It's not academia but Kaggle that's really been on the forefront of building portable and reproducible computational pipelines. The real key is incentives and

It's not academia but Kaggle that's really been on the forefront of building portable and reproducible computational pipelines. The real key is incentives and there are two that standout to me: - Incentive to get others to "star" and fork your code makes the coder compete to not only have an accurate result, but also prioritize producing code/notebooks that are digestible and instructive. That includes liberal commenting/markup, idiomatic syntax and patterns, diagnostic figures, and the use of modern and standard libraries. - There is an incentive to move with the community on best practices for the libraries while still allowing experimental libraries. Traditionally, there is the incentive of inertia: e.g. "I always do my modelling in Lisp, and I won't change because then I'd be less productive". But with kaggle, to learn from the insights and advances of others, you need to have an ability to work with the developing common toolset. In academia, if these incentives were given weight on par with publication and citation then we'd see the tools and practices fall into place.