1. Introduction to research data management

Research data are the original, digital or non-digital sources or material that are created or collated to conduct a research project. The answer to the research question is based on the analysis of these data. The types of research data can be grouped according to whether they are collected data, produced data, or reused data. The origins, types, and qualities of data are discussed in chapter 2. 

Research data management is an integral part of responsible conduct of research (in Finnish hyvä tieteellinen käytäntö). It promotes the transparency and replicability of research and research results. Data management also enables the verification of research data in an article’s peer-review process, for example. In addition, according to UEF Open science and research policy, research data should be managed and by principle also be opened whenever possible and in accordance with research ethics and the legal framework, including copyright legislation and agreements. For research ethics and legal framework, see further chapter 2 of this learning material. 

Data description is an essential part of research data management. The description should include all information that is required to understand and reuse the data, regardless of whether the data is made publicly open or not. In practice, this includes explanation of, for example, what kind of material the data includes, where it has been collected, who has collected it, and where it is stored. In the context of data description, terms such as metadata and documentation are used. These are discussed in more detail in chapter 3 of this learning material. 

The management of research data can be planned in practice through the creation of a Data Management Plan (DMP). Data management plan is a continuously updated document that can be compiled using pre-existing templates and webtools such as DMPTuuli.  

In short, the aim of research data management is to make the research process and use of research data responsible and efficient, while simultaneously fulfilling the expectations and requirements of the university, funding agencies and jurisdiction (legislation and contracts). 

Information on research data management is compiled and updated on the UEF Data Support website. The drafting of a DMP is discussed in chapter 6 of this learning material. 

Research data lifecycle describes the measures related to the data at different stages of the research process. Often this lifecycle is presented as a circle, which serves to emphasize the continuity of the data: the data does not come to a clear end point, as its lifecycle may continue, for example, through reuse. 

As shown in the figure below, phases such as planning, collecting, analysing, storing, publishing, disposing, discovering, and reusing are often mentioned in the lifecycle. It is good to remember that during research, data management measures do not always proceed according to the model from one stage to the next. For example, collecting or publishing data can take place at many different stages of research. Storing, sharing, and publishing are discussed in chapter 4 and chapter 5. 

In the beginning of your research process, it is advisable to plan the data collection as precisely as possible. You must carefully examine the laws and ethical considerations. Agree about the details of data management with your research partners and always update your data management plan when necessary. 

Data should be documented, described, and stored so that they remain usable and protected during the research and after it. After collecting your data, you analyse it and report your findings e.g. in a journal article. 

At the end of your research or afterwards, the data may be shared with others or archived according to the guidelines of your research organisation or collaborator. The data lifespan continues, and if openly available, the data can be reused for new purposes, such as research, teaching, studying, or commercial use.  

At all stages of research, systematic data management creates the necessary basis for good research and facilitates the concrete implementation of the research project. 

Source: Open Science Foundation (CC BY)

Adhering to the FAIR-principles is an integral part of research data management. FAIR principles were published in 2016 (The Future of Research Communications and e-Scholarship, i.e., Force), and guide in the production of data and its metadata (the descriptive information of the data) 

• Findable 
• Accessible 
• Interoperable 
• Reusable.
  

FAIR principles and their implementation in practice means the following (and you might probably partially follow these without having paid attention to it): 

• Findable: The research data can be easily found through different search services and in data repositories and publication channels. Findability is supported by comprehensive description (metadata), a unique persistent identifier (PID), and the registration or storage of the data in a data service that also provides a persistent identifier for the data. 
• Accessible: The data and/or its metadata are freely available. The accessibility of data is enabled in particular by a persistent identifier. This also involves the accessibility of metadata in case the data cannot be (fully) opened or if the post-research storage period of the data has already expired and the data has been destroyed. 
• Interoperable: The research data can be used in various environments and at different times. Interoperability is supported through the use of open-source software, by favouring open and machine-readable vocabularies and codebases, and by linking data (or metadata) to other outputs or previous versions. 
• Reusable: The research data can be reused for new research or for non-research purposes in accordance with the terms of use of the data. Reusability is made possible through thorough documentation and clear presentation of access rights.
  

Adhering strictly to the FAIR principles is not always feasible. It is good to consider what kinds of restrictions and possibilities relate to your research data. In addition, following FAIR principles does not necessarily equate to sharing the data openly. 

Several data management services, such as CSC’s Fairdata.fi, have been developed to help you implement the FAIR principles in your work. In addition to choosing a service that has implemented the principles, you should consider what other actions you as a researcher must take to make your data as FAIR as possible. 

The Fairdata services are available to researchers and other actors in Finnish universities, applied science and state research institutes. The services are offered free of charge for their users. The services are offered by the Ministry of Education and Culture and produced by CSC – IT Center for Science.

IDA: research data storage 

Qvain: research dataset description tool 

Etsin: research data finder 

Digital Preservation Service for Research Data (in Finnish Fairdata PAS) 

You will need to register as a CSC customer in order to use the Fairdata services, which can be done, for example, with the HAKA credentials of the home university. 

IDA allows you to store research data during and after your research, when it is no longer being actively processed. Data in IDA can be “frozen” and made available to other users through other Fairdata services. Data containing sensitive personal data cannot be stored in IDA. 

Qvain is a tool for describing research data. It allows you to create metadata for research data stored in IDA or elsewhere. The research material is given a permanent identifier, or PID for short (DOI or URN).    

The data descriptions published through Qvain are available via the Etsin service. Etsin is a search service for finding research data and their descriptions from Fairdata and other sources copied and imported into Fairdata. 

The Fairdata PAS service provides long-term preservation services for valuable research data. Its aim is to ensure maximum reusability for very significant research data and its utilisation for future generations. The organisation assesses the suitability of the research data for archiving in Fairdata PAS. 

UEF instructions and information about research support in Heimo (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.