Contents

Preface [to read a PDF of the preface click here]

1.     Conceptualising data [to read a PDF of the chapter click here]
•    What are data?
•    Kinds of data
•    Data, information, knowledge, wisdom
•    Framing data
•    Thinking critically about databases and data infrastructures
•    Data assemblages and the data revolution

2.    Small data, data infrastructures and data brokers
•    Data holdings, data archives and data infrastructures
•    Rationale for research data infrastructures
•    The challenges of building data infrastructures
•    Data brokers and markets

3.    Open and linked data
•    Open data
•    Linked data
•    The case for open data
•    The economics of open data
•    Concerns with respect to opening data

4.    Big data [to read a PDF of the chapter click here]
•    Volume
•    Exhaustive
•    Resolution and indexicality
•    Relationality
•    Velocity
•    Variety
•    Flexibility

5.    Enablers and sources of big data
•    The enablers of big data
•    Sources of big data
•    Directed Data
•    Automated data
•    Volunteered data

6.    Data analytics
•    Pre-analytics
•    Machine learning
•    Data mining and pattern recognition
•    Data visualisation and visual analytics
•    Statistical analysis
•    Prediction, simulation and optimization

7.    The governmental and business rationale for big data
•    Governing people
•    Managing organisations
•    Leveraging value and producing capital
•    Creating better places

8.    The reframing of science, social science and humanities research
•    The fourth paradigm in science?
•    The re-emergence of empiricism
•    The fallacies of empiricism
•    Data-driven science
•    Computational social sciences and digital humanities

9.    Technical and organisational issues
•    Deserts and deluges
•    Access
•    Data quality, veracity and lineage
•    Data integration and interoperability
•    Poor analysis and ecological fallacies
•    Skills and human resourcing

10.    Ethical, political, social and legal concerns
•    Data shadows and dataveillance
•    Privacy
•    Data security
•    Profiling, social sorting and redlining
•    Secondary uses, control creep and anticipatory governance
•    Modes of governance and technological lock-ins

11.    Making sense of the data revolution
•    Understanding data and the data revolution
•    Researching data assemblages
•    Final thoughts

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