirements from third parties, such as funding agencies or employers, when creating them. A practical guidance on core aspects to be covered in DMPs has been published by Science Europe in 2021. The most commonly used components of DMPs are:

Administrative Information
  • project name
  • principal investigator (ORCID, contact information)
  • responsible person for data management (ORCID, contact information)
  • funder information
  • relevant policies
  • description of project
Data description and collection or re-use of existing data
  • kinds of data, file formats , volume of data collected or produced
  • data reused
  • methodology of data collection, hardware and software used
Documentation and data quality
  • metadata (standards) and documentation
  • data organisation (naming, folder structure, versioning)
  • quality assurance/control
  • potential reproducibility
Storage and backup during the research process
  • data and metadata storage processes and infrastructures
  • backup strategies
  • data security and protection
  • data migration
Legal and ethical requirements, codes of conduct
  • usage and access rights
  • intellectual property rights and ownership, licences
  • security of personal data (pseudonymisation, anonymisation)
  • embargo , cooperation agreements
  • possible ethical issues
Data sharing and long-term preservation
  • time and way of data sharing
  • restrictions for sharing
  • place of long-term preservation (repository, archive)
  • methods and tools necessary to access and use the data
  • identifiers for persistant identification of data
Data management responsibilities and resources
  • responsible person(s) for data management (steps)
  • responsible person(s) for update of and compliance with DMP
  • financial and time resources dedicated to research data management

The diversity of research data as well as the handling of it determines the length of a data management plan. It should be short, specific, and agreed upon with all project stakeholders. An incomplete DMP is better than none at all. Changes to the plan are not uncommon and updates are therefore necessary. Ideally, a data management plan evolves dynamically: i.e., it is continuously updated and expanded during the course of the project and thus evolves from an outline to a detailed documentation of the data management process (active Data Management Plan) and thus contributes to the reusability of the data.

Example DMP of different length and detail

Funding Requirements

In Germany, data management plans are already required by a number of research funders at the application stage. Research funders such as the European Commission (European Commission (EC), the German Research Foundation (DFG), and the Ministry of Education and Research (BMBF) increasingly expect the provision of a data management plan at the start of funding, information on the handling of research data to be collected, and - depending on the funding guideline - an exploitation plan for project results or a detailed data management plan at the time of application. The requirements for data management plans can be found in the following table:

Funder DMP required? Required on application? Content Updates?
EC Horizon Europe Yes No, within first 6 months Horizon Europe Template Yes, when significant changes occur and at end of project
DFG Yes Yes Guidelines on Research Data, Checklist No
BMBF Depends on program Yes Depends on program No

Tools, Templates, and further Resources

You'll find an overview of tools, templates and further resources in the wiki of forschungsdaten.org (german only). Here we will list only what we consider to be the most important resources.

Templates

  • DMP EU Horizon 2020 (v3.0)
  • DMP DFG
  • DMP BMBF
  • Stamp: The Stamp (Standardisierter Datenmanagementplan für die Bildungsforschung) is a living document that provides concrete, standardized instructions for planning and implementing data management measures.

Checklists

Tools

Recommendations for a Jump Start

Jump start

make a DMP
use either a DMP template or tool