Newsletter #7: Herbstsemester 24

Welcome

Dear members of the Department of Psychology,

The 7th newsletter of the Open Science Initiative at the Department of Psychology promises to be an exciting read!

This time, we cover how to protect participants’ privacy in the era of open science and ask the question if SWISSUbase is better than OSF. Of course, we also inform you about upcoming events and workshops around open science. We would also like to remind everyone of the upcoming deadline for submissions for the Open Science Prize!

Questions, suggestions, and contributions to this newsletter may be sent toopenscience@psychologie.uzh.ch. The next newsletter will appear at the beginning of FS25.

Warm regards,
Your Open Science Initiative

Topics

The open science movement, with its emphasis on data accessibility and transparency, has brought about a revolution in research. Sharing datasets allows for the reproduction and validation of studies, strengthening the credibility of scientific findings and fostering innovation by enabling researchers to explore new questions (Kalkman et al., 2019). However, this increased accessibility raises concerns about participant privacy, as even anonymized data can be re-identified when combined with other public information. This risk arises because seemingly innocuous data points, when aggregated, can lead to the identification of individuals. Furthermore, data can be misused for criminal purposes such as identity theft. Therefore, researchers must prioritize responsible data sharing practices to balance the benefits of open science with the potential risks to individual privacy.

To address these challenges, researchers can implement several measures:

1. Risk assessment: Evaluating the risk of re-identification is an essential first step, as not every data set carries the same risk.
2. Data minimization: Remove all direct identifiers such as IP addresses, (email) addresses, among others, from the dataset. Variables with many categories should be re-coded by grouping similar values. For example, instead of reporting zip codes, use cantons.

The MinBlur Algorithm
Morehouse et al. (2024) discuss the tension between making research data available to the public and protecting the privacy of human subjects. To address this issue, the authors propose a framework for assessing the risk of re-identification and provide two open-source algorithms, MinBlur and MinBlurLite, to help researchers mitigate this risk. These algorithms work by blurring or removing specific data points that are most likely to lead to re-identification, while preserving as much of the original data as possible. They allow researchers to:

• Flexibly choose what variables to blur
• Be transparent about the process
• Maintain data fidelity

By using tools like MinBlur and MinBlurLite, researchers can share their data openly while still protecting the privacy of their participants. This approach allows for the benefits of open science to be achieved without compromising individual rights.
Disclaimer: The authors promote these algorithms as shiny apps hosted on US servers. We believe that uploading data to these shiny apps is not allowed for members of the University of Zurich. Instead, we encourage downloading the functions provided HERE for local use.

Conclusion
As the open science movement continues to evolve, it is essential for researchers to stay informed about best practices for data sharing and privacy protection. The authors emphasize that the risk of re-identification should not discourage open science practices but rather should encourage the development of new tools and techniques to protect participant privacy.
By adopting these practices and utilizing solutions like MinBlur, researchers can contribute to a more transparent and collaborative research environment while upholding the highest ethical standards. This allows for upholding the principles of Open Science without compromising the security and privacy of the individuals involved.

References

• Kalkman, S., Mostert, M., Gerlinger, C. et al. Responsible data sharing in international health research: a systematic review of principles and norms. BMC Med Ethics 20, 21 (2019). https://doi.org/10.1186/s12910-019-0359-9
• Morehouse, K. N., Kurdi, B., & Nosek, B. A. (2024). Responsible data sharing: Identifying and remedying possible re-identification of human participants. American Psychologist. Advance online publication. https://doi.org/10.1037/amp0001346

On October 3rd, we conducted a hybrid information event on SWISSUbase, jointly organized with the Open Science Services of the University Library. Penelope Weissman and Marieke Heers from SWISSUbase and FORS, the Swiss Center of Expertise in the Social Sciences, demonstrated the functionality of SWISSUbase and told us about possible advantages of using SWISSUbase compared to OSF.

Many members of the Open Science Initiative attended the event and are now ready to be consulted should you consider using SWISSUbase. We were quite pleased with what SWISSUBase has to offer. First of all, the data will be hosted on Swiss servers. More importantly, there are friendly and competent people who will take care of your data and give you advice; some of them are psychologists! As a result of this careful data curation, your data will be tagged with relevant metadata and are guaranteed to be FAIR. Another nice feature of this repository is that you can decide how open your data are: You can publish the data for everyone to download without permission just like on OSF, but you can also include a requirement to ask the authors for permission or define for what the data may be used. Obviously, how open your data can be depending on the informed consent that you used. If you have questions about anonymity preservation, people at SWISSUbase will support you in finding an appropriate solution. Therefore, SWISSUBase should be especially interesting for researchers working with sensitive data. While SWISSUbase is the infrastructure, people at FORS deal with all Social Sciences data, and they also have a lot of useful guides on survey methods and data management available here.

SWISSUbase platform - share, find and access research data: www.swissubase.ch
SWISSUbase Info Website - learn about the mission, services & available support for sharing and accessing data: www.info.swissubase.ch

Did you know that engaging in Open Science practices is rewarding in many ways? For example, many researchers who use preregistration report that this practice helps them proceed with their research in a more focused way, even if their work is more exploratory. Or consider that preparing data and code for other people also means that the person who is most likely to look at these files again after some time (i.e., you!) will be grateful if they can easily find the data and documentation.

There are some material benefits of engaging in Open Science practices as well. In addition to the annual Open Science Prize awarded by the Open Science Initiative, this year a new incentive was introduced at the MaDoKo. More specifically, jurors evaluating the posters also rate the extent to which M.Sc. and doctoral students showed understanding of Open Science practices (on the poster or during the conversation with jurors). That is good to know for next year's MaDoKo, isn't it?

But first things first: The deadline for submitting your work for consideration for the Open Science Award 2024 is January 31st, 2024. More information here:
https://www.psychologie.uzh.ch/de/bereiche/open-science/preis.html

ExPra teachers and supervisors of M.Sc. theses are kindly asked to remind their students of this opportunity!

On November 5, 2024 (10-12h) there will be a workshop on how to use git and GitLab to promote a transparent and reproducible research flow. There are still available spots so if you are interested, please send an a-mail to renato.frey@uzh.ch. The original information about the workshop is attached below:

• One element of open science involves the transparent documentation of how our research artifacts (e.g., code, data, text) have evolved and changed over time. The open-source tool git is predestined to this end, and particularly in combination with platforms such as GitLab or GitHub enables a transparent and powerful research workflow, including collaboration coordination and the exchange of materials. This workshop is suited to all researchers in our department who would like to take the first steps in adopting such a workflow in their own scientific work. Specifically, the workshop will start with a brief introduction of the basic concepts and techniques (for a preview, see these slides: https://renatofrey.net/files/slides/git_intro.pdf), followed by hands-on exercises using the UZH-specific GitLab instance.

There are very interesting talks upcoming as part of the ReproducibiliTea, jointly organized by the universities of Basel and Zurich – check out their detailed program: https://www.crs.uzh.ch/en/training/ReproducibiliTea.html

This newsletter is published once a semester – feel free to contact us if you have any questions regarding it: openscience@psychologie.uzh.ch

As this newsletter is only published once per semester, we are unable to inform you about events scheduled at (rather) short notice. Thus, we recommend subscribing to the mailing list of UZH’s Center for Reproducible Science to stay up to date on offers for further training and scientific exchange on open science at UZH, such as the ReproducibiliTea Journal Club.

Current members of the open science initiative

Prof. Dr. Johannes Ullrich (Leitung); Dr. Walter Bierbauer; Prof. Dr. Renato Frey; Dr. Martin Götz; M.Sc. Patrick Höhener; Dr. Sebastian Horn; M.Sc. Sophie Kittelberger; Dr. André Kretzschmar; M.Sc. Pascal Küng; Prof. Dr. Nicolas Langer; Dr. Susan Mérillat; Dr. Robin Segerer; Prof. Dr. Carolin Strobl; Dr. Lisa Wagner; M.Sc. Jasmin Weber; Dr. Katharina Weitkamp; M.Sc. Natascha Wettstein