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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