Where the data exhibit significant informational paucity, indeterminate values, inordinate biasing, or limited scope it is common to cast them aside in pursuit of something held to be more representative. Alternatively, a move is made to systematically qualify data absence as a means of shoring up grounds for a redefined notion of representativeness to stand upon. Both responses generally fail to engage with data absence as a feature rather than a bug to be quashed. How might data driven scholarship be conducted in a manner that centers data absence?
Padilla turned to Twitter to ask fellow practitioners if they had encountered visualized absence in data, and the remainder of his post details the readings and projects that his respondents pointed him towards. The comments on his post direct readers towards additional resources for investigating absence, and Padilla closes with a call to to treat “data absence as an integral feature rather than a bug to be quashed.”