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Conclusions Are Asserted 164 Step 9. Conclusions Are Examined and Subjected to Validation 16412. Failure Background 167 Failure Is Common 168 Failed Standards 169 Complexity 172 When Does Complexity Help? 173 When Redundancy Fails 174 Save Money; Don’t Protect Harmless Information 176 After Failure 177 Use Case: Cancer Biomedical Informatics Grid, a Bridge Too Far 17813. Legalities Background 183 Responsibility for the Accuracy and Legitimacy of Contained Data 184 Rights to Create, Use, and Share the Resource 185 Copyright and Patent Infringements Incurred by Using Standards 187 Protections for Individuals 188 Consent 190 Unconsented Data 194 Good Policies Are a Good Policy 197 Use Case: The Havasupai Story 19814. Societal Issues Background 201 How Big Data Is Perceived 201 The Necessity of Data Sharing, Even When It Seems Irrelevant 204 Reducing Costs and Increasing Productivity with Big Data 208 Public Mistrust 210 Saving Us from Ourselves 211 Hubris and Hyperbole 21315. The Future Background 217 Last Words 226Glossary 229References 247Index 257 In the next few days, I'll be posting short excerpts from the book, along with commentary. Best, Jules Bermankey words: big data, heterogeneous data, complex datasets, Jules J. Berman, Ph.D., M.D., immutability, introspection, identifiers, de-identification, deidentification, confidentiality, privacy, massive data, lotsa data
Source: Specified Life - Category: Pathologists Source Type: blogs