Taking count: A computational analysis of data resources on academic LibGuides

Abstract

The LibGuides platform is a ubiquitous tool in academic libraries and is commonly used by librarians to compile and share lists of recommended social science numerical data resources with users. This study leverages the machine-accessible nature of the LibGuides platform to collect links to data and statistical resources from over 10,000 LibGuide pages at 123 R1 research institutions. After substantial data cleaning and normalization, an analysis of the most common resources on those guides provides a unique window into the data repositories, libraries, archives, statistical data platforms, and other machine-readable data sources that are most popular on academic library guides. Results show that freely available resources from U.S. government agencies are among the most common to be included on data and statistical resources guides across institutions. Resources requiring paid licenses or memberships for full access, such as Statistical Insight (ProQuest), Social Explorer, and ICPSR are linked to most frequently overall, regardless of the percentage of institutions that include them. Findings also suggest that libraries are more likely to share traditional licensed statistical resources (e.g., Cambridge’s Historical Statistics of the United States) and collections of simple charts and graphs (e.g., Statista) than more robust and complex microdata resources (e.g., IPUMS).

Keywords

• data reference
• LibGuides
• data librarian
• web scraping