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Activity Number: 104 - Visualization and Reproducibility - Challenges and Best Practices
Type: Invited
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Graphics
Abstract #326904
Title: The Extended Reproducibility Phenotype - Interactive Graphics Edition
Author(s): Gabriel Becker* and Vivek Ramaswamy and Nolan Nichols and Altaf Kassam and Dinakar Kulkarni
Companies: Genentech Research and Genentech Research and Genentech Research and Genentech Research and Genentech Research
Keywords: reproducibility; computing; interactive graphics; R; shiny; reproducible research

Results are most useful when we can trust, understand, reason about, reuse, and extend them. In the case of static (script-generated) results, provided that the data and software used are available, the script which generated a result embodies both the ability to computationally verify that result - provided the data and software are available - and the list of the methods applied and computational steps used to create it. This allows the reproducibility of a static result to provide, in principle, assurances about our ability to trust, understand and reason about the result. When discussing reproducibility of a particular state in an interactive graphic, however, the analogs to these two aspects of the generating script are decoupled; they are embodied by the state itself, and the history of the user's actions, respectively. We will present work on extending a reproducibility and discoverability-based result management platform designed for static results and applying the underlying conceptual framework to states within shiny-based interactive graphics in R.

Authors who are presenting talks have a * after their name.

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