Mak, Vanessa; Tjong Tjin Tai, Eric; Berlee, Anna --- "Introduction" [2018] ELECD 1435; in Mak, Vanessa; Tjong Tjin Tai, Eric; Berlee, Anna (eds), "Research Handbook in Data Science and Law" (Edward Elgar Publishing, 2018) 1

Book Title: Research Handbook in Data Science and Law

Editor(s): Mak, Vanessa; Tjong Tjin Tai, Eric; Berlee, Anna

Publisher: Edward Elgar Publishing

ISBN: 9781788111294

Section: Chapter 1

Section Title: Introduction

Author(s): Mak, Vanessa; Tjong Tjin Tai, Eric; Berlee, Anna

Number of pages: 15

Abstract/Description:

This book deals with one of the most important scientific developments of recent years, namely the exponential growth of data science. More than a savvy term that rings of robotics, artificial intelligence and other terms that for long were regarded as part of science-fiction, data science has started to become structurally embedded in scientific research. Data, meaning personal data as well as information in the form of digital files, has become available at such a large scale that it can lead to an expansion of knowledge through smart combinations and use of data facilitated by new technologies. This book examines the legal implications of this development. Do data-driven technologies require regulation, and vice versa, how does data science advance legal scholarship? Defining the relatively new field of data science requires a working definition of the term. By data science we mean the use of data (including data processing) for scientific research. The availability of massive amounts of data as well the relatively cheap availability of storage and processing power has provided scientists with new tools that allow research projects that until recently were extremely cumbersome if not downright impossible. These factors are also often described with the term ‘big data’, which is characterized by three Vs: volume, velocity and variety.The term data science is nonetheless broader, because it can also refer to the use of data sets that are large but still limited—and therefore, unlike big data, of a manageable size for processing.