LAWHA at DHASS21: Missing links – or the unsuccessful search for art from Lebanon
Monique Bellan took part in the Digital Art History Summer School 2021 / DAHSS21 from 30 August to 4 September 2021, organized by the University of Málaga and the University of California, Berkeley.
For this year’s online edition of the summer school, 45 participants from all over the world came together to learn about the various aspects of digital exhibitions and to discuss theoretical approaches and challenges of digital art history. The summer school was structured in five tracks, each of which focused on a specific field of digital art/history briefly outlined here:
- Track A focused on configuring digital spaces for exhibitions on virtual platforms and techniques such as basic modelling, animation, and data visualization;
- Track B on data science was concerned with the creation, analysis, and visualization of linked open data;
- Track C on 3D data, modelling, and rendering experimented with augmented and virtual reality tools for interactive exhibitions;
- Track D investigated AI and computer vision applications to problems in art history and visual culture;
- Track E explored natural language processing (NLP) to study what textual data can tell us about art on a large scale.
In the daily plenary sessions, the participants informed each other about their respective group work and planned the final presentation; alumni talks framed the daily working sessions and gave additional insight into various art-historical projects and their particular challenges.
I took part in track B on cultural data science to learn more about data analysis and visualization since these are relevant aspects for LAWHA. The course consisted of readings and theoretical discussions as well as hands-on training with tools for data modelling and visualization.
What exactly is digital art history and what are its aims? Digital art history is the process of creating knowledge with new methods. It aims not to replicate existing knowledge but to generate new knowledge with the help of digital tools. Therefore, it is data-driven art history. Data science uses statistics, data analysis, and machine learning to understand and analyse specific phenomena and relations. The question is, therefore: How can the computer assist in doing better research? To achieve this goal, information technologists and humanists need to establish a work routine that builds on continuous exchange to find solutions to the questions that researchers are concerned with and understand how technology can contribute to this. However, there is an important distinction to make: digitized art history is different from digital art history. The former means working with digitized material and analogue methods; the latter implies gathering and visualizing data, i.e. in knowledge graphs. In doing so, new knowledge can be generated, which would have been difficult to access with analogue methods. This is especially relevant when dealing with a huge amount of data, which is challenging to structure and analyse. Knowledge graphs are semantic networks that represent objects, events, people, concepts, etc. They visualize the relationships between these entities and can be helpful tools, especially when dealing with large datasets.
Some of the tools track B experimented with were Nodegoat, Palladio, and Gephi. The group members either used their own data or retrieved some from Wikidata to visualize them with these tools.
I attempted to retrieve data on artists in and from Lebanon. However, the Wikidata query delivered only very scarce information and was not a straightforward process. The outcome of my query is visualised in the graph below. Since the amount of data was too little, any attempts to visualise the connections with the mentioned tools were rather unfruitful. This slide was my contribution to our final presentation organized as a 3D exhibition. In this room, you can find the outcome of track B. For more pictures follow this link.