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BK

Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne : SAGE, [2022]

xx, 246 stran : ilustrace ; 25 cm

ISBN 978-1-5297-3254-2 (vázáno)

Terminologický slovník

Obsahuje bibliografii na stranách 221-239, bibliografické odkazy a rejstřík

001650655

CONTENTS // List of Figures and Table viii // Data Stories x // About the Authors xi // Acknowledgements xii // Overmew of the Book xv // Introduction xvii // Parti Data in Society 1 // 1 Data in Society 3 // 1.1 Introduction: Who cares about data? 3 // 1.2 Datafication and its components 6 // 1.3 Data, ethics and knowledge production 10 // 1.4 Conclusion: The impact of Datafication 13 // Part II Data Creation 15 // 2 Big Data in Context 21 // 2.1 Introduction: The rise of Big Data 21 // 2.2 The Big Data mythology: Data transforms society 25 // 2.3 A historical perspective: Society transforms data 28 // 2.4 Conclusion: Data do not speak for themselves 32 // 3 Characteristics of Data 35 // 3.1 Introduction: Data do not stay still 35 // 3.2 Data are not neutral 40 // 3.3 Data are context-dependent 46 // 3.4 Conclusion: Characteristics of data 48 // 4 Data, Evidence and Knowledge 51 // 4.1 Introduction: The representational and the relational views // on data 52 // 4.2 What is evidence? The path from data to knowledge 55 // 4.3 Examples of data within the knowledge production cycle 57 // 4.4 Contrasting the representational and relational perspectives 59 // 4.5 Conclusion: Data science in a relational perspective 62 // Part III Data Circulation 65 // 5 Putting Data to Work 73 // 5.1 Introduction: The complexity of putting data to work 74 // 5.2 The challenge of�messy’ data 75 // 5.3 Infrastructures 77 // 5.4 Conventions and metadata 81 // 5.5 Models 84 // 5.6 Visualisations:

Forms, tools and interfaces 87 // 5.7 Curation 91 // 5.8 Conclusion: Forms of data work 93 // 6 New Data Skills 94 // 6.1 Introduction: Data expertise 94 // 6.2 What is data science? 95 // 6.3 Data science skills 101 // 6.4 Bringing skills together 107 // 6.5 Conclusion: Becoming a data scientist today 113 // 7 Governance of Data Journeys 116 // 7.1 Introduction: What is data governance? 117 // 7.2 Data as private commodities: Closed data 119 // 7.3 Data as public goods: Open data 122 // 7.4 A hard case: The journeys of health-related data 125 // 7.5 Shifting focus to usable data: The FAIR principles 129 // 7.6 International data journeys and the problem of // data inequities 131 // 7.7 Conclusion: Governance is not a silver bullet 134 // Part IV Data Value, Innovation and Responsibility 135 // 8 Data as a Source of Value 143 // 8.1 Introduction: What makes data valuable? 144 // 8.2 Assumptions about the value of data 145 // 8.3 Data and innovation 147 // 8.4 The data economy 149 // 8.5 Who benefits from the value of data? 152 // 8.6 Allocating value, responsibility and profit 154 // 8.7 How does AI add value to data? 156 // 8.8 The value of prediction 159 // 8.9 The value of metrics 160 // 8.10 Conclusion: Making data valuable 161 // CONTENTS // 9.1 // 9.2 // 9.3 // 9.4 // 9.5 // 9.6 // 9 Data Justice and Ethics // Introduction: From data value to data ethics // Which data are ethically sensitive? // Data justice: Implementing fairness // Ethics for data work: General frameworks

// Ethics in data work: Assessing technical decisions // Responsibilities of data workers // Conclusion: From analysis to action, from rules to power // 10 Responsible Use of Data as Evidence // 10.1 Introduction: Data matters // 10.2 What is evidence-based decision making? // 10.3 Ensuring responsible use of data // 10.4 Legal frameworks and formal regulation // 10.5 Codes of conduct // 10.6 Computational metrics and design // 10.7 Organisational and cultural interventions // 10.8 Institutional Review Boards // 10.9 Social participation and slow science // 10.10 Conclusion: Responsibility, monitoring and trust // Part V Conclusion: Data and the Knowledge We Need // 11 Towards Good Data Science // 11.1 Lesson 1:�Data’ is a relational category // 11.2 Lesson 2: Infrastructures and data stewardship are // essential to extract knowledge from Big Data // 11.3 Lesson 3: Data workers must use data sources with // discernment and be aware of the risks of discrimination // and inequality connected to data // 1.4 Lesson 4: Ethics, security and social responsibility are // a fundamental part of data work // 1-5 Lesson 5: Responsible data work requires social dialogue, // community engagement and contributions to data literacy // Glossary // References // Index // 163 // 164 // 165 // 168 // 172 // 175 // 178 // 182 // 184 // 185 // 187 // 191 // 192 // 194 // 195 // 197 // 198 // 199 // 201 // 203 // 211 // 212 // 212 // 213 // 215 // 216 // 218 // 221 // 240