Best practices in bibliographic data science
: Lahti L, Vaara V, Marjanen J, Tolonen M
: Jarmo Harri Jantunen, Sisko Brunni, Niina Kunnas, Santeri Palviainen, Katja Västi
: Research Data and Humanities Conference
: Oulu
: 2019
: Studia Humaniora Ouluensia
: Proceedings of the Research Data And Humanities (RDHUM) 2019 Conference: Data, Methods And Tools
: Studia Humaniora Ouluensia
: 17
: 57
: 65
: 978-952-62-2320-9
: 978-952-62-2321-6
: 1796-4725
: http://urn.fi/urn:isbn:9789526223216
: https://research.utu.fi/converis/portal/detail/Publication/44646716
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.
