Work Cited
Arnold, T., & Tilton, L. (2019). New Data? The Role of Statistics in DH. Debates in the Digital Humanities.
Brüggemann, V., Bludau, M.-J., & Dörk, M. (2020). The fold: Rethinking interactivity in data visualization. DHQ, 14(3).
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1–42.
Dobson, J. (2021). Interpretable Outputs: Criteria for Machine Learning in the Humanities. Digital Humanities Quarterly, 15(2).
Doulamis, A., Doulamis, N., Protopapadakis, E., Voulodimos, A., & Ioannides, M. (2018). 4D modelling in cultural heritage. In Advances in Digital Cultural Heritage (pp. 174–196). Springer.
Drucker, J. (2011). Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1), 1–21.
Goldstone, A. (2019). Teaching Quantitative Methods: What Makes It Hard (in Literary Studies). Debates in the Digital Humanities 2019.
Jänicke, S., Franzini, G., Cheema, M. F., & Scheuermann, G. (2015). On Close and Distant Reading in Digital Humanities: A Survey and Future Challenges. EuroVis (STARs), 83–103.
Lipton, Z. C. (2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57.
Schöch, C. (2013). Big? smart? clean? messy? Data in the humanities. Journal of Digital Humanities, 2(3), 2–13.
Tenen, D. (2016). Blunt Instrumentalism: On Tools and Methods. Debates in the Digital Humanities, 83–91.
Theron, R., & Fontanillo, L. (2015). Diachronic-information visualization in historical dictionaries. Information Visualization, 14(2), 111–136.
Thomas, J. J., & Cook, K. A. (2006). A visual analytics agenda. IEEE Computer Graphics and Applications, 26(1), 10–13.
