Research data management planning prior to research

Research data refers to material produced or utilised during research on which the research results are based. There are many types of research data, such as numerical data, text, images, recordings, videos, survey materials, software, source codes, algorithms, measurement results, samples or notes. 

In the Helsinki Term Bank for the Arts and Sciences (definition in Finnish), research data or research material is defined as a resource produced by a researcher or used during the research process, in digital, analogue, or physical form, on which the findings and results of the research are based. Research data can also be understood more broadly as a collection that includes important descriptions and documentation of, for example, the origin, processing, content, or technical characteristics of the data.

Research data can form significant research infrastructures. 

 
Research data management refers to procedures which ensure that research data is easy to access and interpret, appropriately protected, and available for further use. 

A Data Management Plan (DMP) is a document that describes how research data is collected, stored, and handled. It outlines the entire lifecycle of research data, detailing what happens to the data during and after the research. The data management plan is a part of the research plan.

Research data policy, or data policy, is organisational guidance on data management, outlining the principles guiding research data management.
 

Good data management is part of good scientific practice. High-quality and reliable research is based on accurate and comprehensive research data, the management of which is well-planned and carefully implemented.

 
Data management ensures high-quality and reliable research results, minimises risks and enhances the reuse of data. Thus, data management includes measures to ensure that the research data and the associated metadata are easily discoverable, interpretable, usable and appropriately protected throughout the life cycle of the data. It is advisable to keep in mind that the life cycle of research data may be hundreds of years longer than the life cycle of the research project that originally produced the data in question. Moreover, the subsequent user of the data may be, for example, a teacher or an authority instead of a researcher.

 
Practical measures in data management include defining user rights, applying for the necessary permits or statements, backing up, documenting procedures, publishing the data or making them fit for the needs of a specific data archive or repository. For more information about these actions, see the different sections of this website, which also provide tips and links to additional information outside of these websites. 
 

Benefits of data management in a nutshell 

Investing in data management planning brings many benefits: 

• Quality: The research material remains accurate and comprehensive when, for example, files and folders are clearly named and different versions are saved and named logically. 
• Legislation: Data management planning guides you to consider, for example, the requirements of data protection legislation.  
• Risk management: The risk of sudden destruction of research material is reduced as data security and backups are managed systematically. 
• Meeting requirements: data management makes meeting the requirements of the funders and the policy of the research organisation easier. 
• Resources: Time and other resources will be saved as the research process progresses. 
• Transparency in science: Storing research data in a repository secures the research output and enables it to be made open for others to use. 
• Merit: Making high-quality research data available to others increases the impact of research and promotes new and interdisciplinary cooperation opportunities. 
• Competence: Data management skills can also be utilised outside research as general information management skills. 

FAIR is an acronym for the words findable, accessible, interoperable, and reusable.

FAIR is sometimes equated with open (research) data. However, despite their overlap, the concepts of FAIR and open data are separate. The FAIR principles can be followed even if a certain data set cannot be opened for re-use. For example, if access to research data is restricted due to privacy concerns, this is clearly communicated in the metadata and/or as part of the Data Availability Statement document. (For more information, see the section on Opening and publishing research data).

FAIR principles can be implemented, for example, by:
•    assigning a persistent identifier to research data (e.g., DOI or URN), e.g. data publishing services assign persistent identifiers (F - Findable)
•    using free and open file formats and software (A - Accessible)
•    utilising open and machine-readable vocabularies and codes (I - Interoperable)
•    clearly stating usage rights (R - Reusable)
•    creating comprehensive description and documentation, ensuring high-quality metadata that remains openly accessible even after the data storage period (F - Findable, A - Accessible, I - Interoperable, R - Reusable).

In addition to research data, the FAIR principles can be applied to algorithms, code, software, and various stages of the research process. The focus of FAIR measures is on optimising the machine findability and interoperability of research data.

The FAIR measures were published in 2016, read more from Wilkinson et al. article. Today several research funders encourage researchers to process research data in accordance with the principles, see for example, the Academy of Finland's guidelines.

Test your FAIR expertise with the FAIR-Aware tool. It consists of ten questions with additional instructions. Answering the questions takes approximately 10–30 minutes. This tool was developed by the Dutch national data center (DANS) as part of the FAIRsFAIR project.

Data management is not self-generated. It takes at least time and perhaps also particular knowledge and money. Before starting the research, it is important to clearly define the tasks, responsibilities and rights related to data management of the participants in the research project. In particular, the ownership, user rights and further use of the material must be agreed upon.

The needs for the data management resources, such as time, money and training, are already assessed when planning the research. Realistic resource planning helps during the research and thereafter, for example, when the publication or storage of research data becomes topical.  

The time reserved for data management planning is already part of the resource allocation. Otherwise, the allocation of resources can be assisted by a division that assesses the need for time, competence and money in the research data  

• collection (reuse, equipment requirements, arrangement, processing, research permits, etc.),  
• description and documentation,  
• storage, sharing and data security during research,  
• publication in connection with a research publication (including anonymisation or  
• post-study preservation (incl. anonymisation). 

There are excellent and more detailed tips and additional help for resourcing (e.g. the Costs of data management website of the University of Utrecht or the Costing website of the UK Data Service).   

When allocating resources, it is important to check the terms and conditions of the funder, as they do not necessarily provide separate funding for the costs of preserving and opening research data. For example, the terms and conditions of the Academy of Finland state that, as a rule, these costs are included in the general costs of the research project's host organisation.  

On the UEF Research datasets page, you will find the main datasets and databases used at the University of Eastern Finland.

A Data Management Plan (DMP) is a complementary document to the research plan, outlining data management procedures. It is often a concise document of a few pages, but its length may vary according to the funder's guidelines. Through the data management plan, researchers can conceptualize the entire lifecycle of research data, mitigate data-related risks, and ensure the ethical, secure, and efficient use of research data during and after the study.

The key guidelines and principles of the data management plan should be considered in the beginning of a research project, and further refined during the research project. University of Eastern Finland’s Open Science and Research Policy state that the data management plan should be prepared in the planning phase of the research and.updated when necessary.

Funders may also have their own requirements regarding the data management plan. If a funder requires a DMP approved by the home organisation of the researcher or of the research project, UEF Data Support provides a DMP review service for this purpose.

General or funder-specific templates and guidelines are available for creating a data management plan. In the open learning materials produced by the UEF Library, the process of creating a data management plan is discussed in more detail, utilising the national guidance for data management plans and its template.

DMPTuuli is a tool for writing data managements plans, more information below.  Another example of DMP tools available to researchers is the Data Stewardship Wizard (DSW). It provides guidance and prompts to support the implementation of FAIR principles. The use of DSW is recommended, among other places, in the guidance for the EU's Horizon Europe program. This English-language tool has been developed in collaboration with Dutch (DTL/ELIXIR NL) and Czech (CTU/ELIXIR CZ) research organizations. 

UEF Data Support also organises workshops for writing data management plans. For more information, please refer to the section Data Management Courses and Guides on this website.

More information

UEF guidelines for Writing a Data Management Plan and General Finnish DMP Guidance (in UEF intra, requires UEF login).

General Finnish DMP guidance (2021).

 Additional instructions for planning the management of sensitive and confidential data (2019).

Research infrastructures play a central role in producing research data. Individual infrastructures can also consist, for example, of existing data collections.

Data Management Policy (DMPol) is a document that guides the management of data for parties utilising research infrastructure (e.g., researchers, technical staff, clients). The data management policy provided by research infrastructure can be utilised in drafting data management plans for individual projects.

In the data management policy, the main principles of data management for research infrastructures should be described, such as information on the quantity and type of data produced or aggregated by the infrastructure, access rights, and storage options. According to the University of Eastern Finland’s Open Science and Research Policy, DMPol is to be prepared during the planning phase of the infrastructure or at the latest when applying funding.

The UEF Data Support service also includes DMPol consultation. UEF Data Support is developing materials and training to support the creation of data management policies.

For more information:
Data management policy for research infrastructures. Research Council of Finland.

The research infrastructures of the University of Eastern Finland. Information about UEF research infrastructures and their development program.

Kuusniemi, M.E., Siipilehto, L., Ojanen, M., Korhonen, T., Manninen, S., Leskinen P., Ellmén, U., Särkioja M. (2022). Data management policy (DMPol) template for research infrastructures. Zenodo. 

UEF Data Support can provide feedback on data management plans (DMPs) and data management policies (DMPols). You can submit the document to the Data Support service address datasupport@uef.fi. Include either research abstract or research plan, and if necessary, guidelines from the funder or another entity that you must adhere to in the DMP or DMPol being submitted for comments.

For supplementary funding projects (e.g., Research Council of Finland or Horizon Europe), there is a prepared DMP process that aligns with the funding decision timelines.
• Data management plans (DMPs) for supplementary funding research projects at UEF must be submitted to UEF Data Support for comments before sending them to the funder. These projects require that the host institution is committed to data management as planned in the data management plan, so the host institution must be aware of the planned actions.
• The DMP is sent as a file to datasupport@uef.fi address, along with the funding decision number and a brief description of the research or research plan. 
• UEF Data Support experts review the DMP according to national evaluation guidelines and consult other university experts if necessary (IT services, data protection officer, legal experts).
• UEF Data Support provides feedback to the researcher on the DMP and provides guidance for any necessary amendments. UEF Data Support marks the reviews in a checklist. The researcher submits the DMP to the funder following the funder's guidelines. The issuer of the host institution's commitment (department head/dean) verifies, before making the commitment, that the DMP has undergone the commenting process.
•  The DMP is attached to the research plan and archived along with the documents received for funding application/receipt. However, the DMP is a living document, and it is the responsibility of the researcher to ensure its currency.
 

DMPTuuli is a web-based tool for writing data management plans (DMPs) and data management policies. The tool is openly available for use by every researcher and research group. DMPTuuli is tailored to the needs of Finnish research organizations based on the open-source DMPonline tool from the Digital Curation Centre (UK).

DMPTuuli includes guidelines and template plans specific to funders and organizations. It offers ready-made templates for data management plans for research funded by the Research Council of Finland, the Finnish Cultural Foundation, and the European Commission's Horizon Europe program. Additionally, DMPTuuli provides a template for the Research Council of Finland’s FIRI application data management policy (DMPol).

Users can choose the necessary guidelines right at the beginning of plan writing. Plans can be shared and collaboratively written with other researchers. Furthermore, users can set the visibility of the plan as private, organization-specific, or public. The finalized data management plan can be saved as a file in various formats (e.g., docx, pdf).

Access to DMPTuuli requires logging in with organisational credentials (e.g., UEF accounts) or separate registration.