Migrated from #5 for clarity, original idea by @jngrad
Our resources now include "qualified references", i.e. unique identifiers that can be queried in dedicated resolvers, including a "citekey" metadata field that can be resolved against the BibTeX file in the refs/ folder, just like ESPResSo does with doc/bibliography.bib. We could introduce a function pyMBE.pymbe_cite() to print to the terminal the BibTeX record of pyMBE and the BibTeX record of the pKa set that was used, or use a Sphinx plugin to run LaTeX in the background and print the formatted citations in a specific citation style. This is standard practice in the R ecosystem 1, and is very slowly coming into the Python ecosystem 2.
Migrated from #5 for clarity, original idea by @jngrad
Our resources now include "qualified references", i.e. unique identifiers that can be queried in dedicated resolvers, including a
"citekey"metadata field that can be resolved against the BibTeX file in therefs/folder, just like ESPResSo does withdoc/bibliography.bib. We could introduce a functionpyMBE.pymbe_cite()to print to the terminal the BibTeX record of pyMBE and the BibTeX record of the pKa set that was used, or use a Sphinx plugin to run LaTeX in the background and print the formatted citations in a specific citation style. This is standard practice in the R ecosystem 1, and is very slowly coming into the Python ecosystem 2.Footnotes
LaZerte 2021, "How to Cite R and R Packages", rOpenSci: https://ropensci.org/blog/2021/11/16/how-to-cite-r-and-r-packages/#how-to-cite-r-packages ↩
Python package citepy 0.5.0: https://pypi.org/project/citepy/ ↩