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The Ebook Call Number Project at the University of South Florida Libraries automated the process of adding Library of Congress (LC) Classification numbers into call number fields, thereby enhancing ebook metadata while minimizing manual work. This initiative aimed to improve the accuracy of analytics reporting for discipline-specific collection assessment and reporting. The project involved analyzing existing metadata, developing a Python-based automation tool using OCLC’s Connexion API, and implementing a systematic workflow within the Ex Libris Alma Library Management System for cataloging and quality control. The effort increased the number of ebook records with LC call numbers from 91 percent to 99 percent, significantly enhancing reporting accuracy and efficiency. This article details the project’s rationale, methodology, and outcomes, demonstrating how automation and artificial intelligence can streamline metadata enrichment in academic libraries.
Finally, the article presents an alternative method for smaller institutions that lack access to programmers or programming solutions and discusses future applications
