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Liz Woolcott, Andrea Payant, Becky Skeen & Paul Daybell (2021) Missing the MARC: Utilization of MARC Fields in the Search Process, Cataloging & Classification Quarterly, 59:1, 28-52, DOI: 10.1080/01639374.2021.1881010
Abstract: Utah State University Cataloging and Metadata Services (CMS) unit analyzed MARC record discoverability within the libraries’ discovery layer, Encore, using web analytics, a web-scrapping tool, and a relational database to examine MARC records listed in users’ search results. MARC records were identified, isolated, and coded to pinpoint where search terms appeared, determine whether they were present in full or in part, and ascertain prominent fields not present in records. Analysis of results showed that notes and summaries were important for record retrieval and that users interacted with authorized name fields more frequently than authorized subject fields.
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Paul Daybell, Andrea Payant, Elizabeth Woolcott, Becky Skeen, Anna-Marie Arnljots & Kurt Meyer (2022) A Tale of Two Levels: Analyzing the Discoverability and Impact of Item-Level Description in EAD Finding Aids, Journal of Archival Organization, DOI: 10.1080/15332748.2022.2089321
Abstract: As part of a multi-faceted research project examining user engagement with various types of descriptive metadata, Utah State University Libraries Cataloging and Metadata Services unit (CMS) investigated the discoverability of local Encoded Archival Description (EAD) finding aids. The research team put two versions of the same finding aid online with one described at the file (box or folder) level and the other at the item-level. Over a year later, the team pulled the analytics for each guide and assessed which descriptive level was most frequently accessed. The research team also looked at the type of search terms patrons utilized and where in the finding aid they were located. Usage data shows that personal names are the most common type of search term, search terms are most commonly found in the Collection Inventory, and that the availability of item-level description improves discovery by an average of 6,100% over file-level descriptions.