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    Data Science Support at the Academic Library

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    Thumbnail
    Name:
    Oliver-et-al-MS-final.pdf
    Size:
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    Format:
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    Description:
    Final Accepted Manuscript
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    Author
    Oliver, Jeffrey C. cc
    Kollen, Christine cc
    Hickson, Benjamin
    Rios, Fernando
    Affiliation
    Office of Digital Innovation and Stewardship, University Libraries, University of Arizona
    Issue Date
    2019-03-20
    Keywords
    data visualization
    GIS
    Python
    R programming
    reproducibility
    
    Metadata
    Show full item record
    Publisher
    Taylor & Francis Group
    Citation
    Jeffrey C. Oliver, Christine Kollen, Benjamin Hickson & Fernando Rios(2019): Data Science Support at the Academic Library, Journal of Library Administration, DOI:10.1080/01930826.2019.1583015
    Journal
    Journal of Library Administration
    Rights
    © The Author(s).
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Data science is a rapidly growing field with applications across all scientific domains. The demand for support in data science literacy is outpacing available resources at college campuses. The academic library is uniquely positioned to provide training and guidance in a number of areas relevant to data science. The University of Arizona Libraries has built a successful data science support program, focusing on computational literacy, geographic information systems, and reproducible science. Success of the program has largely been due to the strength of library personnel and strategic partnerships with units outside of the library. Academic libraries can support campus data science needs through professional development of current staff and recruitment of new personnel with expertise in data-intensive domains.
    ISSN
    0193-0826
    1540-3564
    DOI
    10.1080/01930826.2019.1583015
    Version
    Final accepted manuscript
    Additional Links
    https://www.tandfonline.com/doi/full/10.1080/01930826.2019.1583015
    ae974a485f413a2113503eed53cd6c53
    10.1080/01930826.2019.1583015
    Scopus Count
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