j6k4m8
commented
3 years ago I have adapted the code to a fully Python-based implementation here, which runs quite fast (~1 minute on my laptop with my janky internet connection).
Main differences:
The Good:
- I use biblatexparser which dramatically increases speed and eliminates a few parsing errors I encountered on more esoteric cite types in the cleanBib implementation
- I cache results so that the same author is not looked up more than once (which saves gender-api credits too)
The Bad:
I don't "heal" broken references by looking them up on crossref (the cleanBib implementation didn't catch that many new datapoints in my bib file so I didn't bother adding it, but maybe this is important!)I don't perform the full analysis yet, I just generate the predictions/data and save to a pandas dataframe / csv- I don't check for first/last author of the current paper. (!!!)
The I-Don't-Know-if-It's-Good-Or-Bad:
- I think the race model I use is very slightly different than the one in here
I don't (yet) count single-author papers
Just dropping this here in case it's helpful to you or if you are able to repurpose any of the code. I definitely want to respect your emphasis on reproducibility.
(This is also explicit permission to use that code if you want any of it)
dalejn
To help streamline and simplify the Binder, note that the code uses both Python and R. The most straightfoward way to modify the code is to do so within the Jupyter Notebook launched through Binder (see instructions https://github.com/dalejn/cleanBib#instructions). Then, after modifying, you can save/export the Jupyter Notebook with your changes by saving it as an .ipynb file on your computer. Please test the changes in your own Github branch and/or attach your code to a reply here with a general description of the problem being addressed and the change to the code.