Reproducibility, Collaboration, and Communication for Exploratory and Visual Analyses

Visualization is an important data analysis method that allows scientists to explore a dataset without preconceived questions, and is thus crucial for hypothesis generation. When combined with algorithmic approaches, it bridges the gap between exploration and confirmation. Visualization is also essential in communicating research findings.

Current visualization tools, however, have a crucial shortcoming: the interactive visual exploration process is not captured, which means that the analysis steps cannot be shared. Being able to reproduce visual analysis sessions and enabling third parties to understand, modify, and extend analysis sessions can have a significant impact on transparency, reproducibility, and innovation of analysis processes. Furthermore, there is enormous potential to utilize visual analysis sessions to efficiently communicate data.

We propose to develop methods and tools that make this vision a reality. By capturing the visual analysis process and by enabling users to comment on their decisions, we will make visual analysis reproducible. We will also leverage the data about the analysis process to allow scientists to create "Vistories", which are interactive and narrated figures, to communicate their findings. Vistories do not only efficiently communicate the findings, but also give audiences the ability to retrace and modify an analysis. Demos for our prototype for this approach are accessible at

In this project, we will integrate our approach for visualization with methods for computational reproducibility (Jupyter Notebooks,; improve and harden our Vistories prototype; and build a platform for the scientific community to create and publish Vistories.

Our project will make two contributions to Open Science: First, we will provide analysts with an ecosystem to analyze and communicate data in a way that supports the publication of both the underlying data and the analysis process. Second, we will create a software ecosystem and provide documentation that will allow the community to integrate the Vistory approach into their tools.

Our proposal for the Open Science Prize on how to take vistories to the next level. It got rejected, but we still think it is worth your time.



Comparing Wealth vs. Life Excepectancy over time, illustrating the basic concepts of Vistories.



Reproducing previously published Use Cases of the StratomeX visualization technique, highlighting reproducibility and storytelling aspects of Vistories.

The source code for our prototype is available on GitHub. Vistories are built with

(in alphabetical order)

Nils Gehlenborg

Department of Biomedical Informatics at Harvard Medical School |  nils_gehlenborg |  ngehlenborg

Samuel Gratzl

Department of Computer Science at Johannes Kepler University Linz


Alexander Lex

Scientific Computing and Imaging Institute at University of Utah |  alexander_lex |  alexsb

Marc Streit

Department of Computer Science at Johannes Kepler University Linz |  marc_streit |  marc_streit

We thank Nicola Cosgrove (@NicolaLady) for contributions to the prototype and the video.

Vistories are based on a paper to appear at EuroVis 2016:

Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Nicola Cosgrove, Marc Streit
From Visual Exploration to Storytelling and Back Again
Computer Graphics Forum (EuroVis '16) (to appear), 2016