FAIR is not the end goal | Daniel S. Katz's blog

I’ve been watching and participating in FAIR work for a while, first in terms of data, and more recently in research software, workflows, and machine learning models. Some of this has been based on my general interest in how research works and how we improve it, and some has been based on more specific work, such as raising the profile of research software and its developers and maintainers, and even some collaborative work in specific domains, such as high energy physics.

Nowhere in these contexts and communities did anyone get up one morning and say, “I think we should create a new goal, FAIR, and then define metrics to measure how this goal is met.” This concept wasn’t created by funders or bureaucrats to give researchers something else to do and be measured upon. Instead, people who have been thinking for a long time about how research is performed thought about the overall process of research, and over a number of discussions and meetings, decided to identify and name some of the elements of the research ecosystem and process that needed to be improved in an effort to reinvigorate the agenda.

At least in part because they came up with a clever name (FAIR) and did a really good job of disseminating this, it has caught on in the research management, research administration, and research funding communities, and to a lesser extent, in the researcher community as well. And something identifiable that people can pursue and try to measure has led to lots of focus on being FAIR.

But FAIR isn’t the end goal, it’s just one part of the solution.

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