Big Data, Little Data, No Data: Scholarship in the Networked World (Hardcover)
Christine L. Borgman
- 出版商: MIT
- 出版日期: 2015-01-02
- 售價: $1,200
- 貴賓價: 9.8 折 $1,176
- 語言: 英文
- 頁數: 416
- 裝訂: Hardcover
- ISBN: 0262028565
- ISBN-13: 9780262028561
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相關分類:
大數據 Big-data
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商品描述
"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 -- 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 -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- 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.
商品描述(中文翻譯)
「大數據」出現在《Science》、《Nature》、《經濟學人》和《Wired》雜誌的封面上,也出現在《華爾街日報》和《紐約時報》的頭版。但儘管媒體炒作,正如克里斯汀·博格曼在這本關於數據和學術研究的考察中指出的那樣,擁有正確的數據通常比擁有更多的數據更好;少量的數據可能和大數據一樣有價值。在許多情況下,沒有數據存在,因為相關數據不存在、無法找到或不可用。此外,數據共享困難,共享的激勵也很少,不同學科的數據實踐差異很大。
博格曼是學術交流領域的權威,她認為數據在孤立狀態下沒有價值或意義;它們存在於一個知識基礎設施中,這是一個由人、實踐、技術、機構、物質對象和關係組成的生態系統。在闡述她的研究前提(六個「挑釁」,旨在激發關於數據在學術研究中的應用的討論)之後,博格曼提供了科學、社會科學和人文學科數據實踐的案例研究,然後考慮了她的研究結果對學術實踐和研究政策的影響。博格曼主張,要長期管理和利用數據,需要大量投資於知識基礎設施;學術的未來就在此。