Method in Interdisciplinary Research

Data Science for Digital Art History

  • Ewa Machotka (Author)
    Stockholm University
  • Panagiotis Papapetrou (Author)
    Stockholm University

Identifiers (Article)


This paper creates a conceptual frame and explanatory point of reference for the collection of papers presented at the exploratory workshop “Data Science for Digital Art History: Tackling Big Data Challenges, Algorithms, and Systems” organized at the KDD 2018 Conference in Data Mining and Knowledge Discovery held in London in August 2018. The goal of the workshop was to probe the field and to build a constructive interdisciplinary dialogue between two research areas: Data Science and Art History. The workshop’s chairs and the authors of this paper share the conviction that Data Science can enrich art studies while analysis of visual data can have a positive impact on Data Science. Thus, the research initiative tried to critically reflect on the interdisciplinary collaboration between diverse research communities and its epistemological and ontological effects.




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interdisciplinary collaboration, DAH community, methodology, data science, diversity, globalization, feature extraction, machine learning, deep learning
How to Cite
Machotka , Ewa, and Panagiotis Papapetrou. 2019. “Method in Interdisciplinary Research: Data Science for Digital Art History”. International Journal for Digital Art History, no. 4 (December):5.03-5.11.