Open dprada opened 3 years ago
There are some options already in the market: ducredit, sempervirens, citepy.
Do they fit our needs?
As @Daniel-Ibarrola suggested, we have to evaluate the use of duecredit. And if this library does not meet our needs, we should try to create a new library.
Imagine we add a function to our scientific library to implement the method 'Misfit Sampling' published in the paper X:
def running_misfit_sampling():
bla, bla
Now, let's also imagine a potential user of our scientific library. This fellow used the library in a python script or in a jupyter notebook to run some analysis, and now it is time for him or her to write a report. Could we provide with a tool to easily extract the cites used along his or her workflow?
What if we have a library to do something like:
@cite(key='Ibarrola2023Misfit', comment='Method to run a smart sampling strategy based on the Misfit method')
def running_misfit_sampling():
bla, bla
or
@cite(bibtex= {year: 2023, author: 'D. Ibarrola', title: 'Misfit sampling vs. adaptive sampling'}, comment='Method to run a smart sampling strategy based on the Misfit method')
def running_misfit_sampling():
bla, bla
Maybe then... our potential user could add at the end of the jupyter notebook or script something like this
give_credit()
to print out a list of papers to be cited in whatever format.
Or... maybe, the collection of the papers to be cited can also be extracted with a CLI interface:
give_credit my_analysis.py -o my_analysis.bib
If MolSysMT is going to be used as support to build workflows, an auxiliary library to cite everything that was used along that workflow has to be included. If this library does not exist already, we may have to implement it.