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“FAIR” (or “FAIR data”) is an term that I’ve been bumping into more and more frequently. For example, its included in the UK’s recently published Geospatial Strategy.
FAIR is an acronym that stands for Findable, Accessible, Interoperable and Reusable. It defines a set of principles that highlight some important aspects of publishing machine-readable data well. For example they identify the need to adopt common standards, use common identifiers, provide good metadata and clear usage licences.
The principles were originally defined by researchers in the life sciences. They were intended to help to improve management and sharing of data in research. Since then the principles have been increasingly referenced in other disciplines and domains.
At the ODI we’re currently working with CABI on a project that is applying the FAIR data principles, alongside other recommendations, to improve data sharing in grants and projects funded by the Gates Foundation.
From the perspective of encouraging the management and sharing of well-structured, standardised, machine-readable data, the FAIR principles are pretty good. They explore similar territory as the ODI’s Open Data Certificates and Tim Berners-Lee’s 5-Star Principles.
But the FAIR principles have some limitations and have been critiqued by various communities. As the principles become adopted in other contexts it is important that we understand these limitations, as they may have more of an impact in different situations.
A good background on the FAIR principles and some of their limitations can be found in this 2018 paper. But there are a few I’d like to highlight in this post.
They’re just principles
A key issue with FAIR is that they’re just principles. They offer recommendations about best practices, but they don’t help you answer specific questions. For example:
- what metadata is useful to publish alongside different types of datasets?
- which standards and shared identifiers are the best to use when publishing a specific dataset?
- where will people be looking for this dataset to ensure its findable?
- what are the trade-offs of using different competing standards?
- what terms of use and licensing are appropriate to use when publishing a specific dataset for use by a specific community?
- …etc
Applying the principles to a specific dataset means you need to have a clear idea about what you’re trying to achieve, what standards and best practices are used by the community you’re trying to support, or what approach might best enable the ecosystem you’re trying to grow and support.
We touched on some of these issues in a previous project that CABI and ODI delivered to the Gates Foundation. We encouraged people to think about FAIR in the context of a specific data ecosystem.
Currently there’s very little guidance that exists to support these decisions around FAIR. Which makes it harder to assess whether something is really FAIR in practice. Inevitably there will be trade-offs that involve making choices about standards and how much to invest in data curation and publication. Principles only go so far.
The principles are designed for a specific context
The FAIR principles were designed to reflect the needs of a specific community and context. Many of the recommendations are also broadly applicable to data publishing in other domains and contexts. But they embody design decisions that may not apply universally.
For example, they choose to emphasise machine-readability. Other communities might choose to focus on other elements that are more important to them or their needs.
As an alternative, the CARE principles for indigenous data governance are based around Collective Benefit, Authority to Control, Responsibility and Ethics. Those are good principles too. Other groups have chosen to propose ways to adapt and expand on FAIR.
It may be that the FAIR principles will work well in your specific context or community. But it might also be true that if you were to start from scratch and designed a new set of principles, you might choose to highlight other principles.
Whenever we are applying off-the-shelf principles in new areas, we need to think about whether they are helping us to achieve our own goals. Do they emphasise and prioritise work in the right areas?
The principles are not about being “fair”
Despite the acronym, the principles aren’t about being “fair”.
I don’t really know how to properly define “fair”. But I think it includes things like equity ‒ of access, or representation, or participation. And ethics and engagement. The principles are silent on those topics, leading some people to think about FAIRER data.
Don’t let the memorable acronym distract from the importance of ethics, consequence scanning and centering equity.
FAIR is not open
The principles were designed to be applied in contexts where not all data can be open. Life science research involves lots of sensitive personal information. Instead the principles recommend that data usage rights are clear.
I usually point out that FAIR data can exist across the data spectrum. But the principles don’t remind you that data should be as open as possible. Or prompt you to consider about the impacts of different types of licensing. They just ask you to be clear about the terms of reuse, however restrictive they might be.
So, to recap: the FAIR data principles offer a useful framework of things to consider when making data more accessible and easier to reuse. But they are not perfect. And they do not consider all of the various elements required to build an open and trustworthy data ecosystem.
