Collaborative data science: High level guidance for ethical scientific peer reviews
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Preamble
Catalan Castellers are collaborating (Wikipedia) |
Availability of distributed code tracking tools and associated collaborative tools make life much easier in building collaborative scientific tools and products. This is now especially much more important in data science as it is applied in many different industries as a de-facto standard. Essentially a computational science field in academics now become industry-wide practice.
Peer-review is a pull request
Technical excellence does come with decent behaviour
- Don’t be a jerk We should not request things that we do not practice ourselves or invent new standards on the fly. If we do, then give a hand in implementing it.
- Focus on the scope and be considerate We should not request things that extend the scope of the task much further than the originally stated scope.
- Nitpicking is not attention to details Attention to details is quite valuable but excessive nitpicking is not.
- Be objective and don’t seek revenge If someone of your recommendations on PRs is not accepted by other colleague don’t seek revenge on his suggestions on your PRs by declining her/his suggestions as an act of revenge or create hostility towards that person.
Conclusion
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