Author: Reader

The point of visualization is usually to reveal as much of the structure of a dataset as possible. But what if the data is sensitive or proprietary, and the person doing the analysis is not supposed to be able to know everything about it? In a paper to be presented next week at InfoVis, my Ph.D. student Aritra Dasgupta and I describe the issues involved in privacy-preserving visualization, and propose a variation of parallel coordinates that controls the amount of information shown to the user.

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Like cognitive literary studies, digital humanities must draw on other disciplines, using methods and tools that many humanities scholars aren’t comfortable with. And digital humanities has witnessed similar debates about the extent to which we must immerse ourselves in these other disciplines. Do we, as Stephen Ramsay suggests, have to know how to code, and build things? Do we have to be trained statisticians so that the our text-mining results are “statistically significant? Are we more or less rigorous than the proponents of culturomics, whose work many humanities scholars seem skeptical about? These are questions about method, and interdisciplinarity, and collaboration. And they’re not particularly new questions.