Description
Facing increasing demands on physical space, many academic libraries are investigating large-scale de-duplication of physical collections. These collection management efforts require trustworthy, complete data and must consider not just local needs, but shared print retention commitments, preservation of scarce and at-risk materials, and continuous access to licensed electronic versions. While common solutions exist for monographic resources, these questions remain much harder to answer for serials. The pymarc and pandas libraries offer effective tools for analyzing bibliographic data to generate “families” of both print and electronic serial records, and compare holdings of those titles as part of large-scale de-duplication efforts.Presenter(s): Speaker: Kelly Thompson, University of Minnesota