Big Data Factories
Collaborative Approaches
Editors: Matei, Sorin Adam, Jullien, Nicolas, Goggins, Sean P. (Eds.)
Free Preview- 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
Buy this book
- 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 (9 chapters)
-
-
Introduction
Pages 1-6
-
Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration
Pages 9-22
-
The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science
Pages 23-35
-
Levels of Trace Data for Social and Behavioural Science Research
Pages 39-49
-
The Ten Adoption Drivers of Open Source Software That Enables e-Research in Data Factories for Open Innovations
Pages 51-65
-
Table of contents (9 chapters)
Recommended for you
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
- Softcover ISBN
- 978-3-319-86564-5
- Series ISSN
- 2509-9574
- Edition Number
- 1
- Number of Pages
- VI, 141
- Number of Illustrations
- 4 b/w illustrations, 14 illustrations in colour
- Topics
*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works and instructor copies are not included.
