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Dark data : why what you don't know matters /
"Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant t...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton :
Princeton University Press,
[2020]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Part 1: Dark Data: Their Origins and Consequences; Chapter 1: Dark Data: What We Don't See Shapes Our World; The Ghost of Data; So You Think You Have All the Data?; Nothing Happened, So We Ignored It; The Power of Dark Data; All around Us; Chapter 2: Discovering Dark Data: What We Collect and What We Don't; Dark Data on All Sides; Data Exhaust, Selection, and Self-Selection; From the Few to the Many; Experimental Data; Beware Human Frailties; Chapter 3: Definitions and Dark Data: What Do You Want to Know?; Different Definitions and Measuring the Wrong Thing
- You Can't Measure EverythingScreening; Selection on the Basis of Past Performance; Chapter 4: Unintentional Dark Data: Saying One Thing, Doing Another; The Big Picture; Summarizing; Human Error; Instrument Limitations; Linking Data Sets; Chapter 5: Strategic Dark Data: Gaming, Feedback, and Information Asymmetry; Gaming; Feedback; Information Asymmetry; Adverse Selection and Algorithms; Chapter 6: Intentional Dark Data: Fraud and Deception; Fraud; Identity Theft and Internet Fraud; Personal Financial Fraud; Financial Market Fraud and Insider Trading; Insurance Fraud; And More
- Chapter 7: Science and Dark Data: The Nature of DiscoveryThe Nature of Science; If Only I'd Known That; Tripping over Dark Data; Dark Data and the Big Picture; Hiding the Facts; Retraction; Provenance and Trustworthiness: Who Told You That?; Part II: Illuminating and Using Dark Data; Chapter 8: Dealing with Dark Data: Shining a Light; Hope!; Linking Observed and Missing Data; Identifying the Missing Data Mechanism; Working with the Data We Have; Going Beyond the Data: What If You Die First?; Going Beyond the Data: Imputation; Iteration; Wrong Number!
- Chapter 9: Benefiting from Dark Data: Reframing the QuestionHiding Data; Hiding Data from Ourselves: Randomized Controlled Trials; What Might Have Been; Replicated Data; Imaginary Data: The Bayesian Prior; Privacy and Confidentiality Preservation; Collecting Data in the Dark; Chapter 10: Classifying Dark Data: A Route through the Maze; A Taxonomy of Dark Data; Illumination; Notes; Index.