From Historiography to Networks: A Digital Approach to Colonial Mexican Painters (1680-1730)

This thesis proposes the construction of a relational database to study colonial Mexican painting, focusing on the networks of painters active between 1680 and 1730. While traditional art historical research has primarily relied on narrative and biographical approaches, this study applies digital methodologies to structure, analyze, and visualize historical data in new ways. To build this database, I processed 15 different datasets, integrating information on nearly 3,000 individuals, 780 artifacts, and numerous historical details such as life events, locations, occupations, and relationships. These datasets were derived from archival sources, academic monographs, and structured institutional records, requiring extensive digitization, OCR processing, text cleaning, and data normalization. Tools such as Recogito, OpenRefine, and Gephi were employed to extract, structure, and visualize relationships within the datasets. Over the course of this research, methodological decisions evolved, particularly regarding the selection of sources and the refinement of the processes needed to convert unstructured text into structured data, digitization, and reconciliation techniques. This study highlights both the possibilities and challenges of database-driven historical research. It demonstrates how structured data can reveal previously overlooked artistic networks, providing new insights into regional artistic production and workshop structures outside of Mexico City. However, it also reflects on the limitations of historical data, the role of archival biases in dataset construction, and the interpretative nature of database design in historical inquiry. Ultimately, this thesis contributes to digital art history and colonial Hispanic American studies by demonstrating how database methodologies can enhance our understanding of artistic production in the early modern world. It also underscores the need for interdisciplinary approaches and a new set of research skills that help bridge historical research, digital humanities, and data-driven analysis to produce more nuanced interpretations of the past.