The FAIR principles underpin good quality in research

The FAIR principles are often referred to in relation to research data. The acronym stands for Findable, Accessible, Interoperable and Reusable and applies to the source material, methods and data products of research. By focusing on these objectives from the outset, you can ensure that not only is your research well documented, you can also achieve merit for parts of your research other than the traditional publications in the form of books or articles. The idea that research results could be published without proper citations certainly seems strange, but unfortunately digitisation has led to many such situations.

FAIR stands for Findable, Accessible, Interoperable and Reusable.

The FAIR principles themselves are highly detailed technical specifications, which can be difficult to understand and even more difficult for the individual researcher to implement by themselves. In practice, you need an infrastructure and digital services in order to publish research data. Fortunately, these are beginning to become available, but it can still sometimes be a challenge to use them, for practical or legal reasons. It is therefore a good idea to seek advice from the research services within your own organisation if you are uncertain about how to proceed. It is also important for the organisation to understand the needs of its researchers and that knowledge is born out of collaboration between researchers and data support. This is often a new and complex area for all concerned.

The technical specifications go into great detail about how research data should be provided with sufficient documentation in the form of metadata. This metadata should also be usable, not only by other researchers, but should also be machine readable. This means that it should be well structured and use common terms and open formats. In practice, this can be ensured by using services designated for research data in your research domain. It is important to remember that even if the data itself cannot be made freely available, the metadata can be published. Other important things to consider are that there should be a license stipulating the terms of use for the data, and also a persistent identifier. The latter is often referred to as a PID. Often this is a DOI or a URN and these are the cornerstone of FAIR because a unique and persistent identifier is a promise that the data it represents is unchanged or at least changes are documented.

The researcher should consider their colleagues and the research community by providing the opportunity to verify their research and by enabling their research material to be found and reused. 

The researcher should consider their colleagues and the research community by providing the opportunity to verify their research and by enabling their research material to be found and reused. In short: remember that you must also provide proper citations for your own digital sources. To do this, you need a PID, which you can obtain when you register or publish your data. When you do this, you must remember to document them properly using metadata, so that others can truly understand them and use them correctly.

The FAIR principles originate from circles that work with linked data and the Semantic Web. It is of great benefit if you are able to incorporate these principles when designing databases, for example. In many cases, however, it is sufficient to publish your datasets in a suitable data archive and to use persistent identifiers wherever possible when referring to something.

Jessica Parland-von Essen, data specialist, docent and Senior Coordinator at CSC – IT Center for Science.

_{Further information:}

_{Fairdata.fi: www.fairdata.fi
Wilkinson, M., Dumontier, M., Aalbersberg J.J. et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data. Vol 3. http://dx.doi.org/10.1038/sdata.2016.18
UNIFI ry Suomen yliopistojen rehtorineuvosto (2018). Avoin tiede ja data. Toimenpideohjelma suomalaiselle tiedeyhteisölle.  http://urn.fi/URN:NBN:fi-fe2018052424593
Laine, H. & Nykyri, S. (2018). Dataviittaamisen tiekartta tutkijalle. Informaatiotutkimus, 37(2). https://doi.org/10.23978/inf.72999
H2020 Online Manual. European Commission. Research and Innovation. H2020 Participant Portal. [web page] Retrieved from http://ec.europa.eu/research/participants/docs/h2020-funding-guide/cross-cutting-issues/open-access-dissemination_en.htm
Finnish Committee for Research Data (2018). Tracing Data: Data citation roadmap for Finland. http://urn.fi/URN:NBN:fi-fe201804106446
FORCE11. The FAIR data principles. https://www.force11.org/group/fairgroup/fairprinciples}