The Pages of Early Soviet Performance (PESP) aims to create a dataset of early-Soviet illustrated periodicals related to the performing arts. We are using machine learning techniques to better understand this rich cultural material and to facilitate new avenues of research about Soviet culture during the first decades after the October Revolution (1917-1932).
Princeton University has digitized ten titles - totaling 526 issues and approximately 26,000 pages - of Soviet performance journals, which can be freely viewed online in Princeton University Library's Digital PUL (DPUL). Journals are a diverse and complex genre: taken together, this collection contains hundreds of thousands of articles, poems, editorial commentary, advertisements as well as images, illustrations and graphic art. Today, researchers can browse the journals and view and download high-quality page images on DPUL.
Our project asks: what if we could access this collection as data? What patterns -- of words, phrases, or images -- can we discover across the whole collection? Which words or names are most frequent? What type of images, or subjects, appear or disappear at points in a journal’s publication history? How did the role of advertisements evolve over the course of the 1920s? Which plays or concerts were the most frequently performed during this period?
The PESP team is interdisciplinary, multi-institutional and international. It is spearheaded by Princeton’s Kat Reischl (Slavic), Thomas Keenan (PUL), and Natalia Ermolaev (CDH), with assistance from Alexander Jacobson (graduate student, Slavic). Our technical lead is Andrew Janco, Digital Scholarship Librarian at Haverford College. We partner with scholars from the Digital Humanities Research Center at St. Petersburg State University of Information Technology, Mechanics and Optics (ITMO): Antonina Puchkovskaya, Vladislav Tretyak, Anastasia Mamonova and Alexander Kudryashov.
For more on this project, see Princeton’s Slavic DH Working Group website.
CDH Grant History
- 2020–2021 Dataset Curation