The course has a theoretical-practical orientation: theoretical exchange and critical discussions will be combined with practical sessions (lab-based sessions) through which participants will work collaboratively. The results will be publicly presented on the last day of the course.
August 30th – September, 4th, 2021
The course is organized around FIVE TRACKS
Track A: Digital Display Spaces
Track A, Greg Niemeyer (UC Berkeley) Niemeyer will work with participants in configuring digital spaces for exhibitions on virtual platforms such as newart.city and modzilla hub. Techniques include basic modelling and animation, .fbx or .glb file format, spatial strategies for virtual engagement, data visualization and local sound synchronization in virtual spaces. Track A participants will create content and curate content produced in the other Tracks to cumulate in an online virtual exhibit about DAHSS 2021.
Track B: Data Science
In this track, led by Harald Klinke (DAHJ), you will learn how to create, analyse and visualise linked open data. We will identify preconditions, gaps and biases in collection data and discuss the transformative effects of historical knowledge generated by digital methods on society. No prior knowledge required
Track C 3D Data, Modeling, and Rendering
Track C lead by Justin Underhill (UCB). We will learn to create 3D models for art-historical purposes and will experiment with Augmented and Virtual Reality tools for creating interactive exhibitions.
Track D: AI + Computer Vision
Track D, led by Leonardo Impett (Durham University), will investigate applications of AI/deep learning – especially computer vision – to problems in art history and visual culture. We will look at the long history of the computer analysis of images from the late 80s to today. Through the low-code visual programming environment www.imagegraph.cc, developed specifically for DAHSS, we’ll learn the basics of computer vision and deep learning in Python, including multimodal text-image models. We’ll also talk about how to visualise and interpret big image data in the context of Cultural Analytics, Distant Reading, and contemporary curating. If you have digital image datasets from your own work or research, please bring them along (and don’t worry if not).
Track E: Natural Language Processing (NLP)
Track E, led by Yadira Lizama Mué (CulturePlex Lab, Western Ontario University) will explore the power of NLP to study what textual data can tell us about art on a large scale. NLP is a field of Artificial Intelligence that centers around measuring human language to make it intelligible to machines. It combines the power of linguistics and computer science to contemplate the guidelines and structure of language and make intelligent systems fit for comprehension, breaking down, and separating significance from text and speech. We’ll learn a wide range of NLP topics, such as regular expressions, word tokenization, named-entity recognition, topics extraction, sentiment analysis, and text classification. We’ll also gain practical experience in the use of tools such as Spacy, alongside libraries that utilize deep learning to solve common NLP problems. We will have the opportunity to explore collections of texts related to art included in H.W.Wilson’s Art Full Text database, Project Muse, Wikipedia, and hundreds of media articles related to art exhibitions.
No matter what track you pick, you will also see what students do in other tracks in our daily plenary session. In the plenary sessions, notable alumni of the DAHSS program will also share feedback and observations about how DAHSS helped them in their work.
To accommodate the most possible time zones, the plenary sessions will be conducted daily at 18:00 CEST. Track sessions will be at 16:00 CEST. However, other options could be considered according to the time zones of participants in each track.