What and Why FAIR data? Easier said than implemented?


(Image credit: Farm Data Train project that aims to connect agricultural data to make them more usable. It is a joint initiative of GODAN, CABI, Wageningen UR and DTL).

In 2016, a Nature article "The FAIR Guiding Principles for scientific data management and stewardship" launched the FAIR concept.

FAIR stands for Findable, Accessible, Interoperable, Re-usable principles (The FAIR principles as published by FORCE11). The FAIR Data principles act as an international guideline for high quality data stewardship. Throughout the FAIR Principles, we use the phrase ‘(meta)data’ in cases where the Principle should be applied to both metadata and data.

“Percentage of time spent finding and organising data according to research data specialists: 79%” (#RDAPlenary). Processing and curating data according to FAIR principles both save time and have impact through information and knowledge build upon this data.

In a move to sharp its focus on the processing data according to FAIR principles rationales, the Research Data Alliance, for example, keeps identifying different parameters of FAIRness helpful to establish nodes (e.g. ELIXIR nodes: the national implementation of a harmonised FAIR Data Management programmes) for FAIRifying data, important in maximizing the discovery and reusability of digital resources in long term goal.

Research Data Alliance :
Data Management Framework : What Makes It So Special?

FAIR Data Principles apply not only to data but also to metadata, and are supporting infrastructures (e.g., search engines). Most of the requirements for findability and accessibility can be achieved at the metadata level, but interoperability and reuse require more efforts at the data level. This scheme depicts the FAIRification process adopted by GO FAIR.

Even though Open Data and FAIR Data are different, they can be overlapping concepts; FAIR data doesn’t not automatically imply that it needs to be accessible - there can be limitations to access, for example, for sensitive data. Accessibility of FAIR data means “how-to-access” and is defined in a human- and machine-readable way.

FAIRness should be assessed before and after the work with data is done. Recently, a prototype software infrastructure and a set of metrics to assess the FAIRness of digital resources were developed (take a look at: FAIR Metrics; 1 May, 2018: RDA Workshop: Improving Reproducibility In Research: The Role Of Measurement Science). 

FAIR data is not a platitude and is not a goal; it is a process. Besides the importance of FAIR compliance, FAIR data requires a paradigm shift, investments at the Data Management Planning stage, incentive structures and cultural change.

FAIR data practices have arrived in the communities because they have been shown to increase the quality of scientific findings. Getting more FAIRdata and moving its trust forward is also not just about FAIR SCIENCE, it's about FAIR SOCIETY...

RELATED :

European Open Science Cloud (EOSC) and FAIR data principles: 

To be continued… stay also tuned to #FAIRdata and #FAIRprinciples for updates


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