We think this is an interesting paper for anyone who wants to learn to build an API with the R package plumber. This is a novel method in health economics, but we believe will help improve the transparency of modelling methods in our field.
There are many applications to multi-MeV X-rays. Their penetrative properties make them good for scanning dense objects for industry, and their ionising properties can destroy tumours in radiotherapy. They are also around the energy of nuclear transitions, so they can trigger nuclear reactions to break down nuclear waste into medical isotopes, or to reveal smuggled nuclear-materials for port security. Laser-driven X-ray generation offers a compact and efficient way to create a bright source of X-rays, without having to construct a large synchrotron. To fully utilise this capability, work on optimising the target design and understanding the underlying X-ray mechanisms are essential. The hybrid-PIC code is in a unique position to model the full interaction, so its ease-of-use and reproducibility are crucial for this field to develop.
Because: - Two fellow PhDs working on different topics have been able to reproduce some figures by following the README instructions and I hope this extends to other people - I've tried to incorporate as many of the best practices as possible to make my code and data open and accessible - I've tried to make sure that my data is exactly reproducible with the specified random seed strategy - the paper suggests a method that should be useful to other researchers in my field, which is not useful unless my results are reproducible
The original data took quite a while to produce for a previous paper, but for this paper, all tables and figures should be exactly reproducible by simply running the jupyter notebook.
This was my third attempt at making a paper fully reproducible. To date I it's the most reproducible that I have published. I'm interested to know what stumbling blocks exist that I'm not aware of (aside from needing software like ArcGIS to fully rerun the complete analysis).
