Detailed Academy data management plan guidelines and best practices in DMPTuuli

Data management plan questions, Academy of Finland guidelines and best practices

These detailed guidelines are the same as those published in DMPTuuli that concern calls by the Academy of Finland.


  1. General description of data
  2. Ethical and legal compliance
  3. Documentation and metadata
  4. Storage and backup during the research project
  5. Opening, publishing and archiving the data after the research project

1. General description of data

Academy of Finland guidelines

The qualities of data and the choice of file formats support researchers’ research activities and collaboration with other scholars. Both are important information regarding the best practices in opening and sharing research data.

Use standardised or validated protocols of data collection and standard data types to ensure data sharing and reuse. The types of data to be stored and archived depend on the type of research and the scientific discipline. Describe the details on the data collection and analyses in the research plan.

Data quality measures over the entire data life cycle will minimise the risks of data errors and inaccuracies.

DMPTuuli guidelines

1.1 What kinds of data is your research based on? What data will be collected, produced or reused? What file formats will the data be in?

Consider your DMP as a part of your research plan. The standalone readability of a DMP is not necessary. The DMP complements your research plan with a description of the technical management of your data. To avoid redundancy, refer to your research plan in your DMP and vice versa.

Briefly describe what types of data you are collecting or producing. Also explain what kinds of existing data you will use, for example, the types of texts, images, photographs, measurements, statistics, physical samples or codes.

Categorise your data so that you can refer to it later in the plan. That is, your answer to this first question can form a general structure for the rest of the plan, for example, A) data collected for this project, B) data produced as an outcome of the process, C) previously collected existing data which are reused in this project, and D) managerial documents and project deliverables.

List the file formats for each dataset. In some cases, the file formats used during the research project may differ from those used in archiving the data. List both. The file format is a primary factor in the accessibility and reusability of your data in the future.

Tips for best practices

  • Data analysis and methodological issues related to data and materials should be described in your research plan.
  • Examples of file formats: .csv, .txt, .docx, .xslx and .tif.
  • When listing the file formats you will be using, make sure to include any special or uncommon software necessary to view or use the data, especially if the software is coded in your project.
  • Use a table or bullet points for a concise way to present data types, file formats, the software used and so on.

1.2 How will the consistency and quality of data be controlled?

Explain how the data collection, analysis and processing methods used may affect the quality of data and how you will minimise the risks related to data accuracy.

Data quality control ensures that no data are accidentally changed and that the accuracy of data is maintained over their entire life cycle. Quality problems may emerge due to the technical handling, converting or transferring of data, or during contextual data processing and analysis.

Tips for best practices

  • Transcriptions of audio or video interviews should be checked by someone other than the transcriber.
  • Analog material should be digitised in as high a resolution as possible for accuracy.
  • In all conversions, maintaining the original information content should be ensured.
  • Software-producing checksums should be used.

2. Ethical and legal compliance

Academy of Finland guidelines

Ethical questions and intellectual property rights are key issues regarding the limitations on storing and opening research data. The Academy of Finland aims to maximise access to data and the reuse of data, but reminds that research data should be closed when necessary. Researchers need to find a balance between openness, privacy concerns, commercialisation and IPRs. They should also take into account the effects of the new Finnish data protection act (based on the EU’s General Data Protection Regulation) on data security, personal data processing and anonymisation.

Make the necessary plans and arrangements to solve possible ethical or legal issues that could affect data sharing. Details of the ethical issues, ethical committee statements and use of laboratory animals should be described in the research plan. In your DMP, describe only ethical aspects of data management.

DMPTuuli guidelines

2.1 What ethical issues are related to your data management, for example, in handling sensitive data, protecting the identity of participants, or gaining consent for data sharing?

Describe how you will maintain high ethical standards and comply with relevant legislation when managing your research data. Ethical issues must be considered throughout the research data life cycle.

For example, following the guidelines regarding informing research participants is considered an ethical requirement for most research. Moreover, if you are handling personal or sensitive information, describe how you will ensure privacy protection and data anonymisation or pseudonymisation.

Tips for best practices

  • Check your institutional ethical guidelines and data security policy, and prepare to follow the instructions that are given in these guidelines.
  • If your research is to be reviewed by an ethical committee, outline in your DMP how you will comply with the protocol (e.g., how to remove personal or sensitive information from your data before sharing them to ensure privacy protection).
  • See, for example, the Finnish Advisory Board on Research Integrity for more information about the responsible conduct of research.
  • See, for example, the European Code of Conduct for Research Integrity.

2.2 How will data ownership, copyright and IPR issues be managed? Are there any copyrights, licences or other restrictions that prevent you from using or sharing the data?

Describe who will own the data and how the ownership issues have been agreed. Describe who can issue permissions to (re)use the data.

Tips for best practices

  • Check your organisational data policy for ownership, right of use and right to distribute.
  • Ownership agreements should be made as early as possible in the project life cycle.
  • Also consider the funder’s policy on copyrights and IPR.
  • It is recommended to make all research data, code and software created within a research project available for reuse, for example, under Creative Commons, GNU, MIT or another relevant licence.

3. Documentation and metadata

Academy of Finland guidelines

Standardised data documentation throughout the research project creates effective links between the project and the whole scientific community, especially to enable the validation of results presented in scientific publications and the reuse of shared data. The data produced or used in the project need to be findable, identifiable and locatable with metadata.

DMPTuuli guidelines

3.1 How will you document your data to make them findable, accessible, interoperable and reusable for you and others?  What kinds of metadata standards, README files or other documentation will you use to help others understand and use your data?

Data documentation enables datasets and files to be found, used and properly cited by other users (human or computer). Metadata are essential information regarding the data, for example, where, when, why and how the data were collected, processed and interpreted. Metadata may also contain details about experiments, analytical methods and the research context.

Tips for best practices

  • Describe all types of documentation (README files, metadata, etc.) you will provide to help secondary users find, understand and reuse your data.
  • Following the FAIR principles will help you ensure the Findability, Accessibility, Interoperability and Reusability of your data.
  • Use research instruments that create standardised metadata formats automatically. Then your data can be moved from one manufacturer tool to another.
  • Consider how the data will be organised during the project. Describe, for example, your file-naming conventions, version control and folder structure.
  • Identify the types of information that should be captured to enable other researchers to find, access, interpret, use and cite your data.
  • Repositories often require the use of a specific metadata standard. Check whether a discipline/community- or repository-based metadata schema or standard (i.e., preferred sets of metadata elements) exists that can be adopted.

4. Storage and backup during the research project

Academy of Finland guidelines

Arrangements for storage and backup are important themes during the research project, especially if the amount of data is exceptionally large or the various data collected create a complex set of materials. You should describe your plans for securely and reliably storing data during the entire life cycle of the research project.

DMPTuuli guidelines

4.1 Where will your data be stored, and how will they be backed up?

Describe where you will store and back up your data during your research project. Methods for preserving and sharing your data after your research project has ended are explained in more detail in Section 5.

Consider who will be responsible for backup and recovery. If there are several researchers involved, create a plan with your collaborators and ensure safe transfer between participants.

Tips for best practices

  • The use of a safe and secure storage provided and maintained by your organisation’s IT support is preferable.

4.2 Who will be responsible for controlling access to your data, and how will secured access be controlled?

It is essential to consider data security issues, especially if your data are sensitive (e.g., personal data, politically sensitive information or trade secrets). Describe who has access to your data, what they are authorised to do with the data and how you will ensure the safe transfer of data to your collaborators.

Tips for best practices

  • Access controls should always be in line with the level of confidentiality involved.

5. Opening, publishing and archiving the data after the research project

Academy of Finland guidelines

Research data are important outputs of the public research funding provided by the Academy of Finland. Therefore, open access to all data produced with Academy funding is the default policy. Access and sharing of data helps increase the scope and outcomes of scientific discoveries, often beyond the initial boundaries of the original research project. Open data compilations are also merits for the scholars and research teams that have collected and opened them.

You should describe your plans for preserving the data after the project as well as specify the intended established and safe data repositories or databases. If you are unable to open your research data for possible reuse, please explain how and where you will open the metadata.

DMPTuuli guidelines

5.1 What part of the data can be made openly available or published? Where and when will the data, or their metadata, be made available?

Describe whether you will publish or otherwise make all your data or only parts of them openly available. If your data or parts of them cannot be opened, please explain why. The openness of research data promotes its reuse.

Tips for best practices

  • You can publish a description (i.e., the metadata) of your data without making the data themselves openly available, which enables you to restrict access to the data.
  • Publish your data in a data repository or peer-reviewed data journal.
  • Check to find a repository for your data.
  • Remember to check funder, disciplinary or national recommendations for data repositories.
  • It is recommended to make all research data, code and software created within a research project available for reuse, for example, under Creative Commons, GNU, MIT or another relevant licence.
  • Consider using repositories or publishers that provide persistent identifiers (PID) to enable access to the data via a persistent link (e.g., DOI, URN).

5.2 Where will data with long-term value be archived, and for how long?

Briefly describe what data to archive and for how long – as well as what data to dispose of after the project. Describe the access policy to the archived data.

Tips for best practices

  • Remember to check funder, disciplinary or national recommendations for data repositories.

5.3 Estimate the time and effort required for preparing the data in order to publish or to archive them.

Estimate the need to hire expert help to manage, preserve and share the data. Consider the additional computational facilities and resources that need to be accessed, and what the associated costs will amount to.

Tips for best practices

  • Remember to specify your data management costs in the budget.
Last modified 5 Jun 2018
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