LMU Munich, Institute for Theater Studies, 4.- 6. October 2023,
organized by Ulf Otto (LMU Munich), Nora Probst (Univ. Cologne)

Live or not, the theater of today is shared, negotiated and remembered within digital cultures, inevitably being turned into data and subjected to computation.

Accordingly, theater research is confronted with digitalization not only as a topic and a framework of contemporary performances, but also as an epistemological game changer, potentially decentering canonical narratives, or on the contrary reinforcing older academic biases with newly industrial ones. Increasingly digital tools and computational approaches, already established in related disciplines, become employed by theater scholars, necessitating both a methodological exploration and a critical reflection of the changing epistemic technologies and related research practices. How to make sense of performance data?

Following up on a virtual workshop series we organized in 2022, we will address these issues by initiating a new hybrid series of workshops that will foster international and interdisciplinary exchange. We will bring together a small group of theater scholars with colleagues from neighboring disciplines like Film Studies and Art History on the one hand, and Computer Science and Machine Learning on the other. With this we are pursuing a trifold aim: to connect questions, approaches, and results of individual research projects, to collaboratively explore tools and practices hands-on and to explore possibilities for ensuing cooperation. For that aim, each workshop will focus on one specific data type and compile a corresponding data set, that will be shared among participants six months in advance for preparatory exploration and as a hands-on reference point during the workshop.

CTS#1 will open the series with a focus on performance recordings and explore how to make sense of video data from a Theater Studies point of view. Its focus will lie on practices of annotation, the defaults of available tools, and the potential use of computer vision algorithms. In particular, three topics will be addressed:

• Manual annotation as a research practice: what features, what segmentations, what vocabularies do different research agendas employ?
• Evaluating and comparing annotations: what standards and formats can be used to share, merge and further analyze results?
• Publishing and referencing videos: what forms of publications, data models, research infrastructures are needed to reference performance snippets?