Big Data Factories : Collaborative Approaches / edited by Sorin Adam Matei, Nicolas Jullien, Sean P. Goggins.

Format:

Book

Contributor:

Matei, Sorin Adam, editor.

Jullien, Nicolas, editor.

Goggins, Sean P., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Computational social sciences 2509-9574

Computational Social Sciences, 2509-9574

Language:

English

Subjects (All):

Data mining.

Big data.

Bioinformatics.

Application software.

Research--Moral and ethical aspects.

Research.

Data Mining and Knowledge Discovery.

Big Data/Analytics.

Computer Appl. in Social and Behavioral Sciences.

Research Ethics.

Local Subjects:

Data Mining and Knowledge Discovery.

Big Data/Analytics.

Bioinformatics.

Computer Appl. in Social and Behavioral Sciences.

Research Ethics.

Physical Description:

1 online resource (VI, 141 pages) : 18 illustrations, 14 illustrations in color.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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, et cetera). 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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-59186-5

9783319591865

Access Restriction:

Restricted for use by site license.