Analyzes the regulation of data access and transfer to understand how internet users can obtain the data they generate.

An exploration of the current state of global trade law in the era of Big Data and AI. This title is also available as Open Access on Cambridge Core.

"Data collected and distributed on the internet is generally free, non-exclusive, and non-rivalrous. Yet online data is often difficult to access. This book examines the infrastructure for collecting, storing, and distributing data to show how it is embedded behind intellectual property and technological barriers. It proposes that the EU introduce an access and transfer governance right to data that can work in tandem with data protection rules. Chapters explore the subject matter of this protection, potential rights holders and the scope of the protection, and exceptions and limitations under intellectual property law and competition law. Comprehensive and timely, Regulating Access and Transfer of Data, sets the foundations for a new legal system for our data-driven generation"--

Written by a renowned expert on data protection law, this work examines the history, policies, and future of transborder data flow regulation, and is the only text to provide a detailed legal analysis of its global implications.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.