Scaling Up Bibliographic Data Science

Abstract

Bibliographic data science is an emerging research paradigm in digital humanities. It aims at systematic quantification of the trends in knowledge production based on large-scale analysis of bibliographic metadata collections and the methods of modern data science. Compared to the earlier related attempts in book history and sociology of literature, advances in data processing and quality control are now making it possible for the first time to scale up the analysis to millions of print products while at the same time paying attention to data quality, representativity and completeness. This provides a new quantitative method that can support the analysis of classical research questions in intellectual history. Here, we discuss the methodological challenges that we have encountered in such studies and how to scale up the solutions based on collaborative research efforts.

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