In recent years, the advancement of open science has led to data sharing becoming more common practice. Data availability has clear merits for science as it opens up possibilities for re-use of datasets by others, leading to less redundancy, more efficiency, and more transparency. The ideal is for scientific data to be as open as possible and FAIR: Findable, Accessible, Interoperable, and Reusable. Parallel to this development, recent times have seen more stringent guidelines with respect to data privacy, culminating in the General Data Protection Regulation law, or GDPR. Navigating the balance between protecting participants’ privacy and making one’s dataset as open as possible can be challenging for researchers. In this paper, we provide two worked examples with real datasets 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.
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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