In the data management plan (DMP), you document how the data will be produced and used in research, a project or a thesis. In addition to the project/research plan, the data management plan is a separate document that covers the entire life cycle of the data and must be updated as the research progresses.
A data management plan is done in all RDI projects of Turku University of Applied Sciences. Careful planning of data management makes it possible to safely process even sensitive data throughout its life cycle, including post-project reuse.
The National Archives of Finland has ordered data management plans to be archived permanently.
In the tabs below, you will find the main principles and tools related to data management, as well as instructions on how to fill in a data management plan.
In a data management plan, you document how the data will be produced and used in the project or other activity all through the data's life cycle. The data management plan is a document that is updated as the research progresses. The data management plan is commonly referred to as DMP.
In your data management practices take into account
In an externally funded project, you ensure with a data management plan that:
Agree with others on data management in the preparation phase of the project or at the beginning of the project in project consortia, especially in business projects, to avoid problems later. A data management plan plays a major role in meeting data protection requirements and can be used to agree on a joint controller more flexibly, for example.
At its best, a data management plan is a living document that takes into account the project's data management needs and the reuse of RDI data even after the project. Even if the funder has not required you to write a data management plan, it is a convenient way to agree on matters among the research group and ensure the impact of the project's results.
The data management plan you will cover the following issues, among others:
With good management of research data, its life continues:
Data management plan describes the events in the data's life cycle:
Data management and conducting research are separate, interdependent processes. All research-related measures and decisions affect how data management is planned and implemented.
The key stages of conducting research are
All the stages of research require data management.
Image: Research life cycle (Think Open blog, University of Helsinki IT services, What is research data management (RDM)? 2020, CC BY, edited).
Many people may think that the study ends when the research results are published. After that, however, you can continue your research based on the research results and the collected research data, if you have agreed on further use of the data. Alternatively, the data can serve as a starting point for a completely new kind of research.
In universities of applied sciences, research is usually carried out as part of externally funded RDI projects. In this case, preparing for research includes making a project plan and applying for funding. The research will only be launched if the funder decides to support the project. Externally funded research ends when the received funding ends. Ending the study includes reporting the research process and the research results to the funder.
Image: Research data life cycle (ATT project 2020, CC BY, edited).
Research data life cycle is often longer than the life cycle of the research that produced it. If the research was carried out as part of an RDI project, data management has to be continued even after the project has ended. For example, a funder may require that the data collected in the project is stored for 5 years after the end of the project. Well-implemented data management is a strong foundation for new projects.
Research can also be carried out as part of a thesis. The thesis process at Turku University of Applied Sciences has been described in more detail on the Thesis page in Messi intranet.
To support the reuse of data shared open access it should follow the FAIR principles. Research data that is FAIR is Findable, Accessible, Interoperable and Reusable.
Image: SangyaPundir, CC BY-SA 4.0.
In data management this means that possible further use of the data must be considered even before the data is collected. You are following the FAIR principles when:
1) The data has extensive metadata that can be found in a public search service, such as research.fi.
2) The data has a unique and persistent identifier, PID (Persistent Identifier).
3) The data or its metadata can be retrieved using an open and free connection policy.
4) Open file formats have been used in the saving of the data, e.g. .csv, .odf.
5) The data has a clear use license that describes how it can be reused.
The data management plan is written in the DMPTuuli service. The head of research ensures that the plan is written and updated as necessary. The final version of the data management plan will be saved in the Dynasty case management system at the end of the project at the latest. If the funder requires more than one version of the data management plan, these versions are also stored in Dynasty with clear names.
DMPTuuli:
DMPTuuli consortium is responsible for the administration of DMPTuuli. Turku University of Applied Sciences is a member of the consortium. DMPTuuli is based on a tool created by the University of Edinburgh and the University of California that has later been customised to suit the Finnish scholarly context.
DMPTuuli's user interface and instructions are mainly in English. The General Finnish DMP has instructions in English and Finnish. You can also use data management plan templates created by other users in the tool.
In DMPTuuli, most data management plan templates are divided into six parts that must be filled in according to the best of your knowledge and updated if the information changes.
DMPTuuli's General Finnish DMP template guides you through the plan with the following questions:
Turku University of Applied Sciences follows good data management practice, which is part of the responsible conduct of research. Good data management practices mean that the data and their metadata keep the data usable, reliable and secure throughout its life cycle.
Successful RDI data management is done systematically. The costs related to data management must be taken into account in the project budget. At best, RDI data both support the project's goals and is shared with parties outside the project for reuse.
Each member of the Turku University of Applied Sciences complies with ethical research guidelines in data management and ensures confidential information is protected in accordance with legislation and responsible conduct of research. The project leader is responsible for ensuring agreements on the ownership, use, and confidentiality of the data generated in the research are made as early as possible, if applicable, even before the start of the research project. The details related to these agreements are also recorded in the data management plan and in the metadata of the research data.
In large and data-intensive projects, it may be necessary to appoint a dedicated data management officer (DMO) for the project, who is responsible for preparing, monitoring and updating the data management plan as well as managing the data in practice.
A consortium agreement is a good place to agree on the ownership and use rights of RDI data. The agreement must take into account the uncertainties related to the data and find a balance between a detailed proactive agreement and flexible plans that change according to the situation. The consortium agreement should always reflect the funder's intentions and funding terms, which provide a ready-made framework for the agreement.
This guide covers the Turku UAS instructions on data management.
You don't necessarily have to collect all the data yourself. Suitable open data may already be available, e.g. register data or open data available from various data repositories. e.g.
FAIR principles have been developed in extensive, international research collaboration and they were published in 2016. The FORCE11 Group participated in commenting on the FAIR principles and the group shares information to support their implementation. The Council of the EU has issued a policy on compliance with the principles, and the Finnish Ministry of Education and Culture is committed to complying with them.
The policy for open research data and methods was published in two parts. The first policy component on open access to research data was published in 2021 and the second policy component on open access to research methods was published in 2023.
The Research Council of Finland's website provides information on research infrastructures and related funding opportunities.
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