Over the past two years I’ve been noticing a rise in what I like to call “shock and awe” graphs in digital humanities, designed to overwhelm their audience and perhaps even to evoke doubt in one’s own abilities to compete in the same scholarly conversation. These graphics are both incredibly complex representations of data, and incredibly beautiful. If we got rid of the axes, we might even be tempted to hang them as art. A colleague of mine used the term “poster graph” to describe these works. The idea behind that name was that the graph looked nice enough to blow up and put on a poster. Implicitly, this colleague suggested that represented in this manner, the data was likely to impress and captivate. Great. But are complex graphs good for scholarship? Scholarship shared between academics is not inherently meant to impress. It is meant for making discoveries. And so, while complex graphs are beautiful, they have a time and place.
Exploring data is certainly one of those times. Complex representations of data are sometimes the only way we can make some types of discoveries. Our eyes are, after all, great at noticing patterns. In a recent example (of which I was quite openly critical), trends in a set of data only became evident when it was plotted logarithmically. This graph then led the researchers on the trail of some interesting discoveries that would not have otherwise been possible. I have no issue with this. I have no issue with quantitative analysis.
I also have no issue with attempting to engage an audience who might not otherwise be interested in the research. I’m always thrilled to see historians, archaeologists, and mathematicians discussing their work on TV or on radio. That’s fantastic. And in those cases, a “shock and awe” graph is probably appropriate. After all we have to sell what we do if we hope to compete with the Hollywood pros and the increasingly popular data journalists in major news outlets for the scant attention of the masses.
But I do have issue with shock and awe graphs sneaking into work intended for academic colleagues – particularly in peer reviewed work, and particularly when the complexity of the graph is not absolutely necessary to the conveyance of information. I do have issue with the fact that many very intelligent people who are responsible for evaluating the truth of these claims do not have the skills to interrogate these complex visualizations. These graphs have seemingly come out of nowhere for many who have spent their entire careers working almost exclusively with text and perhaps only simple numbers. For interdisciplinary work, there is a good chance that the first time many researchers will come across a “shock and awe” graph is when they have been handed a paper to review for a journal.