Buch
Big Data Factories
-Collaborative Approaches-Sorin Adam Matei; Nicolas Jullien; Sean P. Goggins (Hrsg.)
37,44
EUR
Lieferzeit 12-13 Tage
Übersicht
Verlag | : | Springer International Publishing |
Buchreihe | : | Computational Social Sciences |
Sprache | : | Englisch |
Erschienen | : | 30. 08. 2018 |
Seiten | : | 141 |
Einband | : | Kartoniert |
Höhe | : | 235 mm |
Breite | : | 155 mm |
Gewicht | : | 238 g |
ISBN | : | 9783319865645 |
Sprache | : | Englisch |
Autorinformation
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.
Inhaltsverzeichnis
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.