The IPython Notebook has evolved into the Jupyter project. This free, open-source hook into many different programming languages simplifies some types of software experimentation. Jupyter’s advocates have attracted some generous institutional and foundational funding to develop the tool. The project has posted its winning proposal touting it as the “Engine of Collaborative Data Science” and ramming home the “computational narrative” as the means. Authors write notebooks with embedded data and code for a variety of audiences and interested readers can run computations for themselves.
It isn’t clear how this will work for complex algorithms that require a lot of computing power. Notebooks can be static presentations in those cases but then they have no advantage over a conventional report. The current Notebook doesn’t have the tools for real software development or algorithm analysis. Savvy users recommend not relying on them beyond certain limits. Variable inspection, debugging, and change control are all on the roadmap for the new JupyterLab and the project’s claims can’t be addressed until we see how well these work. Every addition will require screen space which will mean less space for the data and visualizations. It might in time be as convenient as the current (not-free) Matlab User Interface but it will take work to get there.
Yes, this is the funded scope and if it existed, they would be proposing something else. The Principal Investigators agree that other Notebook interfaces have been around for a long time but imply that cost and proprietary architectures have been the principal roadblocks to their impact. The Notebook metaphor itself is left alone and that’s puzzling. There should be plenty of data (ha!) on how prior interfaces have or have not revolutionized the areas they claimed they were going to revolutionize. The proposal does devote detail to the enabling technologies, the support of large companies, and the future constituency.
But, it is the word ‘narrative’ gets my hackles up. It sounds disturbingly similar to ‘pitch’ and the pitch culture is dangerous. People can be led down a bad path any number of ways – yellow journalism, Powerpoint, or just outright demagoguery. Groups can lie just as well as individuals and Notebooks, like vaunted social media, can just as easily be co-opted for b.s. Data-driven decisionmaking is resurgent yet cyclical. It ebbs when the data don’t match the preconceptions – the internal narratives – of the ones with the money. We may, as a society, have gone past failsafe in handing over control to the unworthy.