Humanists do not use data the same ways as other disciplines. We do not simply pull datasets and find that they can easily be interpreted in useful ways. Most of the time data must be mined from available sources and modeled in a way that is helps to answer a research question. Any data could be mined, but without a careful, methodical data modeling, it would be difficult to evaluate the gaps in the data as well as the nuances that historians and other humanists are trained to address and interpretation through their expertise. The upside to the work that goes into these projects, though, is that mining and structuring data allow us to study sources in new ways.

Creating data for humanities projects requires a lot of interpretation of sources. Because the formatting of historical sources, for instance, is not standard, it requires interpretation. In evaluating interviews conducted and collected of former slaves in the 1930s as part of a project done by the Works Progress Administration, I noticed how varied the length, format, and content was between the interviews. There is no reliable way to automate the aggregation of meaningful data across these interviews as a group. Instead, I followed a checklist suggested by Johanna Drucker to create a data model, which includes things like identifying information that can be described unambiguously, standardizing formats, and checking for assumptions and bias before utilizing the data (2021).

Working with the data is an important part of the research project in digital humanities. Transparency about the process of data handling is a crucial part of the overall project. Why was the scope pared down to what it was? What data was ultimately eliminated and why. What decisions were made about standardization of the data and what does that mean for the outcome? One project that I evaluated this week was the Early African American Film Project, which involved collecting data about early race films. One of the things I appreciated about their project was the transparency about some of the decisions the researchers had to make and the inclusion of data they ultimately cut. They did this so that other researchers could evaluate their decisions because the aggregation of data and the data modeling is part of the research process that others need to understand in order to evaluate the work (see Hoekstra and Koolen, 2019).

When humanities source data is put in a structured format, new methods of study are possible. Another project I evaluated this week was Tudor Networks of Power. Researchers used computers to an assisted cleaning of data, to link data, and to create visualizations of the information cleaned from the datasets, which illuminated patterns in the ways letters were used in a political network in a way that could never be understand by a researcher simply reading all the letters over time and trying to see the patterns on their own. The data was carefully mapped and cleaned, and the researchers were very detailed in their descriptions of their processes.

We have moved way beyond digitizing sources and thinking that is the extent of the usefulness of technological applications for the humanities. There is so much more that can be evaluated by mining and modeling historical data that does not appear in a surface level reading alone.

References

Drucker, Johanna. “Data Modeling and Use.” In The Digital Humanities Coursebook: An Introduction to Digital Methods for Research and Scholarship, 1st ed. Taylor and Francis, 2021.

Hoekstra, Rick, and Marijn Koolen. “Data scopes for digital history research.” Historical Methods: A Journal of Quantitative and Interdisciplinary History 52, 2 (2019): 79-94.