A3 Refereed book chapter or chapter in a compilation book
Best practices in bibliographic data science
Authors: Lahti L, Vaara V, Marjanen J, Tolonen M
Editors: Jarmo Harri Jantunen, Sisko Brunni, Niina Kunnas, Santeri Palviainen, Katja Västi
Conference name: Research Data and Humanities Conference
Publishing place: Oulu
Publication year: 2019
Journal: Studia Humaniora Ouluensia
Book title : Proceedings of the Research Data And Humanities (RDHUM) 2019 Conference: Data, Methods And Tools
Series title: Studia Humaniora Ouluensia
Number in series: 17
First page : 57
Last page: 65
ISBN: 978-952-62-2320-9
eISBN: 978-952-62-2321-6
ISSN: 1796-4725
Web address : http://urn.fi/urn:isbn:9789526223216
Self-archived copy’s web address: 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.
Downloadable publication This is an electronic reprint of the original article. |  |
