Recommended by Librarians: A Computational Citation Analysis Methodology for Identifying and Examining Books Promoted in LibGuides (Dataset and Scripts)

Abstract

Dataset and Jupyter notebooks (Python) created as the basis for an article aiming to expand current approaches to studying library guides as published on Springshare’s LibGuides platform. Library guides are curated web-based collections of resources created by librarians to support the research needs of their users by providing easy access to high-quality information and resources in a variety of formats. To conduct a nuanced and thorough evaluation of resources promoted in these guides, it is necessary to employ computational techniques. The Python code here is presented as a model for comprehensively gathering data from the LibGuides Community and individual LibGuide pages and applying computational methods to explore the resultant data. For our cases study, we started with a pre-selected list of 159 books, programmatically queried the titles using the LibGuides Community search engine, cleaned and filtered the data, and compiled a list of 1,529 relevant individual LibGuides referencing 20,484 books. Using citation analysis metrics, such as frequency, co-occurrence, and bibliographic coupling, we were able to gain some insight about books that are promoted on this platform.