Using the WorldCat API to Develop Data-Driven Decision-Making for Gifts-in-Kind

Abstract

In practice, evaluation and acceptance of books donated to a library (gifts-in-kind) often lack the same data-driven decision-making that libraries apply to purchased materials. Factors of “specialness” or “uniqueness” that are important components of why libraries still seek donations are not necessarily data-driven. This practice may be especially true for items located within a library’s general collection, rather than special collections or archives. The research presented here develops new methods that support data-driven decision-making in evaluating gifts-in-kind, particularly for items for the general collection. The authors focus on the concept of rarity and geographic scarcity using OCLC holdings, the WorldCat API, and geospatial methods. They retroactively examined monographs added to the general collection as gifts over a ten-year period at the University of Colorado Boulder (UCB) that are an initial dataset of sixteen thousand or more books. The majority of items are neither unique or rare in holdings, nor are they geographically scarce. However, some are, and the shared characteristics of many of these rare or geographically scarce items may be relevant to Area Studies faculty, students, and researchers. While the results of this study are localized in scope, the methods developed could be easily replicated by libraries seeking to evaluate uniqueness and proximity of current or future gifts-in-kind with high efficiency and objectivity.