Making Data FAIR Requires More than Just Principles: We Need Knowledge Technologies

Abstract

Discussions regarding open science have circulated in the scientific community for many years. The articulation of the FAIR principles in 2016, however, led to a groundswell of excitement to make experimental data findable, accessible, interoperable, and reusable. The FAIR acronym is catchy and easy to remember. The 15 FAIR principles, however, are not. Efforts to enhance access to scientific datasets and to promote their reuse require intuitive tools that implement the FAIR principles as a side effect of their use. The CEDAR Workbench is one such tool that simplifies the authoring of standardized, comprehensive metadata to make datasets FAIR. Systems such as the CEDAR Workbench, which renders datasets FAIR in a transparent fashion, can enhance open science as a direct byproduct of their use. Current projects that have adopted the CEDAR Workbench provide an opportunity to assess how well knowledge technologies can facilitate the creation of FAIR data.