Method in Interdisciplinary Research

Data Science for Digital Art History

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

Identifiers (Article)

Abstract

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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References

Bahrani, Zainab and Jas Elsner, Wu Hung, Rosemary Joyce, Jeremy Tanner. 2014. “Questions on ‘World Art History’.” Perspective: Actualite en Histoire de L’Art 2: 181-194.
Barry, Andrew and Georgina Born. 2013. Interdisciplinarity: Reconfigurations of the Social and Natural Sciences. London: Routledge.
Bentkowska-Kafel,Anna. 2015. ”Debating Digital Art History.” The International Journal for Digital Art History 1: 51-64.
Bentkowska-Kafel, Anna and Hugh Denard. eds. 2016. Paradata and Transparency in Virtual Heritage. London, New York: Routledge.
Bishop, Claire. 2017. “Against Digital Art History.” Humanities Futures: n.p.
Carrier, David. 2008. A World Art History and its Objects, Philadelphia: Pennsylvania State University Press.
Drucker, Johanna and Anne Helmreich, Matthew Lincoln et Francesca Rose. 2015. “Digital Art History: The American Scene.” Perspective 2: n.p.
Elkins, James. 2007. Is Art History Global, New York, London: Routledge.
Emmeche, Claus and David Budtz Pedersen, Frederik Stjernfelt. 2018. Mapping Frontier Research in the Humanities. London, New York: Bloomsbury Academic.
Estill, Laura. 2019. “Digital Humanities’ Shakespeare Problem.” Humanities 8 (1): 45.
Freitas, Alex. 2014. Comprehensible Classification Models: A Position Paper. SIGKDD Explor. Newsl. 15 (1): 1-10.
Jaskot, Paul. 2013. “Review of Debates in the Digital Humanities.” Visual Resources 29 (1-2): 135-140.
Jasonoff, Sheila. 2016. The Ethics of Invention Technology and the Human Future. New York: W.W. Norton & Company.
Jõekalda, Kristina. 2013. “What Has Become of the New Art History?” Journal of Art Historiography 9: n.p.
Langfeld, Gregor. 2018. “The Canon in Art History: Concepts and Approaches.” Journal of Art Historiography 19: n.p.
Langmead, Alison and Thomas Lombardi, David Newbury, Christopher Nygren. 2018. “A Role-Based Model for Successful Collaboration in Digital Art History.” Interna¬tional Journal for Digital Art History 3: 152-180.
Manovich, Lev. 2015. “Data Science and Digital Art History”, Journal of Digital Art History 1: 13-35.
Moretti, Franco. 2000. “The Slaughterhouse of Literature.” Modern Language Quarterly 61 (1): 207-227.
Nguyen, Khuong and Zhiyoun Luo. 2012. “Conformal Predic¬tion for Indoor Localisation with Fingerprinting Method.” Artifi¬cial Intelligence Applications and Innovations 3: 214–223.
Provost, Foster and Tom Fawcett. 2013. Data Science for Business, Sebastopol: O’Reilly.
Ribeiro, Marco T .and Sameer Singh, Carlos Guestrin. 2016. “Why Should I Trust You? Explaining the Predictions of Any Classifier.” KDD 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 1135-1144.
Toressen, Jim. 2018. ”A Review of Future and Ethical Perspectives of Robotics and Aim”, Frontiers in Robotics and AI 4: 75.
Tyler, Lamar. 2014. The Gatekeepers are Gone: Hustle + Technology = Success. Atlanta: Tyler New Media.
Zijlmans, Kitty and Wilifred Van Damme. 2008. World Art Studies: Exploring Concepts and Approaches, Amsterdam: Valiz.
Published
2019-12-16
Language
English
Keywords
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. https://doi.org/10.11588/dah.2019.4.72068.