Advancing Data Science, Data-Intensive Research, and Its Understanding Through Collaboration

Cynthia Hudson Vitale, Mary Lee Kennedy, Judy Ruttenberg

Source Title: Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems

ISBN13: 9781799897026|ISBN10: 1799897028|ISBN13 Softcover: 9781799897033|EISBN13: 9781799897040

DOI: 10.4018/978-1-7998-9702-6.ch002

MLA

Hudson Vitale, Cynthia, et al. "Advancing Data Science, Data-Intensive Research, and Its Understanding Through Collaboration." Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems, edited by Nandita S. Mani and Michelle A. Cawley, IGI Global, 2022, pp. 1-20. https://doi.org/10.4018/978-1-7998-9702-6.ch002

APA

Hudson Vitale, C., Kennedy, M. L., & Ruttenberg, J. (2022). Advancing Data Science, Data-Intensive Research, and Its Understanding Through Collaboration. In N. Mani & M. Cawley (Eds.), Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems (pp. 1-20). IGI Global. https://doi.org/10.4018/978-1-7998-9702-6.ch002

Chicago

Hudson Vitale, Cynthia, Mary Lee Kennedy, and Judy Ruttenberg. "Advancing Data Science, Data-Intensive Research, and Its Understanding Through Collaboration." In Handbook of Research on Academic Libraries as Partners in Data Science Ecosystems, edited by Nandita S. Mani and Michelle A. Cawley, 1-20. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9702-6.ch002

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Abstract

This chapter contributes to academic library institutions currently engaged in or formulating their strategy for engaging in data science. The chapter provides academic library institutions, and library and information science students, with a context in which to consider how they can collaborate locally and internationally to advance the use of data by scholars (students, researchers, and the public). Presented from the perspective of an association of research libraries, the chapter explores how, together, research libraries work with others to convene, inform, shape, and influence data science and data research policies and practices. The chapter provides examples of data and data science collaborations in teaching, learning, and research so the reader can identify specific skills and knowledge they may need or want to develop in order to collaborate, and they can learn about at least one existing or emerging type of collaboration they would like to explore further.