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Big Data, Little Data, No Data: Scholarship in the Networked World

by Christine L.Borgman
The MIT Press, Cambridge, MA, 2015
400 pp., illus. 7 b/w. Trade, $32.00
ISBN: 9780262028561.

Reviewed by Ana Peraica, PhD
Independent scholar

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, 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 of strategies. Major focus of research falls on definition of data scholarship and academic (fair) practices of using data, recording provenances, data approval releases methods, as well as meta-data creation in sciences, social sciences and humanities, with special regard to newer platforms. Aside 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 their limitations, such as embargo on data, being a proprietary period for a limited-time after finishing the research, which may stay even when the research was publicly funded, as for example in astrology, but also problems of data reuse and plagiarism. The ethics of data is analysed separately, in the concluding chapter.

Book is well organised, in clear units of different disciplines, pointing at divergences of implementation of data in branches different as for example: astronomy and archaeology. The organisation of sub-chapters and subdivisions is analytical, tactical, and so easily memorable. The predominant method used is the systematic deduction, providing scientific evidence to actually common sense insights, making them even clearer in self-evidence. Although not being a central point of research, different types of border academic practices, often expressed in jargon (as for example 'salami slicing' or 'radioactive data,' are making the reading more connected to real life.

What makes this book important is its easy and explanatory style, making the reading accessible to scholars of various disciplines, written in elementary way which may be understood by students that 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 at one place. To illustrate the clarity and thoroughness of this research, one example serves well: that bibliographic reference list counts 70 pages of referred literature.

This reading might be of enormous value to interdisciplinary scholars, seeking to test or adapt different data methods, but also for students, that need to get introduced to them. Without holding back, I would recommend this book, for its clarity, well-organised arguments and throughout approach as a university handbook in the area. It is more than enough to get known to status, practices and procedures concerning any type of data in different research field areas.


Last Updated 1 June 2015

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