Open data, trust and data/visual literacy

“When I opened my twitter timeline on 21 June, a stream of tweets announced the publication of two reports relating to open access and open data: The Royal Society’s report on Science as a Public Enterprise (plus an article about it in the THE and a Nature news blog) and the RCUK’s Open Data Dialogue final report. There was talk about open access, accessible information, intelligent openness, data communication and so on. This calls up images of interested individuals pouring intelligently over openly accessible scientific articles, data and data sets in order to make up their own minds about issues that affect their lives... Social scientists, art historians, science communicators, and sociologists of science are beginning to study the relation between data sets, data visualisation technologies, aesthetics, truth and trust... The issue of trust in particular is important here. The underlying premise of some of the open access and open data agendas seems to be that freely available data will generate more trust in science. There also seems to be an assumption that people just don’t trust science or scientists to interpret data for them and that they therefore want to double check what’s going on. But is this actually possible in an age of what Geoffrey Boulton calls a ‘data deluge’, where much of the data generation, visualisation and interpretation is left to the ‘machine’ (the data crunching software with its algorithms)? The question then becomes: Can one trust the machine and/or algorithm? And what does this mean for the desired accessibility, intelligibility, assessability and usability of the data by non-specialists, be they individuals or groups of individuals? ... There is a tension here, it seems, between on the one hand individuals wanting to scrutinise data and data sets (and their ability to do so without, at least initially, having the necessary expertise and tools) and on the other hand (special interest) groups or communities scrutinising the data (using novel tools which might not be available to individuals). This tension between individuals, groups, technologies and tools has consequences for the democratic governance and politics of science. Let us explore one aspect of this conundrum in a bit more detail. Science, technology, engineering, mathematics and medicine increasingly employ images, imaging technologies and systematic visualisations of data to formulate problems, report on discoveries, and propose new avenues of research and treatment. Advanced imaging and data visualisation technologies allow us to see the unseeable (the incredibly small, the incredibly large, the incredibly far away or the incredibly complex), to integrate and map huge amounts of information, to simulate or model the future and much more. However, the progressive sophistication of such technologies, their proliferation and the increasing ease with which they can be used, pose challenges to science and society. These emergent issues may also challenge received understandings of the relationship between science and politics. To make political decisions in a modern world, policy makers have to rely on scientific information (and the same applies to citizens) (which increasingly also includes data and maps of social, political and digital networks etc)... Information researchers have stressed that this visualisation of information ‘is not the mere decoration of factual information. It is elemental to the construction of meaning and how it is perceived.’ It’s what Richard Saul Wurman calls ‘the design of understanding...’ As understanding determines what social and political actions we perform or encourage or reward as individuals, as politicians and as communities, we should really have a better understanding of how this design of understanding works. There needs to be more research into the politics and rhetoric of visual persuasion, for example. As Tony Prichard, an expert on design for visual communication, has pointed out, ‘successive British governments fail to acknowledge visual methods as intrinsic to solving many of the challenges facing society...‘ Recently there have been many calls for politicians to gain a better understanding of science and the scientific method. There should also be a call for politicians to gain a better understanding of the (visual) ‘design of understanding’, that is, the production of visual maps based on very large data sets using data mining and data visualisation tools. A starting point may be reading The Guardian‘s data blog. Calling for open access, accessible information, intelligent openness, data communication and transparency is not enough. We also need something that might be called data literacy or data visualisation literacy (or, dare I say, public understanding of data and data visualisation). Without it all the open data in the world and all the visually pleasing ‘maps’ out there will remain incomprehensible and meaningless to citizens and politicians alike or, even worse, may be utterly misleading. Without the ability to read the data and make sense of data visualisations, de