Notebooks have become very popular solutions to write data workflows and experiments. We have decided to add support for two of the main notebooks in use nowadays: Jupyter for data mining and Observable for visualisation.
These type of notebooks are an important part of the publishing pipeline for the Atlas of Data, as it allow to access directly the source code and makes it easier to replicate. Both allows the embedding of figures and text.
Jupyter Notebook is arguably the most popular notebook solution for data analysis. Originally based on iPython, it supports today many languages and environments. It is entirely open-source and can be installed locally or on a remote server.
The upside is that you can rely on the running Observable engine to embed your code dynamically anywhere, as demonstrated below. Thanks to Visions Carto for the explanations.