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

by Christine L. Borgman

An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities.

"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.

Genres: Nonfiction, Science, Technology, School

383 pages, Hardcover

First published January 2, 2015

Reviews

[Kim · Rating 1 out of 5]

I really hate library school sometimes.

[Kirsten Bedford · Rating 1 out of 5]

had to read this for a class, I really love info sci and data but this book boring AF!!!!! read two other books in the same class that basically said the exact same things as this one.

[Madras Mama · Rating 1 out of 5]

My attraction to the book remains somewhat nebulous, though it likely stemmed from its title. In hindsight, the book proves utterly devoid of merit for any audience or objective. Regardless of intent, any individual endeavoring to peruse its contents would undoubtedly find themselves compelled to consign it to obscurity, harboring a sincere wish to delete its contents from memory entirely. Whether the author adhered to a structured outline or merely transcribed fleeting thoughts remains uncertain. Should a book-police entity exist, it would undoubtedly deem the author worthy of the gravest penalty conceivable.

[Raeann · Rating 1 out of 5]

To be fair, I'm still not sure why I had to read this book, which is definitely a factor in how much I dislike it. I can't even look at the word "data" anymore without getting terrible flashbacks. This book was assigned as a framework for a paper, and while it gave some interesting insights into the world of data, other than that it was -- to put it bluntly -- "meh." I'm sure someone interested in the subject would find this book helpful, but I found it dull, disjointed, and like it was trying too hard to talk about a variety of topics. I gained no insights from it. There was no, "aha!" moment of understanding. So, unless you are unfortunate enough to have this book assigned for a class, avoid it.

You're welcome.

[Erin · Rating 2 out of 5]

Textbook for library school. This was fine for a textbook, and a subject worth thinking about, but repetitive when read straight through and unable to answer many of the questions it raised. (Not that every book is meant to answer questions about the direction in which the field is moving, but to repeatedly bring up "these are the things we should be thinking about as we go forward" and not offer any more concrete opinion than that... well, it gets old fast.)

[Kelly Lynn Thomas · Rating 3 out of 5]

The content of this book on data management and scholarship is interesting, if you're into that kind of thing. But like many scholarly books, it's completely over-written and the opposite of concise. There was a lot of repetition, which in some cases was necessary and helpful, but in other cases, not so much. Not worth reading unless you are a scholar or a librarian who works in academia.

[Chris Esposo · Rating 2 out of 5]

This book was not what I expected, and although it was disappointing for me, I can see how this book would be interesting for certain people, perhaps in the library and information sciences. The text can be described as a general overview of the data collection, and storage (mostly a conceptual accounting of which attributes could/are important in a particular field), and other points of interests on the data or the underlying phenomena (the information) that informs the nature of that data.

The book covers 3 main application domains: the sciences, social science, and humanities. For the first topic, is exclusively astronomy (the biggest “big data” field outside of biology in the sciences), social science is vaguely a discussion on economic/political science applications, and humanities focuses on applying machine learning and data analysis for on non-standard topics of interests to historians, like analyzing palampsets, or perhaps intelligent machine read-in (and maybe translation?) of ancient text etc.

From the perspective of a data scientist or machine learning practitioner, much of the book is vaguely interesting, but there’s almost nothing functional discussed in this text. If one of those kinds of professionals were to find themselves working in one of these application domains, I can imagine this sort of information (and probably at much more detailed level) could be easily gotten by most SMEs in a 1-hour knowledge share, so it’s unclear what value reading this book would have for those kinds of individuals.

For someone working in the library/information sciences, this could be a useful survey of data-processes applied to these domains, which could help those individuals better understand standard meta-data and attribute tables from those domains that they happen to be stewarding. Otherwise, I found little enlightenment from this book. A more interesting book that covers this topic in a more broad manner is “Data And Reality” by William Kent. A classic text, written when the relational database systems were still fairly new (written about 2 decades after), and connects the information sciences to their philosophic roots. As for this book, it’s not recommended, but for the specific readers identified above.

[Satu · Rating 1 out of 5]

Ei kiitos

[Brian · Rating 4 out of 5]

I was surprised to find a scholarly book on CD. Books on CD are usually more commercial.

[Gi V · Rating 3 out of 5]

Valuable insights but desperately needs a revision, a decade after first publication. In such a revision, I would recommend adding the human and environmental costs of data storage.

[Grace · Rating 5 out of 5]

Disclaimer: I work with one of Professor Borgman's graduate students, whose research is quoted liberally throughout the book.

There is nothing wrong with a thoroughly researched and academic book. In fact, it is the perfect book for someone interested in how we got to the current state of data scholarship. She also gives some good ideas about how to improve it.

I found the survey of data practices in different fields fascinating. It's long. But you have to understand how we got here, what works, what doesn't. You can't do that without thorough research, which this book has in spades.

I thoroughly recommend this book for data professionals. Early career researchers in all fields should also skim through this book and learn the research data norms of their particular field (lest they break the social norms and tank their career before it starts).

I put this on a list of recommended reading for beginning graduate students in geoscience:
http://ncarrda.blogspot.com/p/recomme...

[Drew Gordon · Rating 5 out of 5]

I like this book. It is about research data from an i-school academic, but it's not exactly a text book.

For those who are interested in how scientific practice turns into the effort to store, preserve, and share research data beyond the present, it is a very broad but well-researched summary of this practice across three main types of academic research: hard science, "soft" science, and the humanities.

It's not exactly a pleasure read, but if you are interested in the topic, it moves quickly enough and touches on a lot of interesting challenges and considerations in research data management.

[Devin Kelly · Rating 1 out of 5]

really boring.

[モーリー · Rating 3 out of 5]

More of a textbook than anything readable straight through. I found it lacking an argument overall so it wasn't that compelling.

[Plamen Miltenoff]

http://blog.stcloudstate.edu/ims/2017...

[ACRL]

Read by ACRL Member of the Week Haipeng Li. Learn more about Haipeng on the ACRL Insider Blog.