Description
Beyond providing space for data science activities, academic libraries are often overlooked in the data science landscape that is emerging at academic research institutions. Although some academic libraries are collaborating in specific ways in a small subset of institutions, there is much untapped potential for developing partnerships. As library and information science roles continue to evolve to be more data-centric and interdisciplinary, and as research using a variety of data types continues to proliferate, it is imperative to further explore the dynamics between libraries and the data science ecosystems in which they are a part. The Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems provides a global perspective on current and future trends concerning the integration of data science in libraries. It provides both a foundational base of knowledge around data science and explores numerous ways academicians can reskill their staff, engage in the research enterprise, contribute to curriculum development, and help build a stronger ecosystem where libraries are part of data science. Covering topics such as data science initiatives, digital humanities, and student engagement, this book is an indispensable resource for librarians, information professionals, academic institutions, researchers, academic libraries, and academicians.
Author: Nandita S. Mani
Publisher: Information Science Reference
Published: 05/06/2022
Pages: 448
Binding Type: Hardcover
Weight: 2.91lbs
Size: 11.00h x 8.50w x 1.00d
ISBN13: 9781799897026
ISBN10: 1799897028
BISAC Categories:
- Language Arts & Disciplines | Library & Information Science | Administration & Management
- Computers | Data Science | General
Author: Nandita S. Mani
Publisher: Information Science Reference
Published: 05/06/2022
Pages: 448
Binding Type: Hardcover
Weight: 2.91lbs
Size: 11.00h x 8.50w x 1.00d
ISBN13: 9781799897026
ISBN10: 1799897028
BISAC Categories:
- Language Arts & Disciplines | Library & Information Science | Administration & Management
- Computers | Data Science | General
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