Data visualisation (or DataViz, the graphic representation of data) is a very powerful tool to make information easy and attractive to inquisitive readers.

My journey in coding for data visualizations is rather recent, yet I have much experience in graphic design for science communications. In my opinion, the art of data visualization requires more than simply applying a code on Python, R, or whatever the platform used, to a bunch of data. It requires a deep sense of aesthetics and knowledge in the area the data belongs to, in order to create the most functional visual representation.

This year, the Central European University took the initiative of exhibiting cutting edge data visualizations to communicate findings on behavioral or social research. The event, known as Data Stories, calls for artists and data scientists to present their creative visualizations. I participated in this fantastic opportunity to share my creative work on data science. What I did was to take one simple question about leadership in perfoming arts, and I called my work: “Beyond ballet: the leading creators of our favorite ballets”.

This project consisted of collecting structured data from the Wikidata about ballet creators: composers, choreographers, librettists and costume designers.

A collaboration is defined as artists that appear as co-creators of the same work of ballet.

With this information, I reconstructed the collaboration network of ballet creators and used objective network metrics to determine the influential power of each artist within the network. This would give me the names of leading ballet creators.

The methodological part of data processing was very straightforward, given my previous works on network visualization. Yet the whole creation of the data story was incredibly challenging. I started selecting the most important networks I wanted to show. I carefully selected nodes’ colors to make it easily differentiable by artists type and communities.

Once I had my network selection, I exported the png files and explored different backgrounds trying not to lose any detail that contributes to accurately understand the collaboration network of ballet creators. Since I wanted to emphasize network properties, I selected a clear color. Snow white was not the option, neither black swan feathers, but one clear tone in between with a soft gradient would be idea.

I wanted to complement my data story with an attractive photograph by Karolina Kuras. She secretly sponsors my academic slides with her outstanding photographs… so why not again? I have used the photo of the ballerina in the pentagonal window before, when I did the pitch competition for postgrad students.

One slide for Postgrad Pitch competition at the UDD.

The problem with this beautiful image is the need to distribute the information in a symmetric fashion. I did one sketch of how I wanted to organize the information, but putting it into the real canvas was a tough trade-off: there was not enough room to make the information fluid for optimal understanding. The photograph was forcing me to to sacrify clarity of the info display. It was definitely not a go for the data story…

I did not give up on the idea of the photograph of the ballerina. Yet I thought that I should first organize the information and data I wanted to convey, then take care of the extra aesthetic elements. I worked in simple white background, managed different typographies and put everything in the right order.

Then, I explored different layers, colors, textures and visual effects to make this data story more appealing. I selected a photograph from Karolina’s professional portfolio (once again!) to inspire the beauty and subtlety of ballet. I ended up with a minimalistic data story, good enough to be exhibited at seventh edition of the Data Stories, Research Visualization Exhibition at CEU Budapest.

Part of this research was accepted to be virtually presented at the WikiWorkshop during The Web Conference 2020, Taipei. More details on the findings of this collaborative work, here.

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