De-identification when making datasets FAIR: Two worked examples from the behavioral and social sciences

• DOI: 10.31234/osf.io/acpm3_v2
• Publication Date: 2025-03-04T13:40:09Z
• Authors: Don van Ravenzwaaij; Marlon de Jong; Rink Hoekstra; Susanne Scheibe; Mark M. Span; Ineke Wessel; Vera Ellen Heininga
• Keywords: Identification; Behavioural sciences

In recent years, the advancement of open science has led to data sharing becoming more common practice. Data availability clear merits for as it opens up possibilities re-use datasets by others, leading less redundancy, efficiency, and transparency. The ideal is scientific be possible FAIR: Findable, Accessible, Interoperable, Reusable. Parallel this development, times have seen stringent guidelines with respect privacy, culminating in General Protection Regulation law, or GDPR. Navigating balance between protecting participants’ privacy making one's dataset can challenging researchers. paper, we provide two worked examples real from behavioral social sciences on how closed necessary, goal maximally facilitating while minimizing risk participant identification.