The body of data-science scholarship in library and information science is characterized less by methodological consensus than by a set of recurring intellectual tensions. At its core lies a debate over what kind of knowledge libraries should legitimately produce. One strand—rooted in evidence-based librarianship and aligned with the managerial turn in higher education—treats data science as an extension of assessment: usage analytics, impact metrics, and computational models designed to optimize resources and justify institutional value.
A second strand, grounded in digital humanities and critical information studies, positions data science as an epistemic partner in scholarship itself, where librarians participate in text mining, sociotechnical inquiry, and cultural analytics alongside faculty researchers. A third strand adopts a reflexive stance: rather than celebrating analytics, it interrogates their consequences, examining how data infrastructures encode power, bias, and inequity. These strands are not mutually exclusive—they coexist uneasily in conferences, journals, and job descriptions—but their coexistence signals that the field is still negotiating its own boundaries.
