FAIR data
Findable, Accessible, Interoperable and Reusable research data
In this chapter we explain what the FAIR principles mean and how they can be applied to a greater or lesser extent within Urban Vitality research. We do this by using published FAIR checklists in UvA/HvA figshare. In addition, we provide useful links for more information on FAIR.
What is FAIR?
FAIR is an acronym for Findable, Accessible, Interoperable and Reusable. The FAIR principles are guidelines for making data findable, accessible, interchangeable and reusable.
Findability is like a shop window: both man and machine (computer) can see what is available inside. Accessibility does not mean that the data are open (for everyone to see), but specifies who may 'enter' (access the data), in what way and under what conditions. Interoperability, or (semantic) interoperability, is like 'speed dating for machines': it is useful if data from different sources have a 'match' and if there is a common, universal (computer) language. Reusability means that there is additional information present that makes the data interpretable. Think for example of a research or measurement protocol, syntax of operations and analyses or a logbook. (Source: Steeds FAIRder. Verslag van het Urban Vitality zaaigeldproject 'FAIR: geen woorden maar data' ).
Data can satisfy FAIR principles to a greater or lesser extent. There are 15 FAIR principles that have as their ultimate goal the ability to reuse data.
FAIR in research
The Dutch Code of Conduct for Scientific Integrity, various grant makers, the National Platform Open Science, all have incorporated FAIR principles. So too do the HvA guidelines for research data management (RDM). The FAIR principles should be applied throughout the research data cycle.
Benefits for research and researchers
- Increased impact of research through findability (visibility) and reusability of data (citation of your data)
- Making it possible or easier to combine data from different studies
- Data become (more) suitable for AI applications
- Enable or facilitate reproducibility, verifiability, and replicability of research and results
FAIR checklists
Already in the preparation phase of a research project, it is of utmost importance to establish a specific data collection protocol and anticipate how other researchers might reuse the data from your research project. There are several levels in making research data FAIR. Our goal is to create several checklists that can be used by researchers and support staff during a research project.
FAIR enough checklist.
The first checklist describes the minimum effort for Urban Vitality (UV) research projects and can be applied by researchers with minimal assistance from a data steward. Following this checklist makes the research data quite FAIR to people and somewhat FAIR to machines (computers). The checklist should be used immediately after obtaining research funding.
DOI: https://doi.org/10.21943/auas.20178863.v1
FAIRder checklist.
Checklist yet to be developed that will make your research data even more FAIR compliant. This checklist will have to be completed in consultation with datasteward.
FAIRy innovative checklist
Checklist yet to be developed that makes research data and metadata fully 'machine actionable' (can be found, understood and interpreted by computers). It involves developing a semantic model, see for example here . This is a team effort of researchers, datasteward and potentially other expertise from outside the HvA (data engineers, FAIR experts).
References and usefull links
FAIR refers to the following chapters within the Research Manual:
The FAIR-aware tool makes you aware of FAIR principles in an approachable way:
https://fairaware.dans.knaw.nl/
Knowledge clip: FAIR data principles. https://www.youtube.com/watch?v=2uZxFu9SFi8
The GO-FAIR initiative explain the FAIR principles (fairly technical in nature):
https://www.go-fair.org/fair-principles/
A Danish "deep dive" into FAIR:
Utrecht University provides clear information on making data FAIR:
https://www.uu.nl/en/research/research-data-management/guides/how-to-make-your-data-fair
Wilkinson et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientic Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18 .
Jacobsen et al. (2020). A Generic Workflow for the Data FAIRification Process. Data Intelligence, 2:1-2, 56-65. https://doi.org/10.1162/dint_a_00028 .
Kanis et al. (2020). FAIR: Geen woorden maar data. University of Amsterdam / Amsterdam University of Applied Sciences. Online resource. https://uvaauas.figshare.com/projects/FAIR_Geen_woorden_maar_data/83840 .
De Personal Health Train in de zorg. Verhalen uit de praktijk (juli 2020). Retrieved from https://pht.health-ri.nl/sites/healthtrain/files/2020-07/PHT%20in%20de%20zorgpraktijk.pdf .
Deist et al. (2020). Distributed learning on 20 000+ lung cancer patients – The Personal Health Train. Radiotherapy and Oncology, 144, 189-200. https://doi.org/10.1016/j.radonc.2019.11.019 .