Best practices in bibliographic data science

Bibliographic data science aims to quantify historical trends in knowledge production based on the rich information content in library catalogue metadata collections. Compared to the earlier attempts in book history, advances in data science are now making it possible to automate remarkable portions of such analyses, while maintaining or improving data quality and completeness. Such quantitative approaches can support the analysis of classical questions in intellectual history. Here, we discuss best practices in this emerging research field.