In the last decade, data science has generated new fields of study and transformed existing disciplines. As data science reshapes academia, how can libraries and librarians engage with this rapidly evolving, dynamic form of research? Can libraries leverage their existing strengths in information management, instruction, and research support to advance data science?
Data Science in the Library: Tools and Strategies for Supporting Data-Driven Research and Instruction brings together an international group of librarians and faculty to consider the opportunities afforded by data science for research libraries. Using practical examples, each chapter focuses on data science instruction, reproducible research, establishing data science services and key data science partnerships.
This book will be invaluable to library and information professionals interested in building or expanding data science services. It is a practical, useful tool for researchers, students, and instructors interested in implementing models for data science service that build community and advance the discipline.
PART 1: DATA SCIENCE AND RESEARCH LIBRARIES PERSPECTIVES
Sustainability and Success Models for Informal Data Science Training within
Libraries
Elizabeth Wickes
The Fundación Juan March DataLab: A Data Science Unit within a Research
Support Library
Luis Martķnez-Uribe, Paz Fernįndez and Fernando Martķnez
PART 2: DATA SCIENCE INSTRUCTION
Toward Reproducibility: Academic Libraries and Open Science
Joshua Quan
Start with Data Science
Mine Ēetinkaya-Rundel
PART 3: DATA SCIENCE SERVICES
In Support of Data-Intensive Science at the University of Washington
Jenny Muilenburg
From a Data Archive to Data Science: Supporting Current Research
Tim Dennis, Zhiyuan Yao, Leigh Phan, Kristian Allen, Jamie Jamison, Doug
Daniels and Ibraheem Ali
PART 4: DESIGNING AND STAFFING DATA SCIENCE
In-House Training as the First Step to Becoming a Data Savvy Librarian
Jeannette Ekstrųm
Designing for Data Science: Planning for Library Data Services
Joel Herndon
Joel Herndon is the Director of the Center for Data and Visualization Sciences (CDVS) at Duke University Libraries where he leads a library data science program providing support for data visualization, data management, digital mapping, and computational research support. Joel's research focuses on how universities can improve data sharing and data science initiatives through partnerships, training, infrastructure, and project support.
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