Over the last 10 years, Python has slowly inched towards becoming the most popular scientific computing language, beating or seriously challenging Matlab, R, Mathematica and many specialized languages (S, SAS, ...) in numerous applications.
A large part of this growth is driven by amazing community packages, such as numpy, scipy, scikits-learn, scikits-image, seaborn or pandas, just to name a few. Development of such packages represents a significant time investment by people working in academic environments. To be able to justify the investment of time into such package development and support, the developers usually associated them with a scientific article. The number of citations of those articles are considered as measures of the usefulness of articles and are required to justify the time spent on them. Unfortunately, as of now, a significant issue is that such packages are not cited despite being extensively used. Part of this is due to the difficulties with compiling the list of proper citations for each module (and, for libraries associated with multiple update publications, selecting the relevant citation). Part of this is due to users not realizing which of the modules they are using have associated publications and should be cited. To remediate to that situation, I suggest a __citation__ method associated to each package installation and import. Called from the __main__, __citation__() would scan __citation__ of all imported packages and return the list of all relevant top-level citations associated to the packages. As a scientific package developer working in academia, the problem is quite serious, and the solution seems relatively straightforward. What does Python core team think about addition and long-term maintenance of such a feature to the import and setup mechanisms? What do other users and scientific package developers think of such a mechanism for citations retrieval? Best, *Andrei Kucharavy*Post-Doc @ *Joel S. Bader* * Lab*Johns Hopkins University, Baltimore, USA.
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