Big Data Factories: Collaborative Approaches

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

Table of Contents

Chapter 1 Introduction

Part I Theoretical Principles and Approaches to Data Factories
Chapter 2 Accessibility And Flexibility: Two Organizing Principles For Big Data Collaboration
Chapter 3 The Open Community Data Exchange: Advancing Data Sharing And Discovery In Open Online Community Science

Part II Theoretical Principles and Ideas for Designing and Deploying Data Factory Approaches
Chapter 4 Levels Of Trace Data For Social And Behavioural Science Research
Chapter 5 The Ten Adoption Drivers Of Open Source Software That Enables E-Research In Data Factories For Open Innovations
Chapter 6 Aligning Online Social Collaboration Data Around Social Order: Theoretical Considerations And Measures

Part III Approaches in Action Through Case Studies of Data Based Research, Best Practice Scenarios, or Educational Briefs
Chapter 7 Lessons Learned From A Decade Of Floss Data Collection
Chapter 8 Teaching Students How (Not) To Lie, Manipulate, And Mislead With Information Visualization
Chapter 9 Democratizing Data Science: The Community Data Science Workshops And Classes