Advancing reproducible research by publishing notebooks as interactive sandboxes
Abstract
Best practices such as open-source software and notebooks have played a pivotal role to promote the FAIR principles of open science. For example, (1) well-documented scripts and notebooks with rich narratives are deposited at a trusted data centre, (2) interactive notebooks can be run on-demand as a web service, and (3) web apps provide nice user interfaces to explore research outputs. These approaches are well suited for the intended use, but they are not particularly helpful to reproduce elements of a research analysis or allow users to consider what-if analysis by changing a few lines of code. Using the R language as an example, we propose using the learnr package to expose certain code chunks in R markdown so that users can readily experiment with them in guided, isolated and resettable code sandboxes. Our approach does not replace the existing use of notebooks and web apps (e.g. R Shiny), but it adds another level of abstraction between them to promote reproducible science. We illustrate our approach with the sandbox app we produced accompanying a recent scientific paper.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMIN55D..05T