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Research Data Management: Data management plan (DMP)

Write a data management plan (DMP): what, why, and how?

A written data management plan (DMP) is an important part of research data management, an essential tool for following good research practices and opening up your data. A researcher carrying out research should create a DMP before he/she starts his/her research project. In addition, the plan should be updated as the research project evolves. It is a living document that accompanies the whole research life cycle, even after the active phase of the research project.

A DMP accompanying funding applications to the Academy of Finland and several other research funders must cover the collection and processing of data, ethical compliance, ownership and rights to use, short-term storage and backup, long-term preservation and reuse, planned disposal and the associated resource needs. The plan can be 1-2 pages. More information, see DMP guides and checklists on the right.

The advantages of data management planning include the following:

  • facilitating the citation and reuse your data to increase the impact of your research.
  • supporting open access and promoting new discoveries and future collaborations.
  • ensuring that your data is consistent with the FAIR data principles.
  • saving time and money.
  • reducing the risk of losing data.
  • meeting funder and other policy requirements.
  • maintaining and ensuring data protection and data integrity.
  • avoiding ownership and user rights problems in advance.
  • ensuring that the necessary resources and equipment are available.

The DMP is part of a research plan. You can refer from one document to the other in order to avoid overlap between them. Introduce data analysis and other methods in your research plan.

In the DMP, data is understood as a broad term including:

  • data collected by various methods (such as surveys, interviews, measurements, imaging techniques etc.),
  • data produced during the research (such as analysis results),
  • research sources (such as archive materials), and
  • source code and software.

You can use the DMP tool DMPTuuli or  DMP templates to help you write a DMP. See DMPTuuli and templates as follows.

 

DMPTuuli and templates

The Tuuli (DMPTuuli) tool is helpful when writing your data management plan. It provides guidance for writing a data management plan at all stages. The Ministry of Education and Culture's Tuuli project is responsible for DMPTuuli.

You can find Public DMP templates to meet the specific funder requirements also on DMPTuuli pages. DMPTuuli contains useful advice and tips for all organizations and all types of applications, including:

DMP guides and checklists

Although there may be some differences depending on research domain, in general, a good data management plan will address the following seven aspects:

1. General description of data: Data types and formats, estimated data size; and how to control the consistency and quality of data.

Note that it is important to identify all sensitive, personal, and confidential data types. See Ethical review and Data protection.

2. Ethical and legal compliance: Follow Hanken's ethical guidelines and data protection policy to maintain high ethical standards and comply with relevant legislation.

  • Ethical concerns: eg., how to handle sensitive and personal data, how to gain data-sharing consent from research participants, if your study needs to apply for an ethical review, and research ethics.
  • Legal issues: eg., data protection policy, data usage rights, data-sharing agreements, data ownership, copyrights, licenses, and other Intellectual Property Right (IPR) issues. Agreements on data ownership and other intellectual property rights must be concluded before commencing any actual research activities. See Copyright and Agreements by Finnish Social Science Data Archive (FSD) and Open Data by Creative Commons (CC).

3. Documentation and metadata: How to describe your data and if you use some metadata standards to make your data findable, accessible, interoperable, and reusable for you and others. More information, see:

4. Data storage and backup during the research project: Where and how to store and back up your data, data security, and access control. See Data storage and backup.

5. Data publishing and sharing after the research project: What part of the data to be made openly available or published, where and when to make the data or their metadata publicly available. See Data sharing and preservation.

Note that data with personal information can only be published anonymised. Pseudonymised data is still personal data, and therefore cannot be opened without explicit consent for that purpose. Se Anonymisation and Personal Data by Finnish Social Science Data Archive (FSD).

6. Defining roles and responsibilities of research team members: Who is responsible for data management tasks including data protection, information security, data documentation, data archiving and publishing? 

7. Estimating resources required for data management: time, workload, and possible costs.

 

More information about how to create a DMP, see:

You can use the DMP tool DMPTuuli or DMP templates to help you write a DMP. See DMPTuuli and templates on the left.

Also see: