1. Introduction page 1-14 -- 2. Providing structure to unstructured data page 15-52 --3. Identification, deidentification, and reidentification page 53-84 -- 4. Metadata, semantics, and triples page 85-96 -- 5.Classifications and ontologies page 97-136 -- 6. Introspection page 137-154 -- 7. Standards and data integration page 155-168 -- 8. Immutability and immortality page 169-184 -- 9. Assessing the adequacy of a big data resource page 185-206 -- 10. Measurement page 207-230

11. Indispensable tips for fast and simple big data analysis page 231-258 -- 12. Finding the clues in large collections of data page 259-276 -- 13. Using random numbers to knock your big data analytic problems down to size page 277-302 -- 14. Special considerations in big data analysis page 303-320 -- 15. Big data failures and how to avoid (some of) them page 321-350 -- 16. Data reanalysis: Much more important than analysis page 351-362 -- 17. Repurposing Big data page 363-372 -- 18. Data sharing and data security page 373-394 -- 19. Legalities page 395-418 -- 20. Societal issues page 419-444