FAIR

Definition

Principles to improve “the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.”

Definition source: GO FAIR (n.d.). FAIR Principles.

Stakes

part of: opacity

Adhering to the FAIR Principles ensures your data is technically responsible, as it benefits the reusability and interoperability of the data. This also supports sustainability and better research practices, and consequently, better knowledge production.

Where does it occur in the lifecycle?

1 - Set up

• Write Data Management Plan (DMP)

2 - Collection

• Create usable categories and variables for your dataset

5 - Share & Preserve

• Create or choose an interface to publish data, analyses, visualisations and other publications

Questions to consider throughout your work

• How will your produced data be made FAIR?
• Do you foresee any issues with FAIR implementation? Why?
• Are the categories defined and linked to other databases?
• Can your data be reused in the future?
• Is your work reproducible?
• How are existing datasets used? What are their licenses, formats?

Examples

• HSNDB: https://iisg.amsterdam/en/hsndb
• Untangling FAIR Implementation in the Dutch SSH: https://www.nwo.nl/projecten/icttdcc001003

Good-better-best practices¹

Good	Better	Best
Ensure your dataset is published under open license.	Make use of or create structured and linked vocabularies.	Fill out a FIP (FAIR Implementation Profile) to assess the FAIRness of your research (data). The FIP can also be used as a checklist to understand what you should have in order to have a FAIR dataset - i.e. check existing FIPs and validate these against your own decisions.
Have versioning of documentation and output in place.	Improve findability, by using unique identifiers and indexing metadata and datasets in search engines.	Identify, describe, and reflect on the provenance of your data.
Describe your used metadata schemas and ontologies.	Describe your usage license.

Resources

• FAIR website: https://www.go-fair.org/
• FAIR Implementation Profiles: https://www.go-fair.org/how-to-go-fair/fair-implementation-profile/

This section has kindly been added to by Shuai Wang.

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1. Adapted from GO FAIR (n.d.). FAIR Principles. ↩