FAIR principles part 3: Interoperable

The third letter in FAIR is I for Interoperable. Read this guide to become familiar with what interoperable means, and why it is important, but may be hard to achieve.

Last modified: 14.11.2018


Interoperable #1: (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation

Humans, and especially machines, can have a hard time interpreting data. Words are ambiguous, and the multitude of spoken and written languages add further to the complexity. Problems range from not being able to interpret the value of a cell due to missing information on the metrics used, to more complex situations where you will have to look for many different terms describing the same object, or stumble across words with different semantic meaning across various disciplines. The same holds for e.g. place names.

The FAIR principles address this issue by recommending the use of shared data standards for representing data, and the use of vocabularies and ontologies to represent values and mark-up data. Vocabularies and ontologies are often defined within the research communities and are an unambiguous way of adding semantic meaning to your data. A simple example is to rely on a flower ontology to classify flowers, instead of writing their names in plain text.

Working with data in this way can make your data more useful and discoverable. However, you should be aware that this type of work often has an impact on your methods and the software you use.

Interoperable #2: (Meta)data use vocabularies that follow the FAIR principles

A vocabulary is only good, if it is accessible and allows for the right interpretation of the data. This principle highlights the importance of using vocabularies that are common to the community and well documented, and can be referred to using persistent identifiers. Usually, you will find this type of vocabulary, taxonomy etc. within your research discipline or maybe in other disciplines where these are developed. Evaluating a vocabulary often includes looking for its creator and checking whether it is still maintained and updated. These can be complex vocabularies; or simple mark-ups like ISO standard strings for representing countries in a data set. E.g. Denmark is DNK in ISO 3166-1 alpha-3. In terms of interoperability, this is far better than writing ‘Danmark’, ‘Denmark’, ‘Dänemark, ‘Dinamarca’ etc. in your (meta)data.

Interoperable #3: (Meta)data include qualified references to other (meta)data

It is important to be able to trace the connections between your data set and data sets that are related to it. This can be done by linking to other data sets that are not included in your work. It can also be done through connections that show how your data set is derived from a previous version or e.g. is processed data based on some raw data. Either way, it is important to maintain these connections by referencing between data sets. If your data set relies on other’s data - or your own - this is also an appropriate method to ensure that proper credit is given to the people who created the data that your data is based upon.

Go to the webpage for A FAIRy tale for more information about the FAIR principles.

Based on 'A FAIRy tale' CC-BY-SA 4.0 ‘DK Fair på tværs’.

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