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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 data files belonging to this project as: van Ravenzwaaij, D., Heininga, V. E., Hoekstra, R., Scheibe, S., Span, M., van Bergen, D., & Wessel, I., (2025). De-identification when making datasets FAIR: Two worked examples from the behavioral and social sciences. https://doi.org/10.17605/osf.io/eqbd3. For more details on our methods of de-identification, please see our preprint: https://osf.io/preprints/psyarxiv/acpm3 Dataset 1 is a dataset on work/life boundary management that was collected during a lockdown period in the corona pandemic in 2021. It is part of a separate publication associated with the following OSF page: https://doi.org/10.17605/OSF.IO/HVYZU Dataset 2 is a dataset employing a trauma film paradigm that was collected in 2019. It is part of a separate publication associated with the following OSF page: https://doi.org/10.17605/OSF.IO/9VTBZ