“Those of us who are interested in seeing more robust cultural critique need to be more specific about where the intervention might most productively take place. It’s not only about shifting the focus of projects so that they feature marginalized communities more prominently; it’s about ripping apart and rebuilding the machinery of the archive and database so that it doesn’t reproduce the logic that got us here in the first place….What would maps and data visualizations look like if they were built to show us categories like race as they have been experienced, not as they have been captured and advanced by businesses and governments? (Posner, 2015)
“What is needed is not a set of applications to display humanities “data” but a new approach that uses humanities principles to constitute capta [constructed knowledge, taken not given] and its display….I am not suggesting that we simply introduce a quantitative analysis of qualitative experience into our data sets. I am suggesting that we rethink the foundation of the way data are conceived as capta by shifting its terms from certainty to ambiguity and find graphical means of expressing interpretative complexity." (Drucker, 2011)
“Understanding how the humanities have traditionally approached big problems can inform how experts in data science can model meaningful conclusions based on the same skillful concern with answering questions based on a serious inquiry. Humanists, after all, are experts at probing the largest questions of our species….The particular skills of humanities scholarship take many forms, but they all agree in emphasizing serious engagement with texts and their contexts.” (Guldi, 2019)
- Big (Kaplan, 2015)
- Small (Klienmann, 2016)
- Micro (Risam & Edwards, 2017)
- Smart (Schöch, 2013)
- Ambiguous
- Gender & race (Posner, 2015; McPherson, 2012)
- Historical geographic information (Plewe 2002)