Felipe Álvarez de Toledo is a Ph.D. Candidate in Duke University’s Art, Art History & Visual Studies Department. He received a bachelor’s degree in Economics from Pompeu Fabra University in Barcelona, Spain, in 2015. Felipe studies historical art markets from a data-driven lens, combining art history and economics. His interests include art markets, the transcontinental trade in paintings in the Early Modern Period, Early Modern Spain and Latin America, quantitative approaches to art history and the digital humanities.
When and why did you get involved in DH?
I came into the Ph.D. with an economics background but no idea about how that could be applied to Art History. My advisor, Prof. Hans J. Van Miegroet, was big on data and quantification, which spurred an interest in databases. Starting first very simply, using excel spreadsheets, I learned more about database management and data visualization from other people at Duke University.
Eventually, the contents of my own datasets also spurred an interest in mapping and GIS, first in a course titled Urban Economics, taught by Professor Charlie Becker of the economics department, and then specifically in the AAHVS department’s Historical GIS course taught by Prof. Ed Triplett. Afterwards, I got a graduate internship at the Duke Center for Data and Visualization Sciences which gave me the space and time to learn the programming language Python, and then a teaching assistantship at the Wired! Lab where I continued to pursue that interest and created a learning resource for Humanists trying to learn Python. I guess it was a slippery slope.
Bernardo Lorente Germán (detail), El Tabaco, 1730-1740, (source: academiacolecciones.com)
How will you be featuring DH in your dissertation? How does DH contribute to your art historical research?
My dissertation examines the market for paintings in early modern Seville, Spain from a quantitative perspective. Spain has been under-researched, even though it was one of the major powers throughout the sixteenth and seventeenth centuries and the center of an extensive empire that included oft-studied Flanders. In particular, between 1503 and 1717, Seville, Spain was the administrative center of a trade system that connected the societies of Europe and the Americas, in which thousands of paintings numbered among the objects traded. I aggregate archival information on painters and paintings in Seville, Spain and treat it as quantitative data to supplement and examine existing narratives about Sevillian art and the development of art markets in the early modern period.
Methodologically, my research harnesses digital tools, including relational database management systems, OCR Scanning, and Natural Language Processing, to centralize a wealth of information distributed throughout the city’s archives, some published and some original. With the resulting font of aggregable data on the production, sale, and consumption of artistic goods in early modern Seville, it delivers a quantifiable case study of an early modern industry which had an impact on both sides of the Atlantic. This involves statistical analysis as well as visualization through maps and graphs. Beyond simply employing statistics and digital tools for visualization, I think DH has provided the framework for thinking about the limits and biases, not only of other art historical research, but more importantly of my own approach and sources.
The output of a machine-learning process that automatically tagged and identified that information as person, location, monetary amounts, and dates
Greatest challenges and learning curves you have had to overcome?
There were many, but they always provoked a counter-reaction: determination and excitement. I’ve been a bit stubborn when it comes to learning digital tools. Learning GIS and Python were definitely an important time commitment (one semester each) which I overcame because the institutional spaces were available for me to dedicate that time and effort to them.
I am finishing my Ph.D. with an amazing dataset on the art market of Seville. I would like to improve and expand on it to make it available to other scholars, as well as continue my own analysis of it. I also want to share the methods I employed just in case they are helpful to others.
Most useful tips to get started
First, I think it is important to find a way to incorporate it into the degree structure so that you have the time and resources to pursue it. I have done this through internships, TAships and independent studies. At Duke AAHVS, there are many engaged faculty that are motivated to help us pursue these goals. I learned best through supervised independence: using books and online resources recommended by others who knew and who accompanied me throughout the learning process.
Second, learn with a purpose: the hard work pays off if it serves your research goals and interests. It becomes self-driven as opposed to a chore. Use your own topic to learn: the biggest motivator was seeing my own data and analyses begin to take shape as I learned the software.
Unknown Artist, View of Seville, c. 1660, Oil on Canvas, 163×274 cm. Sevilla, Fundación Focus (Hospital de los Venerables). (source: Wikipedia.com)
Who would you like to meet in the next post of The Humans Behind Digital Humanities?
You are more than welcome to send us suggestions and nominations.