© 2017

Big Data Factories

Collaborative Approaches

  • Sorin Adam Matei
  • Nicolas Jullien
  • Sean P. Goggins
  • 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


Part of the Computational Social Sciences book series (CSS)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Nicolas Jullien, Sorin Adam Matei, Sean P. Goggins
    Pages 1-6
  3. Theoretical Principles and Approaches to Data Factories

    1. Front Matter
      Pages 7-7
    2. Sean P. Goggins, A. J. Million, Georg J. P. Link, Matt Germonprez, Kristen Schuster
      Pages 23-35
  4. Theoretical Principles and Ideas for Designing and Deploying Data Factory Approaches

  5. Approaches in Action Through Case Studies of Data Based Research, Best Practice Scenarios, or Educational Briefs

    1. Front Matter
      Pages 77-77
    2. Kevin Crowston, Megan Squire
      Pages 79-100
    3. Athir Mahmud, Mél Hogan, Andrea Zeffiro, Libby Hemphill
      Pages 101-114
    4. Benjamin Mako Hill, Dharma Dailey, Richard T. Guy, Ben Lewis, Mika Matsuzaki, Jonathan T. Morgan
      Pages 115-135 Open Access
  6. Back Matter
    Pages 137-141

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


trends in data collection data recombination and reuse creating collaborative spaces fungible big data sets factoring data alphabet of social interaction networks of online interaction large scale data privacy and security research ethics

Editors and affiliations

  • Sorin Adam Matei
    • 1
  • Nicolas Jullien
    • 2
  • Sean P. Goggins
    • 3
  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.Technopôle Brest-IroiseIMT Atlantique (Telecom Bretagne)Brest Cedex 3France
  3. 3.Computer ScienceUniversity of MissouriColumbiaUSA

About the editors

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.

Bibliographic information

Industry Sectors
IT & Software
Consumer Packaged Goods
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences