NASIG 2026

Do you ever try to use a file and find that it’s too big for Excel to handle? Do you need to edit many records at once or combine data from two different tables? OpenRefine — “a powerful free, open source tool for working with messy data” — can make these tasks simpler and faster. In a library setting, your data might come from catalog records, a KBART file, or usage reports. Your goal could be to check for inconsistencies in the catalog that should be corrected, compare e-resource collections, or evaluate usage of existing subscriptions. This interactive workshop will introduce you to the main features of the OpenRefine software, and together we will run through several projects using example files based on real-life projects. We’ll download the software together during the session, so please bring a laptop (Windows, Mac, or Linux) and come ready to participate.

Research metrics shape academic careers, influence funding priorities, and quietly drive inequities across disciplines. Many researchers and subject specialists encounter these systems as neutral or inevitable. In this session, a subject librarian and a scholarly communication librarian examine two intertwined critiques of metric culture. Together, the presenters highlight how deeply metrics structure academic life and discuss ways that scholarly communication librarians can help faculty and subject specialists recognize and resist the ways in which metrics have become the built-in logic of academic institutions with unintended consequences - shaping what research is produced, circulated, and valued.

Academic libraries steward large and complex collections to support teaching and research, yet determining how well these collections align with what users produce, seek, and use remains a persistent challenge. Traditional assessment practices rely primarily on circulation and e-resource usage metrics—measures that capture only a fragment of user engagement. These approaches overlook two equally important dimensions: faculty research output, which signals institutional scholarly activity, and user search behaviour, which reflects articulated information needs. This project introduces an integrated framework for collection intelligence: an AI-enabled model that brings together three key data sources—faculty research outputs, user discovery behaviour, and item use—within a unified analytical structure. Using Google Gemini Pro, each dataset is classified with the Library of Congress Classification (LCC) scheme and then analyzed to identify patterns of subject alignment, unmet demand, and emerging areas of scholarly interest. Viewed collectively, these three lenses offer a richer understanding of how library collections correspond to institutional knowledge production and user behaviour. The project moves libraries beyond siloed metrics toward integrated, evidence-based insight. It delivers a scalable, replicable model in which AI serves not only as a classifier but as a connector, linking research activity, discovery practices, and actual use to surface the library’s evolving role in the research ecosystem.