DataArt is a new movement within art, that aims to bring alive the invisible, by merging the visual language of art and data science. It offers a deeper understanding of our word by conceptualizing data in a way that we humans can penetrate its meaning and messageAlbert László Barabási,
Left: Cover for Nature 150 anniversary
By Luke Whyte, Editorial Director
Lead by Albert László Barabási, BarabásiLab is one of the most influential labs in network science. It brings together scientists, designers, and artists, united by a desire to understand networks and complexity. Over 25 years, BLab has pioneered the visual language of networks and DataArt.
I sat down with Barabási to discuss their work and process.
Can we start by talking a little bit about your process? How do projects begin and ideas generated? Are you looking for new trends based on a hypothesis?
Our art is deeply symbiotic with the BarabásiLab’s 25 year long journey of understand complexity and networks. The art aids our research and our research is the inspiration of our art. By bringing alive the systems we study, we build better hypotheses and helps understand the processes we explore.
Is there a lot of data collection and cleaning? What does this look like? FOIA requests? Web scrapers? Are you working in Python? R? How does data get stored?
10% inspiration 90% data cleaning and collection. That is the reality. But we are agnostic to the tools, we scape data where it is allowed, we collaborate with data sources, and we beg for data… And we use any method possible to process it, agnostic to language and tools.
How do you decide how to express the data? Is it a process of experimenting with different common techniques and algorithms for expressing relationships and visualizing systems?
It a very long experimentation process, that can easily last 6 months or so. One one end what we do, DataArt, is a new form of realism, because we need to preserve the image’s fidelity to the data. We are deeply realist in this sense, reaching back to the tradition of realism and even naturalism. But DataArt is also story telling, so we have incredible creative freedom on what visuals metaphors we use to express the content and to tell our story.
What software are you utilizing?
This is a common question—people often perceive that we have some magical software, so we often get this question. The reality is that we build dedicated algorithms for each project. For example, for making the data sculptures, we had to build the theoretical framework, develop the mathematical formula that capture the esthetics in a way that a computer can understand. It is that formula that generats all of our data sculptures, and it was such an important development that Nature magazine published it and featured it on the cover of the journal. See:
Perhaps I’m still misunderstanding but, could you elaborate on “capture the esthetics in a way that a computer can understand”? In other words, what are you using (eg. programming languages and libraries) to express these formulas spatially?
The answer to that question is in this research paper. (For the sculpture shown below), we indeed did build a formula, that is displayed on the wall in the museum, and the one you see in the paper as well, that mathematically captures the evolution of the data sculptures. It does so by minimizing the total link length, which pulls nodes connected to each other closer and reveals communities.
Once we have the equation, we run molecular dynamics simulation to find the final shape of the network. It is all home build software, but it is also released to the community in a Github coming with the research paper.
Do you have different people on the team working on the more mathematical components and others working on the aesthetic components? How are aesthetic decisions – things like color schemes – made?
BarabásiLab has about 30 members, from postdocs to students and designers. Each work is the results of a team, where the data comes from the research of the postdocs and the research, and so does the narrative, and it is them, together with the designers, who work on the visual language as well. It is important to understand that the role of the designer in the lab is not to create “pretty” images, but to help us in our journey understanding complexity. So they are data designers, embedded within the research teams. And decisions are driven by the clarity of the story telling, and of course esthetics as well. We even research some aspects—one PhD student’s research focuses only on color choices in DataArt.
Zooming out, how do you define data art?
DataArt is a new movement within art, that aims to bring alive the invisible, by merging the visual language of art and data science. It offers a deeper understanding of our word by conceptualizing data in a way that we humans can penetrate its meaning and message. Our practice is in particular rooted in DataRealism and DataFidelity, as it is important of us that the work we generate accurately represent the systems we study.
What is the origin story in data art? Who are some of the important names to come before you and milestones in its growth?
There are too many threads to do justice to it—and I am myself researching this space as we speak. There was a fabulous tradition of representing data by hand in the art space, like Mark Lombardi, who drew exquisite networks of many systems, like terrorist networks, by pencil, or the very inspiring practice of Hans Haacke, who has represented multiple dimensions of data as a critique of the art world. In the network space, I am also inspired by the formal language of Tomas Saraceno, or Gertrud Louise Goldschmidt, a Venezuelan artist with a fabulous body of work in the 1960s and 70s.
We stand on the shoulders of giants, and what distinguishes our work from theirs is big data—an ability process massive amounts of data and find a proverbial needle in our haystack, hence to offer a narrative whose depth and level of detail was not possible to them.
Where do you see data art going in the future? Particularly, what is its importance in a world filled with an increasingly large amount of recorded data?
As data take over every aspect of our life, I am convinced that DataArt will become a robust and legitimate movement in art. We will look back at this years as the birth of a movement, and ism within the art space. It must, as it explores a version of our reality and existence that we deeply desire to see and understand. And the role of art is to bring alive this invisible, but very consequential aspects of our existence.
How do you expect the way we visualize trends in, and interact with data, to evolve in the future? Will new technology open new opportunities? Do you feel the field of data art will grow? Will we see more data art that works with real time data?
Many advances in both art and science are driven by new technologies, and DataArt is particularly driven by them. In many ways, crytoartists right now are exceptionally creative, and I am very inspired by them. As the possibilities to show 3d objects as true explorable obj files, virtual and augmented reality, generative works, AI and neural networks and other innovations go mainstream, becoming part of artistic practice, just like the canvass and the paint was for generations before, it is technological and artistic revolution in making.