Go to: Why Computational Reproducibility? Who we are and how we work Seminar Series
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.
After our Q1 coordination meeting in January 2024, we have set out to better organise the task force and come up with a roadmap by summer.
If you feel up to the challenge, please contact Daniel Stekhoven, to join the group.
Seminar Series
Join us every third Thursday of the month at 11:00 (CET) exploring ways to become computationally more reproducible. In this seminar series, we will explore the challenges and opportunities of achieving computational reproducibility in research. We will discuss best practices for data management, code sharing, and documentation, as well as tools and techniques for achieving reproducible results. Join us as we delve into this important topic and work towards enhancing the reproducibility and transparency of scientific research.
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 |
|---|---|---|
| 2024-05-16 | Ten Simple Rules for Good Research Practice | Simon Schwab, Swiss Transplant |
| 2024-06-20 | Available slot | TBA |
| Jul + Aug | Summer break | |
| 2024-09-19 | Available slot | TBA |
Past Seminars
If available slides and recording of the seminars can be found here.
| Date | Topic | Speaker | Resources |
|---|---|---|---|
| 2024-03-20 | Using computational reproducibility tools for benchmarking causal discovery | Jack Kuipers, ETH Zurich | slides, recording follows soon |
| 2024-02-21 | Transparency, reproducibility and the democratization of an ecosystem - the benefits of Snakemake 8 | Johannes Köster, University of 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, recording |
| 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 |
| 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 |
