Computational Reproducibility
Why Computational Reproducibility?
Computational reproducibility is the ability to independently verify the results of a computational study using the same data and methods. It is a critical aspect of scientific research as it ensures the transparency and credibility of scientific findings. Computational reproducibility allows researchers to build upon the work of others, helping to accelerate scientific progress.
The importance of computational reproducibility is particularly evident in fields such as medicine, psychology, environmental science, and economics, where scientific findings can have a direct impact on people’s lives. Inaccurate or non-reproducible results can have severe consequences, ranging from wasted resources to public health risks.
Who we are and how we work
We are researchers and service providers working with tools and technologies facilitating our everyday processing of data. While some of us are good in one aspect of making computational methods more reproducible, others might have improved other ways of achieving this. Together, we aim to learn from each other and find the best ways to ensure reproducibility of our research, our services, and our work.
If you feel up to the challenge, please contact Daniel Stekhoven, to join the group.
Events
Computational Reproducibility Hackathon
Proudly we announce our first working group in person event: a Computational Reproducibility Hackathon!
- When: 7th February, 2025, 09:30 - 17:00
- Where: FernUni Schweiz in Brig-Glis
- What: one-day hackathon with proceedings article
- How: bring your laptop, free of charge
Call for Submissions
We invite you to submit your proposals for methods, tools, or papers that can be reproduced during the hackathon - this can include your own work or work by others that is feasible for reproduction during a 1-day hackathon. By engaging in mutual reproduction, we aim to:
- Quality control: identify strengths and areas for improvement in our work.
- Understand challenges: experience firsthand the difficulties others may face when reproducing our research.
- Improve accessibility: learn how to make our work more accessible and reproducible for future researchers.
- Contribute to posterity: enhance the longevity and impact of our research by making it easier to replicate and build upon.
Please submit by November 30, 2024, through this form.
Register
Please register for the hackathon before December 31, 2024, it is free of charge!
Seminar Series
Join us every third Thursday of the month at 11:00 (CET) exploring ways to become computationally more reproducible.
The seminar is free to attend and no registration is required. Join us on Zoom.
Upcoming Seminars
If you want to give a talk as part of this series, please contact Daniel Stekhoven.
| Date | Topic | Speaker |
|---|---|---|
| Reproducibility in practice – data analysis in a core facility | Hubert Rehrauer, Functional Genomics Center Zurich | |
| 2025-02-20 | TBA | TBA |
Past Seminars
If available recording of the seminars can be found here.
| Date | Topic | Speaker | Resources |
|---|---|---|---|
| Computational reproducibility as part of an agile and open approach to researchh | Geir Kjetil Ferkingstad Sandve, Universitetet i Oslo | slides | |
| 2024-06-20 | REANA: A platform for reproducible computational data analyses | Tibor Simko, CERN | slides, recording follows soon |
| 2024-05-16 | Ten Simple Rules for Good Research Practice | Simon Schwab, Swiss Transplant | slides, recording, paper |
| 2024-03-20 | Using computational reproducibility tools for benchmarking causal discovery | Jack Kuipers, ETH Zurich | slides, recording |
| 2024-02-21 | Reproducibility and beyond with Snakemake and Datavzrd | Johannes Köster, Uni Duisburg Essen | slides |
| 2024-01-17 | The Reproducible Research Platform – Towards FAIR and reproducible sharing of data, code and computational environments | Henry Lütcke, ETH Zurich | slides |
| 2023-12-13 | Sustainable data science with the Renku platform | Elisabet Capon Garcia, Swiss Data Science Center | slides, recording |
| 2023-11-15 | MaRDI TA2: Research Data and Reproducibility in Scientific Computing | Jens Saak, MPI for Dynamics of Complex Technical Systems | slides, recording |
| 2023-10-18 | Singularity Containers in Bioinformatics | Vipin Sreedharan, ETH Zurich | slides, recording follows soon |
| 2023-06-21 | Sustainable tool benchmarking and workflow development in Computational Biology | Kim Philipp Jablonski, Google | slides, recording |
| 2023-05-17 | Replicability, Reproducibility, and Reusability in Computer-based Experiments | Jan Heiland, MPI for Dynamics of Complex Technical Systems | slides |
| 2023-04-19 | LabKey Server for Reproducible Biomedical Research | Natalia Chicherova, ETH Zurich | slides, recording |