Reproducibility and explainability in digital humanities

Abstract

This special issue of the International Journal of Digital Humanities (IJDH) focuses on reproducibility and explainability in the Digital Humanities (DH) context, and examines developments and disciplinary efforts to implement, integrate, and recognize reproducibility and explainability as DH research areas and practices (International Journal of Digital Humanities, 2023a, b).The wide disciplinary range of the contributions to this issue points to the relevance of these two deeply interconnected topics for the development of DH as a multidisplinary endeavor that includes significant methodological reflection. The issue features articles conceived from the perspective of Computational Linguistics and Literary Studies, Art History, the History of Science, Sociology, Political Science, and Information Science. At the same time, the articles' data, corpora, and research methods represent the full spectrum of DH research. Data range from corpora of literary novels, historical newspapers, research articles, political processes, and databases of literary awards, to visual sources from art history and the history of science and structured data from research and institutional documentation. Methods include digital editing and publishing, research code and data management, digital archival work, topic modeling, sentiment and network analysis, linked data, knowledge graphs, species models, as well as techniques from explainable artificial intelligence, computer vision, and visual analytics.This special issue was first conceived in the wake of the controversy around Nan Z. Da's article, "The Computational Case against Computational Literary Studies" in a 2019 issue of Critical Inquiry (Da, 2019a, b). While many contributions to the B Thorsten Ries