Making visualizations like maps or charts is the end product for many data journalism research. When you want to explain the structure of the research and the different steps of the data journalism research, problems emerge. Or when you want to make the research more transparent by sharing the outcome of the different steps of the research. For example I am using R for analysis and plotly for visualizations; for showing the different steps I am have a text describing the whole process in markdown. During the lecture or a training you have to switch from one application to another. Jupyter notebooks solves this problem, because by using different kernels in the notebook you can show text in markdown, calculations in R, and visualizations in plotly. You can also share the notebook with the data, so anybody can after downloading follow the research process step by step.