De-Identification When Making Data Sets Findable, Accessible, Interoperable, and Reusable (FAIR): Two Worked Examples From the Behavioral and Social Sciences

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In recent years, the advancement of open science has led to data sharing becoming more common practice. Data availability has clear merits for science because it opens up possibilities for reuse of data sets by others, leading to less redundancy, more efficiency, and more transparency. The ideal is for scientific data to be as open as possible and findable, accessible, interoperable, and reusable (FAIR). Parallel to this development, recent times have seen more stringent guidelines with respect to data privacy, culminating in the General Data Protection Regulation law. Navigating the balance between protecting participants’ privacy and making one’s data set as open as possible can be challenging for researchers. In this article, we provide two worked examples with real data sets from the behavioral and social sciences on how to be as open as possible and as closed as necessary with the goal of maximally facilitating science while minimizing the risk of participant identification.