Swiss Reproducibility Network

To promote rigorous research and robust results in Switzerland.

SwissRN Academy

As a new member of the Swiss Reproducibility Network Academy, I saw an opportunity to turn one of my scheduled presentations at the Cancer Research Network Bern into a small SwissRN Academy event. For this, we reached out to the Open Science Team from the University Library of Bern, who were kind enough to collaborate with us in that manner. We are thus proud to present:

Swiss Reproducibility Network Academy meets Cancer Research Network Bern

When: May 2nd, 2024, 13:00-14:00 CET

Where: Mur24_EG50, DBMR Building, Murtenstrasse 24, 3008 Bern

Online: https://unibe-ch.zoom.us/j/65758553140?pwd=ZUU2eXMxOE1BUUY4bWZNblBYcFpCdz09

Join us for two short talks on research data management and researcher degrees of freedom. We look forward to your participation!

Best regards, Peter Degen, on behalf of the Swiss Reproducibility Network Academy


Dr. habil. Olga Churakova, Data Steward: Medicine and Veterinary Medicine, University of Bern, University Library of Bern

Research data of high quality is an essential resource for science and society. Research Data Management (RDM) is a part of good scientific practice according to nationally and internationally recognized standards for scientific integrity. Good RDM enhances the robustness of research outputs, facilitates research collaborations, makes research results more reproducible, and strengthens society’s trust in science.

Peter Degen, Radiation Oncology, Department for BioMedical Research, University of Bern

Single-cell RNA-Sequencing has emerged as a powerful tool for exploring cellular heterogeneity and identifying dysregulated pathways. However, often there are ethical, practical, and financial constraints that inhibit the acquisition of large sample sizes. Moreover, a typical analysis involves a plethora of parameters to tune and software packages to choose from, and determining the optimal choice for any particular experiment is seldom straightforward. Given these challenges, it is imperative to account for researcher degrees of freedom and assess the robustness of downstream results to the chosen analysis workflow.