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.