Open and collaborative science: 4+ years of going to the extreme

The goals of open science include easy reproducibility of research results, transparency of research methods and re-usability of artefacts, e.g. data, code, and graphics. Consequently, open science is expected to foster scientific collaboration and sustainability of research, as it enables building on each others' methods and results for many years and decades to come.

Here we report about our collective attempts in the last 4-10 years of taking open science to the extreme by using exclusively open formats, open-source software, sharing all stages of our work online and recording workflows and provenance of code and data. Most of our analyses are carried out in Jupyter Notebooks, which are all shared online through gitlab. In these notebooks and our python-analyses, we integrate the python package essm for transparent and easily reproducible mathematical derivations. For more complex analyses, including large model runs, we use the tool Renku of the Swiss Data Science Center in order to record workflows and provenance of code and data.

Find out where we succeeded, where we failed, what we gained and what we lost in pursuing open science to the extreme. Hear about the views and experiences with open science at the undergraduate, postgraduate, postdoc, engineer and senior researcher level. Eventually, we will also report about what we are still missing for entirely reproducible, verifiable, and reusable open science. We hope we can foster a debate about good open science practices, and how we can remove obstacles that are still in our way.