Date created: | Last Updated:

Category: Project

Description: 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. Counter 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. The GDPR is a stringent European law on data privacy that makes it more difficult for researchers to navigate the balance between protecting participants’ privacy and making one's dataset as open as possible. In the paper associated with this OSF page, we provide two worked examples of concrete choices one may make with real data sets. Guiding principle is a framework that allows for anonymizing up to five different degrees, depending on what is appropriate, for each variable that poses a risk of identification for the original participant. Please cite the datasets as: van Ravenzwaaij, D., Hoekstra, R., Scheibe, S., Span, M., van Bergen, D., Wessel, I., & Heininga, V. E. (2023). Example Data Sets, Full and Curated. https://doi.org/10.17605/osf.io/eqbd3. For more details on our methods of de-identification, please see https://osf.io/preprints/psyarxiv/acpm3 Dataset 1 is part of a separate publication associated with the following OSF page: https://osf.io/hvyzu/ Dataset 2 is part of a separate publication associated with the following OSF page: https://osf.io/9vtbz/