Make Room: An Examination of Private Roman Collections of the Early 18th Century
Tori Schmitt & Haley Di Pressi
The Getty Provenance Index contains archival inventory records from several different countries and time periods. In exploring the archive, we became most interested in how this data could be used to explore questions of time and space. Quite often in the humanities, large trend statements are made regarding a specific time period or movement. These statements are supported in general survey: from these paintings, we can deduce that this and that was popular. The case study is paramount. Traditionally, these narratives are not often supported by large data. The Getty Provenance Index provides the opportunity to explore how data supports and contradicts general understanding of art historical narrative. The large amount of data contained in the Provenance Index can be aggregated and then used to explore historical claims.
Specifically for this project, we chose to narrow in on archival inventories from Rome 1700-1750. This search produced a considerable return of just under 11,000 records. We used this dataset as a platform for exploring the composition, movement, and location of Baroque Roman collections. Due to time constraints and limitations of the project, smaller portions of data were used to create case studies of individual collections. The visualizations produced are not definitive, but seek to suggest what can be done with this type of data in an art historical setting.
What did the average collection look like?
Due to the large volume of data, we decided to use Wordles, or text clouds, to gain a broad understanding of the dataset. Wordle is a free web application that creates text clouds based on uploaded spreadsheets. In creating these text clouds we were able to immediately see which values and terms are most prevalent in the spreadsheet. In a way, we almost used it as a data cleaning tool to visually see portions of our dataset rather than run queries through software. As art historians that have not specialized in the Italian Baroque, this exercise helped to familiarize us with the terminology of the period. Furthermore, it helped familiarize us with the vocabulary used within the document. For the purpose of this project, these are the only visualizations that we made that contain all of the dataset. Because of this, we can consider these visualizations to be the most representative of the 50 year expanse, but also the most general.
Some data proved inconclusive in Wordle format. While it is clear in the above visualization that December is the most prominent recorded date, it is not immediately apparent what this means historically. In our research, we could not find a correlation between Baroque sales practices and time of year. There was no explanation as to what the provided dates signified, and it was apparent that they were not the dates of the creation; however we hypothesize that this is the date the collection was archived since most collections have only one date associated with them. This shows that sometimes, visualizing data can present the researcher with additional questions, and often, these questions can be very specialized.
Iconclasses: What iconography was most represented?
Iconclasses are a branching method of classifying iconography in artworks. They are broken into 10 overarching categories denoted by numbers 0-9, and from there are further subdivided with the addition of more alphanumeric combinations. According to the visualization below, one of the most commonly occurring iconclasses is 11H. In the iconclass system the first 1 denotes “Religion and Magic,” 11 then denotes “Christian Religion,” and 11H signifies “Saints”. The other iconclass that repeatedly showed up in the text clouds was 25H1, which branches as Nature > Earth, World as Celestial Body > Landscapes > Landscapes in the Temperate Zone.
As the two most common iconclasses suggest, much of the artwork found in collections from 1700-1750 in Rome was religious or nature-based. Even when artworks strayed from these two classifications into mythology or portraiture, there were very few cases where the iconclass of a work indicated something that would go against the prevailing Catholic sentiment of Italian culture at the time.
In this time period, what was the origin of Roman-owned art?
Where were paintings located in the Roman home?
The room category was by far the most interesting category in our metadata set. Although the archival inventory was created after the paintings had been removed from their home collection, the individuals who created the archival inventory documented which room the painting had hung in. This is interesting in and of itself, but even more so when considering the history of display in Renaissance Italy. In between 1619 and 1621, Giulio Mancini first published his treatise on art display, Considerazioni sulla pittura (Gage, 68). As both a doctor and an art dealer, Mancini was influential in his community. His treatise of display is both general and specific, outlining both the grand purpose of collecting and the nuanced details of collecting etiquette. He even provides a guide for which types of paintings belong in each room. His recommendations stem off of the general principle that “while profane paintings would adorn the sale (meeting rooms), the devotional images were to hang in the camere (bedrooms)” (Gage, 69).
Although Mancini wrote his treatise roughly 100 years before our dataset, we thought that it would be interesting to see if there was any correlation between our dataset and Mancini’s recommendations. Due to the large size of our dataset, this task required a lot of parsing. We ultimately decided to test our theory by visualizing the names of rooms in a specific collection. As this data was recorded at different times and presumably by different individuals, it was essentially impossible to establish a unified vocabulary. Looking at vocabularies internal to a single collection seemed a more reasonable scope.
We chose to visualize the Capponi collection. As can be seen in the visualizations above, the four rooms with the most paintings were the Stanza del Gabbinetto, Stanza dove sta il letto giallo, Stanza contigua alla sudetta stanza, and Stanza delle Madonne. According to a modern Italian dictionary, gabbinetto refers to a washroom or cabinet room. We interpreted this space as perhaps being a storage room within the Capponi home. This would further explain the large number of paintings (83) in the room. The Stanza dove sta il letto giallo roughly translates to “the room where the yellow bed is.” We interpreted this space as being used as a bedroom. Stanza contigua alla sudetta stanza can be translated as a “continuous room” or hallway. As the name implies, the Stanza delle Madonne means “the room of the Madonna.” These visualizations show the diversity in painting distribution in the Capponi collection. From the data set, it appears that most rooms in the home had artworks and many artworks at that.
To further narrow in on the data, we chose to analyze the painting iconoclasses in the Stanza delle Madonne. We chose this room because the name of room lent itself to an obvious prediction. We predicted that the paintings in the Stanza delle Madonne would all refer in someway or another to the image of the Madonna. We also guessed that perhaps this room was named this way because it contained so many images of the Madonna. Unfortunately, the room description name did not give us insight into what the room was used for. To analyze the contents of the room, we visualized the iconclasses of the 22 paintings contained in the room. As seen in the graph above, the most prevalent is 114F. The iconclass 114F is defined as representing “Madonna, Mary with Christ-child” (Iconclass). The second most prevalent iconclass is 73B13. This iconclass is defined as representing “Mary, Joseph, and the newborn Christ (Nativity scene)” (Iconclass). The third most prevalent iconclass is 73B81. This iconclass is defined as representing “the Holy Family” (Iconclass). All three of these iconclasses fit well with the predicted theme of the room. Save for one painting, all the paintings in the Stanza delle Madonne were religious in scope.
Case Study: Can Gender of Collection Owner Be Visualized?
Within one of the Barberini family collections we were able to find data on the artworks located in the bedrooms and audience chambers of the Principe (Prince) and Principessa (Princess) Barberini. With this data in hand, we fed the iconclasses and artists through Wordle to see if there were any surprising results within each private collection. As expected, the visual majority from the text clouds revealed that the most common iconclasses were those associated with religious themes, and within that category the majority were of scenes from the New Testament. This matches the assessment of individual collections by Giulio Mancini discussed above; one expects to see devotional paintings in the bedrooms and that is exactly what is there. While Mancini says profane paintings would adorn meeting rooms, it seems that the Barberini couple kept their audience chambers decorated with devotional imagery as well.
In addition to the iconclass text clouds, we created Wordles of the artists within each private collection. This visualization had the potential to tell us quite a bit about the collecting preferences, but instead what we ended up visualizing was the fact that many of the artists were not recorded in the archival inventory, hence the large “Anonymous” dominating the cloud. However, this particular visualization was somewhat helpful in that it confirmed our suspicion that the vast majority of their collection would be comprised of Italian artists, and beyond that we can visually see that Reni, Caravaggio, and Carracci were all relatively popular artists within their collections.
Our preliminary findings did not produce concrete conclusions regarding collecting practice. They did however, suggest many future avenues of research. Through this project, we learned a lot about data cleaning and standardization. The most difficult obstacle with our dataset was the standardization of terminology. The wide range of terminology used to describe rooms, artists, and artworks made it difficult to categorize and visualize these categories. If allotted more time in the future, it would perhaps be helpful to re-categorize some of the data. This practice would refer to creating secondary categories such as “room type.” This way rooms with various titles which refer to a bedroom could be visualized all together under the title of bedroom. This would allow for researchers to further investigate the application of Mancini’s theory, as well as other aspects of collecting practice.
Iconclass. “Iconclass-11F4.” Accessed June 9th, 2015. http://www.iconclass.org/rkd/11F/.
Iconclass. “Iconclass-73B13.” Accessed June 9th, 2015. http://www.iconclass.org/rkd/73B13/
Iconclass. “Iconclass-73B81.” Accessed June 9th, 2015. http://www.iconclass.org/rkd/73B81/