ARDC template, Jupyter Notebook, FAIR, Findable, Accessible, Interoperable, Reproducible
Jupyter Notebooks (JN) have been widely used in research and data science (Mendez et al., 2019). Despite its widespread use, various challenges exist in making JN reproducible, accessible and robust. A plausible approach is to use FAIR principles to make JN findable, accessible, interoperable and reproducible. However FAIR principles are high level aspirations in making research robust and can be difficult to apply in practice. This work aims to provide practical recommendations in making JN FAIR and it closely follows the FAIR for Research Software (FAIR4RS) approach.
This repo serves as an example with instructions to create a FAIR Jupyter Notebook. The example Jupyter notebook file is sourced from matplotlib documentation.
Badges allow for the instant launch of the notebook files in a reproducible manner.
Binderhub service hosted on ARDC's Nectar research cloud
A persistent identifier, created once the research is completed. Freezing the released Jupyter Notebook, which makes referring to the research software possible.
