1. Introduction to research data management

Research data comprise the digital or non-digital sources or material created or compiled for the purpose of conducting a research project. The analysis of these data forms the basis for answering the research question. Research data can be classified into three categories: collected data, produced data, and reused data. The origins, types, and characteristics of data are discussed in chapter 2. 

Research data management is a key part of responsible conduct of research (in Finnish hyvä tieteellinen käytäntö). It supports the transparency and replicability of research and its results. Proper data management also makes it possible to verify the research results during, for example, peer review of scientific articles. In addition, according to UEF Open science and research policy, research data should be properly managed and, whenever possible, made openly available. This must be always done in line with research ethics and the legal framework, including copyright laws and agreements. For research ethics and legal framework, see further chapter 2 of this learning material. 

Description of data is an essential part of research data management, ensuring that the data remain understandable both during and after the research project.  Careful documentation makes it possible, for example, to verify research results and to enable the reuse of the data. Descriptive information typically explains what the data contain, where and how they were collected, who collected them, and where they are stored. This is commonly discussed in terms of metadata and documentations, which are examined in more detail in chapter 3 of this learning material. 

The management of research data can be planned by creating a Data Management Plan (DMP). Data management plan is a living document that is continuously updated and can be done by using existing templates and tools such as DMPTuuli.  

In essence, the goal of research data management is to ensure that both the research process and the use of research data responsible and efficient, while also meeting the requirements set by the university, funders, and regulations (including legislation and contracts). 

Up-to-date guidance on research data management is available on the UEF Data Support website. The preparation of a DMP is discussed in more detail in chapter 6 of this learning material. 

The research data lifecycle describes the activities related to the data at different stages of the research process. It is often illustrated as a circular model to highlight the continuous nature of data: there is not necessarily definitive endpoint, as data can remain active through reuse.

As shown in the figure below, the lifecycle typically includes phases such as planning, collecting, analysing, storing, publishing, disposing, discovering, and reusing data. However, it is important to note that data management activities do not always follow this model. For example, collecting or publishing data can occur at multiple stages during the research process. Storing, sharing, and publishing are discussed in more detail in chapter 4 and chapter 5.

At the beginning of a research project, it is advisable to plan data collecting as carefully as possible. This includes paying close attention to relevant legislation and ethical considerations. Researchers should agree on data management practices with their collaborators and regularly update the data management plan as needed.

Data should be documented, described, and stored to ensure that they remain both usable and secure during and after the research. Once data have been collected, they are analysed, and the findings are reported, for example, in a journal article.

At the end of the research project – or afterwards – the data may be shared with others or archived in accordance to the guidelines of the research organisation or partners. The data lifecycle continues: if the data are openly available, they can be reused for new purposes, such as research, teaching, learning, or commercial use.

Throughout all stages of research, systematic data management provides a strong foundation for high-quality research and supports the effective implementation of the research project.

“Data life cycle diagram” by RDMkit / CC BY 4.0

Adhering to the FAIR-principles is an essential part of research data management. FAIR principles introduced in 2016 by the FORCE11 initiative (The Future of Research Communications and e-Scholarship) provide guidance for producing and managing both research data and their metadata (i.e., descriptive information about the data) 

• Findable 
• Accessible 
• Interoperable 
• Reusable.
  

Applying the FAIR principles in practice involves the following:

• Findable: Research data can be easily discovered through search services, data repositories, and publication channels. Findability is supported by comprehensive description (metadata), the use of a unique persistent identifier (PID), andby registering or storing the data in a data service that assigns such identifiers. 
• Accessible: Data and/or their metadata are openly available when possible. Accessibility is typically enabled through a persistent identifier. Importantly, metadata should remain accessible even if the data cannot be fully shared or if they have been deleted after the retention period.
• Interoperable: Research data can be used across different environments and over time. Interoperability is enhanced by using open formats, open-source software, by favouring open, and machine-readable vocabularies and code. Linking data or metadata to related outputs or earlier versions also supports interoperability. 
• Reusable:Research data can be reused for new research or other purposes in accordance with the terms of use of the data. Reusability is ensured through thorough documentation and clearly defined access rights.
  

Strictly adhering to all FAIR principles is not always feasible. It is therefore important to consider the specific limitations and opportunities associated to the data. Moreover, following FAIR principles does not necessarily mean that the data must be openly shared.

A range of data management services, such as CSC’s Fairdata.fi, have been developed to support the implementation of FAIR principles. In addition to selecting appropriate services, researchers should reflect on what practical steps they themselves can take to make their data as FAIR as possible. 

Research support website (UEF Intranet, requires UEF login)

Data management guidelines (Finnish Social Science Data Archive)

Data management checklist (Fairdata.fi)

CSC: Data management and storage CSC’s data management and storage web page introduces data services and offers useful information about data management.

Responsible Research Coordination for Open Science, Publication Forum, The Committee for Public Information (TJNK), Finnish National Board on Research Integrity TENK and the Responsible Research Articles.