Methodology

Daggerobelus combines archival research skills and textual close reading with data science workflows to create its primary methodology.

We apply techniques like

• NLP (Natural Language Parsing) of primary documents from historical archives into machine-readable formats like JSON
• Data analysis using python and pandas to perform techniques like network analyis, statistical modeling, and other advanced techniques
• Charting using D3 and modern web-based visualizations to create readable figures that organize dense historical data

A large part of this methodology includes ensuring that the workflow can be extrapolated to new archives by creating a clear flow from 1) the original archive to 2)machine-readable formats and 3) visual outputs in the form of figures. Since each archive presents its own idiosyncracies, this method can transfer and adapt to a new historical corpus depending on the schema we identify as salient. This method works best with large—and potentially unweildy—archives.