Bern Data Talks - Sensitive Data

Managing sensitive personal data poses numerous challenges: Researchers must store and transfer data securely, monitor access to the data and use protection strategies such as anonymization and pseudonymization. And all of this must be done within an ethical and legal framework (such as the revised Data Protection Act) and planned from the outset.

To encourage sharing of challenges, good practices, and possible solutions, there will be short presentations by:

• Nada El Makhzen (Institute of Biochemistry and Molecular Medicine, University of Bern)

  Challenges in Generating, Sharing, and Storing Genetic Data for Patients with Rare Diseases in Africa: Insights from the Cystic Fibrosis Collaboration The main goal of the African cystic fibrosis collaboration and partnership with African countries is to understand the prevalence of cystic fibrosis in regions like North Africa, emphasizing differences, difficulties with diagnosis, and the need for more education and specialized treatments.

• Regina Jenzer M.Sc. (School of Social Work, Bern University of Applied Sciences)

  Kindesschutz: Herausforderungen im Umgang mit sensitiven Daten In diesem Input werden Erfahrungen und Herausforderungen mit sensitiven Daten im Forschungsprojekt "Besserer Kindesschutz durch kindfokussierte Zusammenarbeit im KESB-Verfahren" präsentiert. Der Fokus des Inputs liegt dabei auf dem Vorgehen bei der Rekrutierung der Kindesschutzfälle, dem Führen von Interviews mit besonders vulnerablen jungen Menschen sowie dem Umgang mit diesem äusserst sensitiven Datenmaterial.

• Prof. Dr. Michael Schulte-Mecklenbeck (Institute of Marketing and Management, University of Bern)

  Protecting personal information while sharing data: synthetic datasets Sharing research data, as one of the cornerstones of Open Science, often runs into a privacy problem. What data can be shared while protecting personal information? I will demonstrate an R package (synthpop https://cran.r-project.org/web/packages/synthpop/index.html) that allows the generation of synthetic datasets (based on real ones) that have the same statistical properties but no trackable personal information.

(Talks in English and German.)

Both early career researchers and experienced researchers are welcome. There will also be time for networking with colleagues, so don't miss the Apéro after the event!

For more information, visit the Open Science website (English or German).