Big data, little data, no data, scholarship in the networked world, Christine L. Borgman
Type
Label
Big data, little data, no data, scholarship in the networked world, Christine L. Borgman
Language
eng
Bibliography note
Includes bibliographical references (pages 289-360) and index
Index
index present
Literary Form
non fiction
Main title
Big data, little data, no data
Nature of contents
bibliography
Oclc number
894491357
Responsibility statement
Christine L. Borgman
Sub title
scholarship in the networked world
Summary
'Big data' is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data<U+0127> because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure<U+0127> an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation<U+0127> six "provocations" meant to inspire discussion about the uses of data in scholarship<U+0127> Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.--, Provided by publisher
Table Of Contents
Provocations -- What are data? -- Data scholarship -- Data diversity -- Data scholarship in the sciences -- Data scholarship in the social sciences -- Data scholarship in the humanities -- Sharing, releasing, and reusing data -- Credit, attribution, and discovery of data -- What to keep and why to keep them
Creator
Subject
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Incoming Resources
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