The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. CONTENTS: Abstract 1. Introduction - 1.1 Definitions - 1.2 Big Data dimensions 2. Technology - 2.1 Applications - - 2.1.1 In research 3. Philosophical aspects 4. Legal aspects - 4.1 GDPR - - Stages of processing of personal data - - Principles of data processing - - Privacy policy and transparency - - Purposes of data processing - - Design and implicit confidentiality - - The (legal) paradox of Big Data 5. Ethical issues - Ethics in research - Awareness - Consent - Control - Transparency - Trust - Ownership - Surveillance and security - Digital identity - Tailored reality - De-identification - Digital inequality - Privacy 6. Big Data research Conclusions Bibliography DOI: 10.13140/RG.2.2.11054.46401

This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature.

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.

This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed.

This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities’ characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.

Ethics, the moral compass guiding our actions, stands at the core of academic integrity. In the field of social sciences research, ethical violations persist as a silent threat, overshadowing the pursuit of knowledge. Uncovering the pervasive challenges, Methodologies and Ethics for Social Sciences Research boldly addresses the often-overlooked ethical breaches within scientific research. From plagiarism to the distortion of data, the book meticulously dissects common ethical pitfalls, emphasizing their significance in maintaining the credibility and trustworthiness of research outcomes. Recognizing the global nature of academic endeavors, the book sheds light on the cultural factors influencing ethical considerations, fostering a collective awareness among scholars. Methodologies and Ethics for Social Sciences Research transcends geographical boundaries, offering a comprehensive exploration of research methodologies in social sciences. It equips researchers, academics, teachers, and students with the tools to navigate the intricate terrain of scientific inquiry while upholding ethical standards. With a focus on the multicultural perspective, the book features contributions from academics worldwide, enriching the narrative with diverse experiences and insights. By incorporating practical examples of ethical violations from different countries, it not only highlights common ethical dilemmas but also provides a foundation for a shared global understanding of research ethics.

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences.

This book gathers case studies presented at the International Conference on Responsible Research and Innovation in Science, Innovation and Society (RRI-SIS2017). It highlights European initiatives and projects in various domains and contexts, each of which explores how to create guidelines and good practices for Responsible Research and Innovation and how to promote them among citizens, industry stakeholders, policy and decision makers, research funders and educational institutions to foster their adoption as a potential benchmark in establishing RRI processes. Further, the book discusses gender and ethical issues, which are highly relevant for RRI initiatives in connection with representativeness, risks and in some cases, minority rights.

The SAGE Handbook of Mixed Methods Research Design provides a comprehensive overview of the existing typologies and future potential applications of this fast evolving field. With a unique focus on design, this handbook prepares researchers for the changing conditions in which they will conduct their studies.