It worked on my laptop! - how to approach reproducibility in astronomy?
2020-11-10, 08:00–09:30, Times in UTC

With large observatories that provide data to thousands of astronomers around the world already online or in the design phase and under construction, it is now more important than ever to approach the problem of reproducibility in astronomy. The last few years have seen a wide adoption of solutions that aim to address some of the reproducibility concerns, such as containers and Jupyter Notebooks. They help to provide a consistent processing environment by, for example, locking users to a single version of Python. This can, however, provide a false sense of security as on the lowest level, these solutions do not take any possible hardware differences into account. On the higher level, the lack of clear software and data format documentation can lead to easily-avoided mistakes. This is especially important in the new era of multi-wavelength astronomy where teams from different backgrounds, using different tools and file formats, come together to solve the same problem.

Considering all of the above, what do we expect from reproducibility? What are we willing to sacrifice to achieve it (and do we have to sacrifice anything at all?)? Can we as a wider community come together and develop a clear set of guidelines and standards that will ensure the maximum possible reproducibility? If 100% reproducibility is not possible, how can we ensure that all the relevant parties are aware of the possible shortcomings and can include them in their analysis?

Theme – Multi-Messenger Astronomy, Data Processing Pipelines and Science-Ready Data, Data Interoperability, Open Source Software and Community Development in Astronomy