Data management plan

Data management plan

A data management plan (DMP) is a formal document that outlines how data will be collected, organized, stored, documented, and eventually shared or preserved throughout the lifecycle of a research project. When applying for funding, applicants are often asked to provide a short summary of plans for data management, with some funders (e.g. Horizon Europe) requiring a full data management plan be completed if the grant is awarded.

DMPs help researchers plan and organize their data-related activities, ensuring data is available and usable throughout the project. It is important that data handling practices comply with ethical standards, institutional policies, and legal regulations, such as data protection laws.

When data is collected the DMP supports data sharing and transparency, which can lead to increased collaboration and the reproducibility of research findings. Data needs also be preserved for the long term, which is essential for future reference and verification of results

Who requires Data management plans?

The demands for researchers to create data management plans stem from a combination of funding agency requirements, institutional policies, legal and ethical standards, research best practices, collaboration needs, and specific journal guidelines. Researchers should be aware of these demands and tailor their DMPs to meet the specific requirements of their research projects and the organizations or entities funding and overseeing their work. Exact requirements will differ between funding agencies and also between programs within the same agencies.

  • RannIs (Icelandic Research Fund) grant applications require an overview of data management related to the proposed research plan. Rannís does not currently require a formal DMP to be submitted before the project starts.
  • NordForsk states that research projects should pay special attention to implementing good data management practices when managing, sharing, and linking data across borders and suggest using the FAIR principles as inspiration. Applications should include plans to make research data and results openly available in accordance with NordForsk’s policy on Open Access.
  • Horizon Europe requires close consideration of data management at proposal and project stages. Precise requirements vary by call and need to be reviewed carefully.

Contents of a data management plan

Whilst each funder specifies particular requirements for the content of a plan, common areas are:

  • Which data will be generated during research.
  • Metadata, standards and quality assurance measures.
  • Plans for sharing data.
  • Ethical and legal issues or restrictions on data sharing.
  • Copyright and intellectual property rights of data.
  • Data storage and back-up measures.
  • Data management roles and responsibilities.
  • Costing or resources needed.

Notes on the implementation of data management plans

It is crucial when developing a data management plan for researchers to critically assess what they can do to share their research data, what might limit or prohibit data sharing and whether any steps can be taken to remove such limitations.

A data management plan should not be thought of as a simple administrative task for which standardised text can be pasted in from model templates, with little intention to implement the planned data management measures early on, or without considering what is really needed to enable data sharing.

A big limitation for data sharing is time constraints on researchers, especially towards the end of research, when publications and continued research funding place high pressure on a researcher’s time. At that moment in the research cycle, the cost of implementing late data management and sharing measures can be prohibitively high. Implementing data management measures during the planning and development stages of research will avoid later panic and frustration. Many aspects of data management can be embedded in everyday aspects of research co-ordination and management and in research procedures.

Good data management does not end with planning. It is critical that measures are put into practice in such a way that issues are addressed when needed before mere inconveniences become insurmountable obstacles. Researchers who have developed data management and sharing plans found it beneficial to have thought about and discussed data issues within the research team.  Key issues include:

  • Know your legal, ethical and other obligations regarding research data, towards research participants, colleagues, research funders and institutions.
  • Implement good practices in a consistent manner.
  • assign roles and responsibilities to relevant parties in the research design data management according to the needs and purpose of research.
  • Incorporate data management measures as an integral part of your research cycle.
  • Implement and review data management throughout research as part of research progression and review.

Costing data management

To cost research data management in advance of research starting, e.g. for inclusion in a data management plan or in preparation for a funding application, two approaches can be taken.

  • Either all data-related activities and resources for the entire data cycle – from data creation, through processing, analyses and storage to sharing and preservation – can be priced, to calculate the total cost of data generation, data sharing and preservation.
  • Or one can cost the additional expenses – above standard research procedures and practices – that are needed to make research data shareable beyond the primary research team. This can be calculated by first listing all data management activities and steps required to make data shareable (e.g. based on a data management checklist), then pricing each activity in terms of people’s time or physical resources needed such as hardware or software.