How does research data become FAIR from the beginning? New guidelines for researchers published

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The guidelines prepared by an AVOTT working group can be applied to the needs of each organization, and they can be used freely to support research activities.

The guidelines supporting good data management drawn up by the Applying FAIR principles working group established by the research data expert panel have been published! The guidelines Improve the quality and impact of your research through data management – A guide for making your data FAIR address researchers and guides them to follow the FAIR principles throughout the research process. Taking the FAIR principles into account supports the repeatability of the research and the verification of the results afterwards. The guidelines also explain some key basic concepts of research data management.

The focus of the guidelines is on the reuse possibilities, quality and value of the data that can be evaluated from different perspectives. The guidelines offer perspectives for evaluating quality and value and guides the researcher to examine the data lifecycle from the point of view of reuse. How is the data described so that it is reusable? How are the special features of the data, such as dynamism or sensitivity, taken into account in the description and publication? How is the material compiled for publication? What kind of data is valuable?

Research data is data that is generated and/or utilised in research. The data lifecycle covers all the stages digital data, from creation to long-term storage or destruction. Managing digital materials requires planning and know-how, because weakly managed data can become corrupted, messed up or lost.

Data management starts as early as the research planning phase, and the implementation of the FAIR principles is carried out throughout the entire research process. The researcher must plan the entire data lifecycle and take into account the technical specifications related to the data and matters related to rights. Responsible research also includes the openness of the process, i.e., opening what has been done to the data and how the end result has been reached.

The FAIR principles rely on good data management

The FAIR principles, which stand for Findable, Accessible, Interoperable and Reusable, are goals that promote good data management and the quality and impact of research. Many funders and organizations also require compliance with these principles. The principles can be applied to forms of data, both quantitative and qualitative, and their goal is machine readability and interoperability of data. The same principles also apply to metadata, which describes data and makes it discoverable.

FAIR data is both human-understandable and machine-processable. Well-structured data can be combined with other data in the same format or searched. The researcher should think about how another researcher could repeat the work done. It is also important to ensure that data and methods are understandable and usable to others. Research funders and scientific publishers may also require data management and compliance with the FAIR principles.

Improve the quality and impact of your research through data management - A guide for making your data FAIR can also be applied to the needs of your own organization, and it can be used freely to support research activities. The guidelines are available in Finnish, Swedish and English on Zenodo.

Text: Sonja Sipponen
Image: Alexander Sinn on Unsplash

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