Researcher's guide to responsible and open science

Well-managed and processed research data is fair. Handling data in accordance with the FAIR principles is a part of responsible data management, as FAIR is related to skills that increase the reliability of data and research. The principles emphasise enabling the reuse of data, but also understanding the protection of data and restrictions on its use when necessary. Research data does not have to be completely open access to be FAIR, but the data is as open as possible and as protected as necessary.

FAIR means that the research data is:

• Findable
  • The data receives a unique and persistent identifier (e.g. DOI), which makes it easy to find.
• Accessible
  • The data has been described in such a way that it can be accessed and found, for example, with a web browser using different search engines.
  • User identification and requesting potential access rights have been made easy.
• Interoperable
  • The file formats of the data are open.
  • The data complies with common standards.
  • This ensures that the data will be genuinely available also in the future.
• Reusable
  • The terms of use and licenses of the data are clearly defined.
  • The content and structure of the data have been described in sufficient detail to enable its use.

Take a closer look at the FAIR principles and use them as a guideline for your own data management work.

More about FAIR: Improve the quality and impact of your research through data management - A guide for making your data FAIR (Zenodo).

A data management plan describes how the research data will be managed throughout the research lifecycle and what will happen to it after the research is concluded. A data management plan focuses specifically on the technical and administrative handling of data, whereas a research plan approaches data from a methodological perspective. Start planning right at the beginning of the research project. The first version of a data management plan is "the best guess for the future". The plan should be updated as things become more known or change as the research progresses.

The management of research data and the preparation of a data management plan are good scientific practices. Drawing up a plan is also worthwhile from a practical point of view of, because

• by planning, you save time and money.
• a data management plan drawn up in advance reduces the risk of loss or destruction of data.
• with a plan, you'll be able to anticipate and manage details related to ownership and permissions.
• making data open access requires planning.
• planning helps in following funder policies.

Tampere University's open science policy highlights the importance of good data management. Many funders also require that a data management plan is created at the latest when funding has been received.

The drafting and structure of a data management plan

We recommend that you write your data management plan using DMPTuuli.

Create an account for yourself in the service and link it to your HAKA account. After this, the HAKA login will also work in DMPTuuli. In DMPTuuli you can find, for example, a template for Tampere University's data management plan and funder templates. Our University community's data management guidelines have also been integrated into the tool. We strongly recommend using the Tampere University plan template, as it is tailored to meet the needs of our organisation.

You can find the Tampere University template in DMPTuuli as follows:

• Go to the “Create plans” tab.
• Choose Tampere University as your organisation.
• Tick the box for “No funder associated with this plan or my funder is not listed”.
• Select “Tampere University DMP guidelines”.

Create a data management plan from the perspective of your own research project. Describe things concretely and write in active voice. Demonstrate in your plan that you can identify, anticipate, and manage risks related to the data management process. The template for the Tampere University data management plan has tick boxes, which means that the plan can be made with relatively little writing. Alongside the tick boxes there are also fields for free-form text where you can and should provide additional information.

At the beginning of the data management plan, include background information such as the name of the project, a brief summary of the project, funding information, and potential grant numbers. The actual structure of the plan may vary for example depending on the requirements of the home institution or funder. The General Finnish DMP guidance lists the themes of a data management plan as follows:

1. General description of the 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
6. Data management responsibilities and resources

Many funders have requirements for good data management. A data management plan is required either already when applying for funding or at the latest when funding has been granted. Here are some examples of funders' requirements:

• Bill & Melinda Gates Foundation:
  • considers that the FAIR principles contribute to the widest reuse of research data.
• Business Finland:
  • The recipient of funding is obliged to take care of the data generated by the research in such a way that the objectives of the project can be achieved.
  • The project must prepare a data management plan that will be updated during the project.
• Horizon Europe:
  • The recipients of funding must submit a data management plan within 6 months of receiving funding.
  • For projects longer than 12 months, the updated plan must also be submitted midway through the project and additionally at the end of the project (if relevant).
  • The EU recommends that data management plans should be public documents and that they should be licensed under a CC0 licence.
  • Horizon Europe has its own data management plan template, which you can find for example in the DMPTuuli tool.
• Research Council of Finland:
  • At the application stage, all applicants must briefly describe the management of the data in the research plan.
  • The actual data management plan is made only after a positive funding decision, and the plan must be submitted to the Council’s SARA online service within eight weeks of the funding decision.
  • The consortium has a joint data management plan.
  • The Research Council of Finland has its own data management plan template, but you can also use the Tampere University template. We recommend using the Tampere University template.
  • At Tampere University, the Dean approves the data management plan sent to the Research Council of Finland. Approval process is as follows:
    1. Send your data management plan for feedback to Research Data Services at researchdata@tuni.fi.
    2. Research Data Services will give feedback on your plan.
    3. Make the requested changes and send the plan back to researchdata@tuni.fi.
    4. Research Data Services sends the plan to the Dean for approval.
    5. Upload the approved data management plan to the Council’s SARA online service.
• Finnish Cultural Foundation:
  • The applicant may attach a data management plan (optional), e.g. using the DMPTuuli-tool.