How FAIR data are helping to build trust in science

If you build a data set and nobody can find it, is it useful? Not as much as it could be. With trust in science under siege from partisan actors and impartial pathogens, the accessibility and transparency of — and trust in — scientific information must be improved. Have people stopped trusting science? The data tell a surprising story Enter the FAIR Data Principles. In 2014, scientists realized that data management and stewardship could benefit from a set of shared guidelines, and dozens of international researchers gathered to draft new recommendations. The resulting principles — which established that data should be findable, accessible, interoperable and reusable (FAIR) — were published ten years ago 1 . The original publication has around 16,000 citations, and governments, funders and publishers around the world now ask that data be hosted and shared in FAIR-compliant ways. A decade on, however, even the founders acknowledge that the FAIR principles are an imperfect tool. Barend Mons, a molecular biologist at Leiden University in the Netherlands who conceived the initiative, says that FAIR was always meant to be a set of general principles, “and so, by definition, cannot address the specifics of every application”. Fortunately, other researchers have taken the framework and extended it to cover the broader data ecosystem 2 , including the algorithms, tools and workflows that drive contemporary research. …