This lecture proposes that historians create personal libraries tailored to their projects rather than engage in macro-level “distant reading” of a centralized repository. This methodological intervention prioritizes contextualization and authentication in data-driven historical research, which technologically translates into robust management, connecting, and querying of records culled from a variety of pertinent databases. After experimenting with the standard set of general-purpose "macroscopes" comprised of SQL, Gephi, and Cytoscape, Javier Cha present a new workflow that utilizes iterative Python code to extract data subsets from universal databases and imports them into the graph database management software Neo4j. Javier Cha discuss why he makes certain choices and explains how digital historians can use a macroscope powered by Neo4j to zero in on potentially insightful fields of view.