Big Data Factories Collaborative Approaches /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Matei, Sorin Adam (Επιμελητής έκδοσης), Jullien, Nicolas (Επιμελητής έκδοσης), Goggins, Sean P. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Computational Social Sciences,
• Θέματα: Computer science.; Big data.; Research > Moral and ethical aspects.; Data mining.; Application software.; Bioinformatics.; Computer Science.; Data Mining and Knowledge Discovery.; Big Data/Analytics.; Computer Appl. in Social and Behavioral Sciences.; Research Ethics.

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...

Πίνακας περιεχομένων:

• 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.