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Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Estimates of the price or value of the individual journals within a full-text database may be useful to librarians engaged in serials reviews or other collection development projects, to scholars investigating the determinants of journal prices, and to publishers seeking to rationalize their pricing strategies. This paper evaluates six methods of apportioning the cost of a full-text database among the individual journals in the database--methods based on variables such as journal size, total citations, Journal Impact Factor (JIF) percentile, and single-journal list price. Each method is evaluated based on how well the resulting prices can be predicted by the determinants of journal prices identified in previous research. Although the six methods yield similar results, the single best option is to use price estimates that account for JIF percentile. If citation data are not available and cannot be estimated, the best alternative is to rely on the equal-value assumption--to split the total price equally among the wanted journals in the database. [ABSTRACT FROM AUTHOR]

To study library guides, as published on Springshare's LibGuides platform, new approaches are needed to expand the scope of the research, ensure comprehensiveness of data collection, and reduce bias for content analysis. Computational methods can be utilized to conduct a nuanced and thorough evaluation that critically assesses the resources promoted in library guides. Web-based library guides are curated by librarians to provide easy access to high-quality information and resources in a variety of formats to support the research needs of their users. Recent scholarship considers library guides as valuable resources and as de facto publications, highlighting the need for critical study. In this article, the authors present a novel model for comprehensively gathering data about a specific genre of books from individual LibGuide pages and applying computational methods to explore the resultant data. Beginning with a pre-selected list of 159 books, we programmatically queried the titles using the LibGuides Community search engine. After cleaning and filtering the resultant data, we compiled a list of 20,484 book references (of which 6,212 are unique) on 1,529 LibGuide pages. By testing against inclusion and exclusion criteria to ensure relevancy, we identified a total of 281 titles relevant to our topic. To gain insights for future study, citation analysis metrics are presented to reveal patterns of frequency, co-occurrence, and bibliographic coupling of books promoted in LibGuides. This proof-of-concept could be adopted for a variety of applications, including assessment of collections, public services, critical librarianship, and other complex questions to enable a richer and more thorough understanding of the information landscape of LibGuides. [ABSTRACT FROM AUTHOR]