First we ran the code via the included binder. Then we tried to delete rawdata and download them and re-create figures from scratch. We also tried to install the package locally.
Different for each reviewer
To run locally, we needed to install:
conda env create -f environment.yml
Notebook 5 is intentionally missing, but this was unclear from the binder. Lack of master script linking notebook code. Unable to reproduce data without raw (deleted original data and tried to reproduce from pulldown)
Great use of the binder framework for sharing the code and documentation, we loved the pre-rendered html files. Setting it up as a binder that doesn't require the user to install anything locally was a great way to make the analysis quickly reproducible for everyone! The fact that the files were neatly split into separate analysis chunks was useful, and the step-by-step file provided a great overview linking everything together.
We didn't find the binder button immediately, so it was not obvious that we could inspect everything in an online pre-configured environment. There were no guidelines on how to run the analysis locally. Which commands do you need to run to install the dependencies via the
envornment.yml file? How do I start the jupyter lab after installing the dependencies locally?
Figuring out which codebook does what, and why. The README nicely describes all the tools that were used, but did not include a big-picture description of what problem the project is solving. The step-by-step was a nice file, but lacked the more general introduction as well.
Documentation was good, they've shown how each script should be pieced together with a brief description of each step
Very transparent paper/code, no corners have been cut or hidden from the public view
Data/Interim/CIGALEOutputs, must be created while the sripts are running, in case user deletes them.
We'd like to thank the authors for putting so much effort into making their workflow reproducible! They went above and beyond, and the comments above are mostly nitpicks that prevent the workflow from being fully reproducible from scratch.