Data Journeys In The Sciences

Preface: A Roadmap for Readers......Page 5
References......Page 11
Acknowledgements......Page 13
Contents......Page 15
1 Introduction: Data Movement and Epistemic Diversity......Page 18
2 Mutability and Portability: Data as Lineages......Page 22
3 Data Journeys as Units of Analysis......Page 26
4 The Significance of Articulating Data Challenges......Page 29
5 Nodes of Difference and Similarity......Page 31
6 Conclusion: Why Study Data Journeys?......Page 35
References......Page 37
Part I: Origins: Data Collection, Preparation and Reporting......Page 42
1 Introduction......Page 43
2 The Continuous Plankton Recorder Survey......Page 46
3 Material Interactions and their Epistemological Implications......Page 48
3.1 Deformation of Plankton Organisms and Identification......Page 49
3.2 Silk Specifications and Quantification......Page 51
4 Material Integration and Continuity......Page 54
5 Scaffolding Sample Analysis and the Creation of Knowledge......Page 56
References......Page 58
1 Introduction......Page 61
2 Selection Criteria and Search Strategy for Usable Data in the ATLAS Experiment......Page 63
3 Local Data Journey at the LHC......Page 66
4 Conclusions......Page 70
References......Page 72
1 Introduction......Page 75
2 Background: What Are Medical Case Reports?......Page 77
3.1 Broader Patterns of Re-use......Page 79
3.2 Case Reports on Infectious Diseases......Page 82
3.3 Case Reporting of Adverse Effects......Page 84
4 Conclusions: Implications for Understanding How Data Journey......Page 87
References......Page 89
Part II: Clustering: Data Ordering and Visualization......Page 93
1 Introduction: Preparing Big Data for Analysis......Page 94
2 Cleaning Data: Empirical Cases from Plant Science and Economics......Page 96
2.1 Empirical Case: Measuring Business Cycles......Page 97
2.2 Empirical Case: Processing and Interoperability Requirements for Imaging Data in Plant Phenomics......Page 100
3 Cleaning by Clustering: The Principles Underpinning Data Cleaning Practices......Page 108
4 Comparing Heuristics across Research Communities in Natural and Social Sciences......Page 112
5 Conclusions......Page 113
References......Page 115
1 Introduction......Page 117
2 Data Sets and Their Kinds......Page 121
3 Economic Data: Perspectives on the World......Page 123
3.1 Accounting......Page 124
3.2 Indicating......Page 127
4 Conclusions......Page 131
References......Page 133
1 Introduction......Page 135
2 Data Production: From Individual Experiments to High-Throughput Experiments......Page 137
3 Data Travels in Systems Biology: Databases and Ontologies......Page 138
4 Cytoscape: A Platform for Generating and Analyzing Network Diagrams......Page 141
5 Further Analyzing Networks: Cytoscape’s App Store......Page 145
5.1 Applying an App for Identifying Active Modules......Page 147
5.2 Applying an App for Modeling Diffusion......Page 148
6 Network Expo: NDEx......Page 149
7 Conclusions......Page 153
References......Page 154
1 Traveling Findings and Data Journeys in Human Population Genomics......Page 158
2 Scientific Data Structures......Page 162
3 Dataset Journey Representations: Two Visualizations......Page 164
3.1 Example: Lab Web Page Dataset Journey Visualization......Page 165
3.2 Example: Excel Spreadsheet Dataset Journey Visualization......Page 166
4 Data Journey Narratives: Datapoints and Datasets......Page 167
4.1 Dataset Assembly Narrative......Page 168
4.2 Dataset Journey Narrative......Page 170
5 A Model of Dataset Journeys and Conclusions......Page 173
References......Page 178
Part III: Sharing: Data access, Dissemination and Quality Assessment......Page 181
1 Introduction......Page 182
2 An Architecture for Observation: Enabling Reflexive Uses of Data......Page 185
2.1 Object: ‘Astronomy is About Observing and Re-Observing Sources on the Sky’......Page 186
2.2 Technology: Astronomical Data Are Digital, and Utilize a Standard Format......Page 187
3 Re-Using Data to Assess an Astronomical Discovery Claim......Page 188
3.1 Record Distance: “A Lensed Galaxy at z = 10.0”......Page 189
3.2 Three Hot Pixels......Page 191
3.3 A Transient Source?......Page 192
3.4 Lost in the Noise......Page 194
3.5 The Toulouse Team Responds to Its Critics......Page 195
4 Discussion and Conclusions......Page 196
References......Page 199
1 Introduction......Page 202
2 Data Journeys Envisioned by ISTI......Page 204
3 Evaluating Data Journeys: Benchmarking and Its Importance......Page 209
4 Another Variety of Model-Data Symbiosis......Page 214
References......Page 215
1 Introduction......Page 218
2 Regulation with Non-travelling Data......Page 221
3 Regulation with Travelling Data......Page 223
4 How Far Can EHR Data Travel?......Page 226
5 “Delivering the Proof in the Policy of Truth”......Page 230
References......Page 233
Part IV: Interlude......Page 237
Most Often, What Is Transmitted Is Transformed......Page 238
References......Page 245
Part V: Interpreting: Data Transformation, Analysis and Reuse......Page 246
1 Introduction......Page 247
1.1 Scientific Data vs Computer Data......Page 249
2.1 Introduction to MEDMI......Page 252
2.2 Data Relations and Epistemic Relations......Page 254
2.3 The Computational Logistics of Digital Data Mixing......Page 258
2.4 “You Need to Say Exactly What You Want”: Data Mixing and Boundaries of Practice......Page 260
3.2 Computer Data as Programmable, Granular and Composite......Page 262
3.3 Socio-Technical Relations and Epistemic Relations......Page 264
3.4 The Scaffolded Relationality of Scientific Computer Data......Page 265
3.5 Computational Data Journeys......Page 266
4 Conclusion......Page 268
References......Page 269
1 Introduction......Page 272
2 Boas’s Statistical Outlook......Page 274
3 Boas’s Data Sheets......Page 277
4 Use and Re-use of Data......Page 281
5 Conclusion......Page 286
References......Page 288
1 The Quest for an Absolute Chronology......Page 291
2 Capturing Radiogenic Data......Page 294
3 Calibration: Refinement and Conversion......Page 296
4 Traceability and Triangulation......Page 299
5 Robustness Reasoning About Temporal Data......Page 302
6 Conclusion......Page 304
References......Page 305
Part VI: Ends: Data Actionability and Accountability......Page 308
‘Overcoming the Bottleneck’: Knowledge Architectures for Genomic Data Interpretation in Oncology......Page 309
1 Introduction......Page 310
2 The Data Interpretation Bottleneck......Page 311
3 Knowledgebases and Databases......Page 312
4 A Spectrum of Data Repositories......Page 315
5 Practitioners’ Accounts of the Database/Knowledgebase Distinction......Page 317
6 Why Knowledgebases?......Page 319
7 Modes of Curation......Page 320
8 Trust and Transparency......Page 321
9 Curation, Interpretation, and Levels of Evidence......Page 322
10 Heterogeneity......Page 324
11 Conclusion......Page 326
References......Page 328
1 The Problem of Data in Housing......Page 332
2 Re-considering Housing Data......Page 335
3 Public Health and Housing Standards: The Committee on the Hygiene of Housing......Page 339
4 The Appraisal Method: Transforming Standards Back into Data that Travels......Page 343
5 Conclusion......Page 348
References......Page 351
1 Introduction......Page 353
2 DALYs as Global Metrics: The World Bank and Economic Triage......Page 356
3 Health System Data and Political Triage: Primary Health Care at WHO......Page 362
4 Missing and Alternative Numbers: The Low Visibility of DALY-Based Triage......Page 364
5 Conclusion......Page 368
References......Page 370
1 Introduction......Page 372
2 Here, See......Page 374
3 See It, See It Not......Page 376
4 In the Blink of an Eye......Page 379
5 Is There Anything to See? Is There Anyone to See it?......Page 383
6 Conclusion: Varieties of Data and Journeys of Art......Page 385
7 Coda......Page 386
References......Page 387
Part VII: Afterword......Page 390
Afterword: Data in Transit......Page 391
References......Page 398
Visual Metaphors: Howardena Pindell, Video Drawings, 1975......Page 400
Index......Page 404