Journal Article: “Data Please!: Expanding the Role of Libraries in Data Science through Digital Scholarship”

The article linked below was recently published by the Journal of eScience Librarianship.

Title

Data Please!: Expanding the Role of Libraries in Data Science through Digital Scholarship

Author

Halie Kerns
Binghamton University

Source

Journal of eScience Librarianship
14 (1): e961

DOI: 10.7191/jeslib.961

Abstract

Objective: As data science becomes more integrated into research and teaching, libraries are well-positioned to support this work. This study examines how a digital scholarship team at Binghamton University enhanced engagement with data science by assessing faculty, staff, and graduate student needs. Through focus group interviews, the study identifies key support gaps and outlines strategic initiatives to strengthen interdisciplinary data science programming within the library.

Methods: A qualitative approach was used, involving 26 focus group interviews with faculty, staff, and graduate students across STEM and related fields. Participants discussed their data science work, tools, training, and perceived resource gaps. Qualitative coding analysis identified key areas for library support.

Results: The study revealed three primary areas for library expansion in data science: (1) fostering interdisciplinary collaboration through outreach, (2) developing structured data science programming aligned with campus needs, and (3) establishing physical and digital infrastructure for data-intensive research. In response, the Digital Scholarship team implemented a three-semester data science programming plan, enhanced research community engagement, and contributed to a dedicated data science space in the upcoming Digital Scholarship Center.

Conclusions: Findings support the library’s role as a vital hub for data science. By aligning digital scholarship services with campus needs, the library can bridge gaps in data literacy, tool accessibility, and collaborative opportunities. While initial implementations show promising engagement, ongoing assessment will be necessary to refine services, particularly for undergraduates and emerging technologies. This study provides a model for other libraries to expand data science programming effectively.

Direct to Full Text Article

Filed under: Data Files, Interviews, Libraries, News, Reports