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
Chapter1. Introduction.- Part 1: Theoretical Principles and Approaches to Data Factories.-
Chapter2. Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration.
Chapter3. The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science.- Part 2: Theoretical principles and ideas for designing and deploying data factory approaches.
Chapter4. Levels of Trace Data for Social and Behavioral Science Research.
Chapter5. The 10 Adoption Drivers of Open Source Software that Enables e-Research in Data Factories for Open Innovations.
Chapter6. Aligning online social collaboration data around social order: theoretical considerations and measures.- Part 3: Approaches in action through case studies of data based research, best practice scenarios, or educational briefs.
Chapter7. Lessons learned from a decade of FLOSS data collection.
Chapter8. Teaching Students How (NOT) to Lie, Manipulate, and Mislead with Information Visualizations.
Chapter9. Democratizing Data Science: The Community Data Science Workshops and Classes.
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.
