Languages of Art Writing is a data curation project focused on the terminology employed in art criticism, artists’ statements, and manifestos published in Western Europe from the late 1940s to the present. It has been developed with the support of a 2021–2023 Data Fellowship from the Center for Digital Humanities (CDH) awarded to co-principal investigators Brigid Doherty and Sara B. Green. Green took the lead in carrying out the project in collaboration with Nathan Stobaugh, who joined as a researcher with additional funding from the CDH.
Languages of Art Writing makes relationships across its corpus legible by documenting references to artists, authors, movements, media, and a wide range of critical terms pertinent to the history of modern art and culture.
The project—whose corpus currently includes texts in French, German, English, and Italian—renders these documents searchable using a lexicon of keywords derived from a range of disciplines. These terms form a local authority, currently comprising over 850 entries, that researchers have cross-referenced with existing controlled vocabularies (the Library of Congress Subject Headings and the Getty Art & Architecture Thesaurus). Languages of Art Writing makes relationships across its corpus legible by documenting references to artists, authors, movements, media, and a wide range of critical terms pertinent to the history of modern art and culture.
The tagging process proceeds by way of a feedback loop that draws its intertextual vocabularies from primary sources and then applies them to further historical documents. Green and Stobaugh have worked collaboratively on multiple scales: attending to the use of individual terms within specific contexts while also determining how to map meaningful connections across texts through shared language.
Charting these connections across its data set at the level of the word, the project approaches art historical texts through an unhabituated mode of reading that makes its researchers’ collaborative textual interpretation available to quantitative analysis.
The development of the data set took as its point of departure the thematic and historical frameworks of an A&A course developed with the support of the 250th Anniversary Fund for Innovation in Undergraduate Education and first offered through the Collaborative Teaching Initiative in Spring 2021, Reckoning with History, Responding to the Present: Art in Europe Since 1960, which was co-taught by Doherty and Stobaugh.
Languages of Art Writing has potential future applications as a pedagogical resource. For example, it would allow undergraduate students to explore research topics through terminology and references derived directly from primary texts.
Languages of Art Writing occasions linguistic associations between artists, movements, writers, and historical contexts not ordinarily considered together.
The methods of close reading and language analysis deployed in Languages of Art Writing allowed Green and Stobaugh to study texts directly tied to their dissertation work, while also engaging a range of documents with a broader historical and discursive scope. Languages of Art Writing occasions linguistic associations between artists, movements, writers, and historical contexts not ordinarily considered together. For instance, the project places texts by Austrian multimedia artist VALIE EXPORT (b. 1940), whose work is the subject of Stobaugh’s dissertation, in dialogue with texts from the art historical context of the post-WWII Paris-based Lettrist movement, the subject of Green’s dissertation.
Green and Stobaugh co-authored an article about Languages of Art Writing in the most recent issue of Thresholds, published by MIT Press. A portion of the article explores possibilities for visualizing the Languages of Art Writing data set on various platforms, including Cytoscape, a program initially developed for modeling protein molecules (see image at left).
Languages of Art Writing offered a fruitful opportunity for collaboration between Art & Archaeology graduate students and with their advisers, made all the more vital given its inception during the period of increased isolation brought on by the COVID-19 pandemic
CDH Data Fellows learn best practices in data selection, structuring, cleaning, transformation, and preservation, with the aim of producing a dataset suitable for computational analysis, open-access publication, and future use in research and in undergraduate or graduate courses. Princeton faculty, staff, postdoctoral fellows, and graduate students are eligible to apply.