Given this background, the core problem we are trying to solve is the collaborative creation of reproducible computational narratives that can be used across a wide range of audiences and contexts. Collaboration is present in nearly all contexts where computational narratives are created: between two postdocs and a professor in the same research group between the writers, editors and visual designers of an online news site between the data scientists and business strategists at a large internet company or between a teacher and students in a university classroom. Multiple individuals need the ability to work together at the same time, on the code, data and narrative. Third, computational narratives are created in collaboration. Reproducibility has long been one of the foundations of the scientific method, but the rise of data science brings new challenges to scientific reproducibility, while simultaneously extending these questions to other domains like policy making, government or journalism. That is, other people - including the same scientist six months later - need to be able to understand exactly what was done (code, data and narrative) and be able to reliably reproduce the work in order to build new ideas off it. Second, these computational narratives need to be reproducible. Across all of these different audiences and contexts, core aspects of the computational narrative remain invariant. Eventually, it may even be important to enable non-coding lab scientists to perform that same statistical analyses and visualizations on data from new samples using a simplified graphical user interface.
Over time, however, that same individual will give talks to other researchers, or even non-technical audiences. For example, a biomedical researcher might originally perform statistical analyses and visualizations for a highly technical paper to be published in an academic journal. There are three fundamental aspects of these computational narratives that frame the problem we seek to address.įirst, a single computational narrative needs to span a wide range of contexts and audiences. Thus, in order for data, and the computations that process and visualize that data, to be useful for humans, they must be embedded into a narrative - a computational narrative - that tells a story for a particular audience and context. Humans, on the other hand, process the world through narratives. Computers are good at consuming, producing and processing data.
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