In the middle of the COVID-19 pandemic, this paper provided important evidence regarding the effect of misinformation on vaccination intent. Its analyses and conclusions were extremely important for decision makers. Therefore, it is also important that the analyses are reproducible.
Popular descriptors for machine learning potentials such as the Behler-Parinello atom centred symmetry functions (ACSF) or the Smooth Overlap of Interatomic Potentials (SOAP) are widely used but so far not much attention has been paid to optimising how many descriptor components need to be included to give good results.
It uses the drake R package that should make reproducibility of R projects much easier (just run make.R and you're done). However, it does depend on very specific package versions, which are provided by the accompanying docker image.