Book Review: Data Science in the Library: Tools and Strategies for Supporting Data-Driven Research and Instruction

Several times each month, we are pleased to republish a recent book review from the Canadian Law Library Review (CLLR). CLLR is the official journal of the Canadian Association of Law Libraries (CALL/ACBD), and its reviews cover both practice-oriented and academic publications related to the law.

Data Science in the Library: Tools and Strategies for Supporting Data-Driven Research and Instruction. Edited by Joel Herndon. London, U.K.: Facet, 2022. 311 p. Includes bibliographic references and index. ISBN 9781783304608 (hardcover) $185.35; ISBN 9781783304592 (softcover) $106.83; ISBN 9781783304615 (eBook) $182.42; ISBN 9781783305186 (ePUB) $74.53.

Reviewed by Erica Friesen
Research and Instruction Librarian (Law) & Online Learning Specialist
Queen’s University

Although the term “data science” may be instantly recognizable as a current catchphrase in information technology, resources for librarians on the topic are somewhat limited. This edited volume helps to fill this gap by addressing the role of the research library in supporting data science from a practical perspective.

As a growing field that is notoriously difficult to define, inherently multi-disciplinary, and methodologically varied, data science can prove elusive as a target of library services, structures, and supports. To address this issue, Data Science in the Library wisely takes as its subject the more broadly defined “rise of data science” (p. 38) rather than specificities within this field. This approach makes its case studies and practical insights extremely valuable from an institutional and organizational perspective. However, subject specialists like law librarians may be disappointed with the lack of specificity regarding discipline-specific concerns.

The book is most valuable as a compilation of practical case studies, guiding principles, ideas, concerns, and models when considering the place of data science in a research library. An introduction by editor Joel Herndon, director of the Center for Data and Visualization Sciences at Duke University Libraries, provides valuable insight into the challenges of defining data science and its rising prominence in academic spheres. The book is then divided into four parts, with each theme brought into focus by data science professionals representing research libraries in the United States and Europe.

Part 1: Data Science and Research Libraries – Perspectives focuses on how research libraries have been responding to the rise of data-driven research, including a case study on the evolution of the Fundación Juan March DataLab. In Part 2, data science instruction is the target of discussion, both as a potential area for library-led instruction and with regards to university-level data science courses. Part 3: Data Science Services details two case studies of American research libraries that have expanded existing data services to support data science and the various choices and challenges that have been made during their implementation. Lastly, Part 4: Designing and Staffing Data Science targets questions of how to staff a research library, both from a hiring and training perspective, to fully support data science.

Common themes and challenges run throughout the selection of chapters, including the pervasive challenges of sustainability, technological infrastructure, staffing, training, and assessment. As contributor Elizabeth Wickes states, “Creating something completely new is not always necessary” (p. 51), and many chapters encourage libraries to identify and operationalize skillsets and knowledge that already exist as a starting point for building out data science services. Collaboration is another crucial and recurring theme. Students, faculty, researchers, IT departments, and external organizations are often identified as resources that library staff can leverage to ensure the success of a program or service. For data science support in particular, these partnerships can prove beneficial from a sustainability perspective because constantly changing and updating methodologies make it difficult to retain and train library staff on all relevant skillsets.

The book’s focus on data science through the lens of the library will prove invaluable to those struggling with questions about the place of the research library in such conversations. At an organizational or institutional level, the book provides several interesting case studies for thinking about the implementation of data science services. At the individual level, the book has perhaps less to offer, though it does provide several starting points for librarians who are interested in developing their skills in this area or developing training and workshops at their library. As the focus is on academic libraries, Data Science in the Library will be of most interest to academic librarians or administrators. It will also be of interest to those involved in creating, adapting, or evaluating a data science strategy for their library.