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Reviewed by:
  • Big Data, Little Data, No Data: Scholarship in the Networked World by Christine L. Borgman
  • Ana Peraica
by Christine L. Borgman. MIT Press, Cambridge, MA, 2015. 400pp., illus. Trade. ISBN: 978-0-262-02856-1.

Big Data, Little Data is an interdisciplinary information/data studies theoretical compendium profiled as a comprehensive source for understanding the status and importance of data in different sciences and disciplines and for introducing and comparing the use of different data methods and tools, along with the scenario of their change in the networked society. The author introduces major theories of data collection, classification, analysis and releasing, with critical analysis of each strategy. The major focus of the research falls on definition of data scholarship and academic (fair) practices of using data, recording provenances and data approval release methods, as well as metadata creation in sciences, social sciences and humanities, with special regard to newer platforms. Aside from production, the research also refers to post-production of data, in terms of curating, sharing and reusing and different types of data collections, such as archives, repositories, etc.

The central concept of the study, as noted, is “data scholarship,” formed around the year 2000 as a special research field working with data only. Special focus throughout the book is given to sharing practices, especially online sharing. Different platforms are taken into account, along with their limitations—such as data embargos (time-limited proprietary [End Page 91] periods after completion of research, which persist even when the research was publicly funded, as for example in astronomy)—but also problems of data reuse and plagiarism. The ethics of data is analyzed separately, in the concluding chapter.

The book is well organized, in clear units of different disciplines, pointing at divergences of implementation of data in different branches, for example, astronomy and archaeology. The organization of subchapters and subdivisions is analytical and tactical, and so easily memorable. The predominant method used is the systematic deduction, providing scientific evidence for commonsense insights, making them even clearer in self-evidence. Although not a central point of research, different types of border academic practices, often expressed in jargon (for example, “salami slicing,” or “radioactive data”), make the reading more connected to real life.

What makes this book important is its elementary explanatory style, making the reading accessible to scholars of various disciplines and easily understood by students who are only about to start working with data material, either in academia or even business. It is an interesting and well-written compendium of already known things found in one place. To illustrate the clarity and thoroughness of this research, one example serves well: The bibliographic reference list counts 70 pages of referred literature.

This reading may be of enormous value to interdisciplinary scholars seeking to test or adapt different data methods, but also for students, who need to be introduced to them. I would recommend this book unreservedly, for its clarity, well-organized arguments and thorough approach, as a university handbook in the area. It is more than sufficient to provide a familiarity with the status, practices and procedures concerning any type of data in different research areas.

Ana Peraica
Email: <>.


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pp. 91-92
Launched on MUSE
Open Access
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