Computational Social Sciences

Big Data Factories

Collaborative Approaches

Editors: Matei, Sorin Adam, Jullien, Nicolas, Goggins, Sean P. (Eds.)

  • Provides basic researchers and practitioners direct guidelines and best case scenarios for developing activities related to data factoring
  • Presents methods for teaching data factoring
  • Proposes a set of principles for developing data factoring
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eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-59186-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $49.99
price for USA
  • ISBN 978-3-319-59185-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as “data factoring” emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing.
The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools.
Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it.
Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com

About the authors

Sorin Matei is a Professor at Brian Lamb School of Communication at Purdue University.  His focus areas are computational social science, collaborative content production, and data storytelling.
Nicolas Jullien is an Associate Professor at the LUSSI Department of Telecom Bretagne.  His research interests are in open and online communities.
Sean Patrick Goggins is an Associate Professor at Missouri's iSchool, with courtesy appointments as core faculty in the University of Missouri's Informatics Institute and Department of Computer Science.


Table of contents (2 chapters)

  • Introduction

    Jullien, Nicolas (et al.)

    Pages 1-6

  • Democratizing Data Science: The Community Data Science Workshops and Classes

    Hill, Benjamin Mako (et al.)

    Pages 115-135

Buy this book

eBook $39.99
price for USA (gross)
  • ISBN 978-3-319-59186-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $49.99
price for USA
  • ISBN 978-3-319-59185-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Big Data Factories
Book Subtitle
Collaborative Approaches
Editors
  • Sorin Adam Matei
  • Nicolas Jullien
  • Sean P. Goggins
Series Title
Computational Social Sciences
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-59186-5
DOI
10.1007/978-3-319-59186-5
Hardcover ISBN
978-3-319-59185-8
Series ISSN
2509-9574
Edition Number
1
Number of Pages
VI, 141
Number of Illustrations and Tables
4 b/w illustrations, 14 illustrations in colour
Topics