CEOS Jupyter Notebooks Best Practice

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualisations and narrative text. Uses include data cleaning and transformation, numerical simulation, statistical modelling, data visualisation, machine learning, and much more. A Jupyter Notebook allows you to combine rich documentation, live and adaptable code, and data visualisations. It can also be used as a tool to share your data analysis with others, collaborate, teach, and promote reproducible science. The Jupyter Notebook began as an IPython Notebook; this use of Python makes it an ideal starting point for many people using EO data for the first time. In addition, it currently supports around 40 programming languages, including Python, R and Julia. While Jupyter Notebooks can be a valuable resource, there are issues surrounding input data, processing, technical dependencies and quality. Poor quality notebooks with hidden dependencies may cause new users a lot of problems. The purpose of this document is to encourage the development of high quality Jupyter notebooks, which are of genuine benefit to the broader community, in order to ensure their discoverability, accessibility and reusability.