Social networking, blogging, tagging, recommender systems, and other collaborative technologies have changed the face of the Internet, and life as we know it. For sociologists, anthropologists, and even physicists and mathematicians, such services have provided a constant stream of data and information about the lives of millions of people. Like anyone who has observed a visualised social network, information tends to be dense, complex, and difficult to understand: all one can conclude is that humanity is a series of nodes stuck in a web. The question then becomes, "How can we show this information in a clear, intuitive, and potentially beautiful way?"

What is most interesting is that through the same collaboration that has caused this deluge of data, people are developing the tools necessary to clarify the underlying systems of sites like Wikipedia, markets like eBay, and social graphs like Facebook. Already, sites like Visual Complexity are springing up where people can share individual visualisations in an effort to showcase some of the best practices in this fusion of art and statistics. Magazines like Seed and exhibits like the Museum of Modern Art's Design and the Elastic Mind are even promoting these efforts in magazines and galleries.

Three trends underscore the exciting developments in and between art and quantitative research. First and foremost, of course, is the data itself. If you have used some of the nifty tools that let you trace Wikipedia edits to government or corporate entities, read blogs based on recommendations from your RSS reader, or used Facebook apps that illustrate your social network, you have experienced the benefits of the open availability of data. Of course, some of this data is proprietary, and some completely unavailable to the public-at-large or other websites. While movements like Creative Commons have shifted the way we view media sharing, similar views on personal data -- or even public data stored in private servers – are unclear. Collaboration has been a blessing to researchers, though many legal questions remain unanswered. These are both commercial and personal issues: to what extent do you want your information publicly available, and how should corporations and private entities control the data they collect?

Regardless of the answers to the questions above, however, data is available and one can analyze this information. Much of this work is being aided by visualisation software, and while one can still build an informative graph using a standard spreadsheet programme, most tools go far beyond conventional charts and tables. To name a few, one can easily download free and open source projects like Processing, Prefuse, and Piccolo to illustrate data in a new and even interactive way. Viewing the galleries of these individual tools can illustrate just how creative and complex visualisation can be -– especially once you explore the images of exhibits like Nyte or NameVoyager, built using Processing and Prefuse, respectively.

However, using visualisation APIs to create new ways to interact with data is still a lonely experience. The web-based collaborative tools are now allowing people to work together to illustrate the data that, ironically, is built through other forms of social collaboration. The simplest approach to this is crowdsourcing graphics design – and while it's not explicitly focused on data visualisation, it can definitely be used for this process. Simply put: upload some data and ask people to illustrate it – though usually you will have to pay someone for the best design. You can do this on sites like 99designs or Design Contest.

One can go a step further and actually build an infrastructure for collaborative data visualisation. For a specific example, there is OpenStreetMap, a map application that anyone can edit. The concept is interesting in that it takes sites like Wikipedia a step further – why leave collaboration on information simply at the text-based level? While not open source, this is also true for IBM's Many Eyes service, where people can upload data and code to make new illustrations and collaboratively explore new ways to visualise data. This tool has even been used in academic courses, as a way to teach students about data visualisation and exploration.

Through the various open source tools and collaborative graphics websites, one can do a great deal in visualization of information, data, and statistics. The usefulness of this teaching tool goes far beyond the classroom –- not only does it help us understand the data, it also helps us understand ourselves and how we interact with other people.

tags: International science-research visualisation data science computing art media design

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