Once upon a time, I crossed Digital Art History

by Sofia Baroncini

Once upon a time…

My story starts with curiosity. During my master’s thesis in Visual Arts at the University of Bologna, I could choose some courses from a long list. Among them, I noticed one course entitled “Digital Humanities”, taught by Professor Francesca Tomasi. Its program included an introduction to the field, showing a clear focus on practical applications, as many markup languages, such as HTML and XML, were listed. Taken by curiosity, I included it in my study program. I would say the impact, in the beginning, was strong: I saw lines of markup without really understanding the differences and motivation lying beyond the descriptors and their role. However, I didn’t give up and I spent much time understanding the differences, and why markup, controlled vocabularies, and structured data were important.

As we better understand what we love, I understood them when I tried to apply them to my favorite art-historical discipline, iconography and iconology. I started exploring controlled vocabularies for the artworks’ subject matter classification trying to comprehend their differences, what was possible to describe with them, and what was omitted. I tried to answer these questions shedding light on the complexity embedded in iconological studies that controlled vocabularies couldn’t grasp.

The Italian writer Italo Calvino, on whom I focused in my BA thesis, was fascinated by the eternal contrast between the infinite world and the finite way in which we perceive it. To learn and understand, humans divide reality into finite boxes. Among his numerous essays, he dedicated one to computers, too (Cibernetica e fantasmi). He made the “tecnica combinatoria” a core aspect of his ars poetica, creating narratives in which given pieces generated a variety of stories.

When I understood that I was attempting to tame the infinite world of iconological understanding with finite cells, I was triggered by the eternal battle between the flame and the crystal, between unstructured, insightful textual descriptions and structured data. This challenge, intrinsic to every act of cataloging and data modeling, fascinated me. 

This is the story of how the exam project became a master’s thesis, and the master’s thesis turned into a PhD project. Now, this postdoc project at IEG is born out of this path. 

Iconography and iconology: the focus on Panofsky

My PhD project, entitled Diving into artworks interpretations through the lenses of semantic data: an application on Panofsky’s iconological studies (University of Bologna, 2020-2024), applies semantic web technology to the branch of study of iconography and iconology. It focused on the modeling, data creation, and analysis of the art historical interpretations by Erwin Panofsky, one of the scholars following the iconological approach initiated by Aby Warburg (1866-1929). 

Panofsky was chosen as a case study as he is the art historian attempting to provide the most extensive theoretical framework of the iconological approach. He states that the interpretation act occurs on three levels of interpretation, from a more superficial to a deeper one; the level reached by the observer depends on his knowledge of the artistic conventions used by artists, the literary subjects they knew, and the culture of the time. 

The ontology and the dataset created reflect this three-layered structure. The ICON ontology (Sartini et. al., 2023), which I developed in collaboration with my former colleague and friend Bruno Sartini, allows for the description of objects recognized at each level of interpretation, indicating relations among them, the possible sources cited as evidence, and the provenance of the assertion.

The Iconology dataset represents the interpretations by Panofsky that were expressed in three of his major books, plus one article. Reflecting the interests of the art historian, the resulting dataset mainly concerns the reception of classical antiquity in medieval and Renaissance Western art, as shown in the picture above.  The analysis, presented on an online dashboard, addresses some traditional research questions quantitatively, over different aspects, to provide a proof of concept of what kind of questions it is possible to address when having such curated data available. For example, it was possible to retrieve all the attributes of iconographies having a symbolical meaning, all the variations of the iconographical representations, how artworks are related in networks connecting them through cultural phenomena, and more. 

My wish is that this study encourages the development of data and quantitative data analysis regarding the study of iconographies and their meanings, in addition to being the starting point for the development of my postdoctoral project. 

How do we deal with the context?

Although the importance of the study of the context in which artworks were created is well established in the traditional, qualitative art historical practice (e.g., see Barrett, 2003), it is barely stated in datasets describing works of art. To give an example, only 0,04% of works of art described on ArCo, the Italian Cultural Heritage knowledge graph, provides information about the location at which the artwork was executed. With “context”, I refer to further information about the socio-cultural reality in which the artist lived, including beliefs and religious practices, both of the society in which the artwork was created and of the artist himself. A unique definition of “context” is challenging in itself, as it can grasp several nuances of the contemporary socio-cultural environment in which the artist lived.

In the fields of Digital Humanities and Digital Art History, there is an increasing awareness that there is a need for contextual data to analyze artworks computationally. Several notable projects are creating and publishing data about contextual information related to the creation of various types of cultural heritage objects. The Golden Agents project, for example, collects several datasets about the cultural industries of the Dutch Golden Age. This data made it possible, for example, to analyze the interactions between authors, printers and illustrator related to the flourishing period of production of illustrated books from the second half of the 17th century to the early 18th century (see the analysis here), or to trace the evolution of iconographies through the aid of artists’ biographies (Li, 2021). 

The postdoctoral project 

The postdoctoral project I am starting in this field at the IEG is as fascinating as challenging. For the complexity that needs to be addressed, this first year will be dedicated to answering crucial questions and verifying whether data helping to answer these questions is available, of what type it is, and whether it can be reused. 

The first part of the study of the state of the art is dedicated to verifying what type of iconographical-iconological questions can be addressed with the currently available data, with a focus on Italian Early Modern art, according to my expertise. An increasing number of datasets is using controlled vocabularies, such as ICONCLASS and Getty Vocabularies, to describe resources, offering standard subject access to an increasing number of artworks (a complete list of resources using ICONCLASS). For example, through the ICONCLASS code, comparing the number of occurrences of classical subjects and Christian ones represented in a dataset is already possible. It would be less feasible to study deeper iconological meanings, as, for their highly interpretative nature, they tend to be scarcely represented in cultural heritage institutional datasets.  

A characteristic of iconology is that the deeper insights attained by scholars are narrowly bound to the art historian proposing the interpretation, as no archaeological evidence can definitely prove that one interpretation is true. Furthermore, art historians expressed iconological interpretations only for a limited number of subjects, often basing their interpretation on specific sources. For these reasons, the core of my project will be examining the scholarly literature and extracting artworks that were objects of an iconological interpretation, and the meaning associated with them. By examining their features and defining the context of their provenance, it may be possible to identify the characteristics of artworks influenced by the cultural phenomena recognized by the scholars and infer the interpretation for similar artworks.

Iconography and iconology analyze artworks also from a diachronic perspective, i.e., seeking the variation of iconographies according to form and meaning over time and in different places. Therefore, multiple contexts with different time and place coordinates should be considered. In my project, I hypothesize that the available literature will highlight the contextual features that are relevant to the study of a certain period of art. 

A more extensive study of the iconological domain than the PhD thesis will then allow for an extension of the ICON ontology aiming at modeling in a detailed way the cultural phenomena that may be related to artworks and have an impact on their interpretation. 

Last but not least, tests to verify whether it is possible to (semi)automatically extract artworks from iconological scholarly literature are in progress. An automated method would allow the identification of the cluster of artworks iconologically interpreted and the extension of the research towards the goal of having enough data available to gain representative and valuable insights for the field.

References

Barrett, T. (2003). Interpreting art: Reflecting, wondering, and responding. McGraw-Hill.

Li, W. (2021). A Network of Iconography: Tracing the Evolution of Iconography in History Paintings in the Dutch Golden Age. Early Modern Low Countries, 5(2), Article 2. https://doi.org/10.51750/emlc11334

Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., & Gangemi, A. (2023). ICON: An Ontology for Comprehensive Artistic Interpretations. Journal on Computing and Cultural Heritage, 16(3), 1–38. https://doi.org/10.1145/3594724


Featured image: Sofia Baroncini: Frequency of second-level subjects in the Iconology dataset, CC-BY-SA 4.0, source: https://iconology-dataset.streamlit.app/



Diesen Blogbeitrag zitieren
Sofia Baroncini (2024, 24. Mai). Once upon a time, I crossed Digital Art History. Digital Humanities Lab. Abgerufen am 16. Juni 2024, von https://doi.org/10.58079/11pt5

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