Reflections
This page contains reflections on the facilitators and barriers to this reproduction, as well as a full list of the troubleshooting steps taken to reproduce this work.
What would have helped facilitate this reproduction?
Figure - provide code to produce the figure
Note: Although not needed for the reproduction itself, when I tried to amend the name and location of the csv file output the model for use in tests, this was very tricky to do as it was hard coded into the scripts and I found difficult to amend due to how the model is run and set up.
What did help facilitate this reproduction?
README - clear README with instructions on how to run the model.
Data dictionary for input parameters - although I didn’t need this, this would have been great if I needed to change the input parameters at all.
Environment - stated version of Python and provided requirements file (simpy, with version given in the app repository).
Simple results spreadsheet - the layout of the results spreadsheet was simple, understandable and easy to read, making it easy to adapt into figures
Simple figures - the figures were simply (just means and CI which are already calculated).
Speed of model - the model was super quick (seconds!) which made it really easy to run and re-run each time.
Random seed - the authors included a random seed so the results I got were identical to the original (so no need for any subjectivity in deciding whether its similar enough, as I could perfectly reproduce).