Big Data Factories : Collaborative Approaches - Matei, Sorin Adam.; Jullien, Nicolas; Goggins, Sean P.; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2017)
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.
Keywords: Data mining; Big data; Bioinformatics; Application software; Research-Moral and ethical aspects; Data Mining and Knowledge Discovery; Big Data/Analytics.; Bioinformatics; Computer Appl. in Social and Behavioral Sciences; Research Ethics