The book proposes a systematic approach to big data collection documentation & 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 & exploitable by parties not directly involved with data collection & documentation Furthermore data factoring uses & encourages pre-analytic operations that add value to big data sets especially recombining & 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 & processing that can lead to building better scientific collaboration & data sharing strategies & tools Finally the book presents the teaching utility of data factoring & the ethical & privacy concerns related to it Chapter 9 of this book is available open access under a CC BY 40 license at linkspringercom