NASIG Autumn 2024

The integration of AI into library practices offers significant potential for enhancing metadata quality. This session presents a project by the University of Toronto Libraries (UTL) that uses ChatGPT and Alma Cloud Apps to improve serial item metadata. The initiative employs AI-driven techniques to automate corrections and enhancements to item metadata and reload records using Alma's cloud apps. The project also demonstrates how these technologies enable retrospective itemization of serials and regeneration of summary holding statements. By utilizing AI and API tools, this scalable approach to metadata remediation allows for large-scale enhancements, improving the accessibility and utility of library collections.

1. Natural Language Processing and serials cataloging : an assisting perspective

What is under the umbrella of AI can really be a starting point in terms of assisting in serials description/cataloging? The complexity and different cases of serials nature, make it very difficult to rely on specific patterns that can be fed into an artificial intelligence model, in the context of teaching or training others on how to catalog a serial. On the other hand creating a trained chat bot on assisting in the basics of serials cataloging, with a serial expert, is something could be helpful. In this session I will present a chat bot I created and trained, for the purpose of the above.


2. Enhance! : Using Google Keep and Notepad++ to Automate Enhanced Contents Notes for a Large Scale Journal Cleanup

In summer of 2024, our cataloging department began work on a large-scale periodicals project requiring the recataloging, relabeling, and barcoding of roughly 12,000 volumes. To assist with the sheer quantity of materials, we cross-trained colleagues from other departments to manage the more straightforward journals, but this still left a large number of monographic series. To comply with our standards for bibliographic records, each item in these multi-author series would require enhanced 505 contents notes. By employing Google Keep for OCR and creating macros in Notepad++, we have streamlined this process to generate in five minutes what used to take twenty.

The Collection of the US House of Representatives began a Significance Assessment in 2023, with a goal of evaluating the collection, articulating and refining a collecting philosophy, and documenting institutional memory. This project revealed need for documentation beyond basic object research, and resulted in a great deal of new digitized collection information. This session will discuss our process, the challenges we encountered, and results so far in our efforts to make all this digitized research and information available, accessible and logically organized for users.

This case study details the University of Arkansas Libraries' initiative to restructure and flatten its library acquisitions fund model, reducing 370 funds and ledgers to under 85. This effort supports near-real-time financial insights via data dashboards within Alma Analytics and improves collection decision-making by enabling tracking of library expenditures in ways that better align with our collection development strategies. Extensive planning, stakeholder engagement, and a thorough review and updating of acquisitions policies and procedures were crucial to the project's success. The restructuring leverages iterative, project-based strategies, supported by detailed documentation, laying the groundwork for future acquisitions integrations with the campus procurement system.