Last week I had the chance to give a talk at Barcelona Unseminar in Bioinformatics, which is an initiative from a group of researchers in bioinformatics aiming to promote the unseminars culture in Barcelona as a tool to share our knowledge (and ignorance!), experiences and interests around biological research and open science.

The topic of the talk was ‘Data Vizz – When science meets art’, and the aim of the unseminar was to give to researchers tools to communicate better their results and to improve how they can visualize data. For that reason we introduced concepts related to the fields of design, infographics, typography and of course, data visualization.

My talk was about dataviz, so I gave a small introduction divided into 3 concepts:

  1. The Grammar of Graphics: what is about? Introduction to the concept in order to explain that exists a foundation to decompose and analyze most of the graphics. Presentation of related work by Leland Wilkinson, Yuri Engelhardt and Hadley Wickham.
  2. Visualization Guidelines: To introduce concepts such Data Ink Ratio, the Lie Factor, tips on do’s and dont’s and so on.
  3. Interaction: Here instead of showing super-cool interactive graphics with stuff moving here and there, I decided to present the work of Bret Victor and his approach to concepts like Interactive Visualization and Explorable Explanations. I think this third point was a hit because for profiles such researchers, to present them tools or techniques to improve his scientific communication should be as ‘must’.
  4. Science publications should not be static PDF, but interactive articles that offer to the reader mechanisms to think about what he is reading and trying to understand!


So it was a great experience, and although there was not too much time available to go into the details (there is never enough time), the format of the “unseminar/unconference” offered a atmosphere that encourages the dissemination and participation


Posted in Data Visualization

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Data Visualization · Interactive · HCI · Open Data · Data Science · Digital Humanities

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