Table of Contents:
  • 1 Introduction: Preparing Big Data for Analysis -- 2 Cleaning Data: Empirical Cases from Plant Science and Economics -- 2.1 Empirical Case: Measuring Business Cycles -- 2.2 Empirical Case: Processing and Interoperability Requirements for Imaging Data in Plant Phenomics -- 3 Cleaning by Clustering: The Principles Underpinning Data Cleaning Practices -- 4 Comparing Heuristics across Research Communities in Natural and Social Sciences -- 5 Conclusions -- References -- The Datum in Context: Measuring Frameworks, Data Series and the Journeys of Individual Datums -- 1 Introduction -- 2 Data Sets and Their Kinds -- 3 Economic Data: Perspectives on the World -- 3.1 Accounting -- 3.2 Indicating -- 4 Conclusions -- References -- Data Journeys Beyond Databases in Systems Biology: Cytoscape and NDEx -- 1 Introduction -- 2 Data Production: From Individual Experiments to High-Throughput Experiments -- 3 Data Travels in Systems Biology: Databases and Ontologies -- 4 Cytoscape: A Platform for Generating and Analyzing Network Diagrams -- 5 Further Analyzing Networks: Cytoscape's App Store -- 5.1 Applying an App for Identifying Active Modules -- 5.2 Applying an App for Modeling Diffusion -- 6 Network Expo: NDEx -- 7 Conclusions -- References -- A Data Journey Through Dataset-Centric Population Genomics -- 1 Traveling Findings and Data Journeys in Human Population Genomics -- 2 Scientific Data Structures -- 3 Dataset Journey Representations: Two Visualizations -- 3.1 Example: Lab Web Page Dataset Journey Visualization -- 3.2 Example: Excel Spreadsheet Dataset Journey Visualization -- 4 Data Journey Narratives: Datapoints and Datasets -- 4.1 Dataset Assembly Narrative -- 4.2 Dataset Journey Narrative -- 5 A Model of Dataset Journeys and Conclusions -- References -- Part III: Sharing: Data access, Dissemination and Quality Assessment
  • Sharing Data, Repairing Practices: On the Reflexivity of Astronomical Data Journeys -- 1 Introduction -- 2 An Architecture for Observation: Enabling Reflexive Uses of Data -- 2.1 Object: 'Astronomy is About Observing and Re-Observing Sources on the Sky' -- 2.2 Technology: Astronomical Data Are Digital, and Utilize a Standard Format -- 2.3 Social Institutions: Sharing Instruments and Data -- 3 Re-Using Data to Assess an Astronomical Discovery Claim -- 3.1 Record Distance: "A Lensed Galaxy at z = 10.0" -- 3.2 Three Hot Pixels -- 3.3 A Transient Source? -- 3.4 Lost in the Noise -- 3.5 The Toulouse Team Responds to Its Critics -- 4 Discussion and Conclusions -- References -- Evaluating Data Journeys: Climategate, Synthetic Data and the Benchmarking of Methods for Climate Data Processing -- 1 Introduction -- 2 Data Journeys Envisioned by ISTI -- 3 Evaluating Data Journeys: Benchmarking and Its Importance -- 4 Another Variety of Model-Data Symbiosis -- 5 Concluding Remarks -- References -- The Babel of Drugs: On the Consequences of Evidential Pluralism in Pharmaceutical Regulation and Regulatory Data Journeys -- 1 Introduction -- 2 Regulation with Non-travelling Data -- 3 Regulation with Travelling Data -- 4 How Far Can EHR Data Travel? -- 5 "Delivering the Proof in the Policy of Truth" -- References -- Part IV: Interlude -- Most Often, What Is Transmitted Is Transformed -- References -- Part V: Interpreting: Data Transformation, Analysis and Reuse -- The Reuse of Digital Computer Data: Transformation, Recombination and Generation of Data Mixes in Big Data Science -- 1 Introduction -- 1.1 Scientific Data vs Computer Data -- 2 Unpacking Digital Data Reuse in Data Linkage Practice -- 2.1 Introduction to MEDMI -- 2.2 Data Relations and Epistemic Relations -- 2.3 The Computational Logistics of Digital Data Mixing
  • 2.4 "You Need to Say Exactly What You Want": Data Mixing and Boundaries of Practice -- 3 Discussion: The Relationality of Scientific Computer Data -- 3.1 Computer Data as Socio-Technical Relational Objects -- 3.2 Computer Data as Programmable, Granular and Composite -- 3.3 Socio-Technical Relations and Epistemic Relations -- 3.4 The Scaffolded Relationality of Scientific Computer Data -- 3.5 Computational Data Journeys -- 4 Conclusion -- References -- Data, Meta Data and Pattern Data: How Franz Boas Mobilized Anthropometric Data, 1890 and Beyond -- 1 Introduction -- 2 Boas's Statistical Outlook -- 3 Boas's Data Sheets -- 4 Use and Re-use of Data -- 5 Conclusion -- References -- Radiocarbon Dating in Archaeology: Triangulation and Traceability -- 1 The Quest for an Absolute Chronology -- 2 Capturing Radiogenic Data -- 3 Calibration: Refinement and Conversion -- 4 Traceability and Triangulation -- 5 Robustness Reasoning About Temporal Data -- 6 Conclusion -- References -- Part VI: Ends: Data Actionability and Accountability -- 'Overcoming the Bottleneck': Knowledge Architectures for Genomic Data Interpretation in Oncology -- 1 Introduction -- 2 The Data Interpretation Bottleneck -- 3 Knowledgebases and Databases -- 4 A Spectrum of Data Repositories -- 5 Practitioners' Accounts of the Database/Knowledgebase Distinction -- 6 Why Knowledgebases? -- 7 Modes of Curation -- 8 Trust and Transparency -- 9 Curation, Interpretation, and Levels of Evidence -- 10 Heterogeneity -- 11 Conclusion -- References -- Realizing Healthful Housing: Devices for Data Travel in Public Health and Urban Redevelopment in the Twentieth Century United States -- 1 The Problem of Data in Housing -- 2 Re-considering Housing Data -- 3 Public Health and Housing Standards: The Committee on the Hygiene of Housing
  • 4 The Appraisal Method: Transforming Standards Back into Data that Travels -- 5 Conclusion -- References -- From Washington DC to Washington State: The Global Burden of Diseases Data Basis and the Political Economy of Global Health -- 1 Introduction -- 2 DALYs as Global Metrics: The World Bank and Economic Triage -- 3 Health System Data and Political Triage: Primary Health Care at WHO -- 4 Missing and Alternative Numbers: The Low Visibility of DALY-Based Triage -- 5 Conclusion -- References -- Data Journeys in Art? Warranting and Witnessing the 'Fake' and the 'Real' in Art Authentication -- 1 Introduction -- 2 Here, See -- 3 See It, See It Not -- 4 In the Blink of an Eye -- 5 Is There Anything to See? Is There Anyone to See it? -- 6 Conclusion: Varieties of Data and Journeys of Art -- 7 Coda -- References -- Part VII: Afterword -- Afterword: Data in Transit -- References -- Visual Metaphors: Howardena Pindell, Video Drawings, 1975 -- Index
  • Intro -- Preface: A Roadmap for Readers -- References -- Acknowledgements -- Contents -- Learning from Data Journeys -- 1 Introduction: Data Movement and Epistemic Diversity -- 2 Mutability and Portability: Data as Lineages -- 3 Data Journeys as Units of Analysis -- 4 The Significance of Articulating Data Challenges -- 5 Nodes of Difference and Similarity -- 6 Conclusion: Why Study Data Journeys? -- References -- Part I: Origins: Data Collection, Preparation and Reporting -- Material Origins of a Data Journey in Ocean Science: How Sampling and Scaffolding Shape Data Practices -- 1 Introduction -- 2 The Continuous Plankton Recorder Survey -- 3 Material Interactions and their Epistemological Implications -- 3.1 Deformation of Plankton Organisms and Identification -- 3.2 Silk Specifications and Quantification -- 4 Material Integration and Continuity -- 5 Scaffolding Sample Analysis and the Creation of Knowledge -- 6 Conclusion -- References -- What Data Get to Travel in High Energy Physics? The Construction of Data at the Large Hadron Collider -- 1 Introduction -- 2 Selection Criteria and Search Strategy for Usable Data in the ATLAS Experiment -- 3 Local Data Journey at the LHC -- 4 Conclusions -- References -- Tracing Data Journeys Through Medical Case Reports: Conceptualizing Case Reports Not as "Anecdotes" but Productive Epistemic Constructs, or Why Zebras Can Be Useful -- 1 Introduction -- 2 Background: What Are Medical Case Reports? -- 3 Detecting Patterns and Themes in Case Reporting and Re-use -- 3.1 Broader Patterns of Re-use -- 3.2 Case Reports on Infectious Diseases -- 3.3 Case Reporting of Adverse Effects -- 4 Conclusions: Implications for Understanding How Data Journey -- References -- Part II: Clustering: Data Ordering and Visualization -- From Dirty Data to Tidy Facts: Clustering Practices in Plant Phenomics and Business Cycle Analysis