Get Data Help: Academic Libraries as the Home for Data Science Support Across Disciplines
Shannon Ricci
Associate Head, Data Science Services, Library
North Carolina State University
Michelle Thompson Gumbs
Senior Geographic Information Systems Specialist
New York University
Mara Blake
Head, Data Science Services
North Carolina State University
With growing campus data science needs, academic institutions are developing programs to support users across disciplines. This briefing presents a model for delivering data science support through library services at two large, STEM-focused research universities. These data science service departments in the Libraries offer workshops, course-embedded sessions, specialized computer labs, and consulting to meet cross-disciplinary data needs. Both programs leverage partnerships with other campus units for service delivery and scaling. The graduate student staffing model forms a core component of the consulting programs, which provides sustainable staffing for data science assistance to patrons at academic institutions while simultaneously offering a high-impact experiential learning experience for the student consultants. The briefing will also provide information about the outcomes of a collaboration to disseminate information about this type of model through a project funded by the Alfred P. Sloan Foundation. It will also share the development of programs, methods for supporting students, performance metrics demonstrating the program’s impact, techniques for engaging in campus partnerships, and a discussion of strategies for scaling and enhancing data science support services to meet the growing needs of campus communities.
Yale’s “One Organization” Approach to Scaling Data-Intensive Research Support
Lauren Di Monte
Associate University Librarian for Research & Learning
Yale University
Rebecca Dikow
Director for Research Innovation & Student Success and Director of Marx Science and Social Science Library
Yale University
As research becomes increasingly data-intensive, academic libraries face a critical challenge: how to scale specialized support sustainably without creating unsustainable silos or major gaps in services. Yale is developing a “one organization” model where libraries, IT, and research centers operate beyond traditional organizational boundaries to support data-intensive scholarship. Rather than building parallel infrastructure and duplicating expertise, Yale’s approach emphasizes shared positions with dual reporting lines, coordinated hiring to ensure complementary skill sets across units, workflows designed with warm handoffs between teams, collaborative development of enterprise research infrastructure, innovative approaches to acquiring and managing data collections, and institution-wide data governance frameworks. This session will describe this model, offer some cultural and organizational lessons learned, and point to future directions. The presentation may be relevant to senior library and IT administrators grappling with resource constraints while facing mounting pressure to support computational research, data science, and AI-driven scholarship.
