1. Evolution of Data Documentation Providing Social Science Data Services Jim Jacobs, 2008

2. Evolution of Data Documentation

3. In the beginning… …was the codebook.

4. …early digital codebooks… Codebook listed to tape

5. …early digital codebooks… OSIRIS Dictionaries

6. …early digital codebooks… SPSS (and SAS) code

7. …early digital codebooks… PDFs

8. What do early digital codebooks have in common? 1. Tied to a particular physical layout of a data file VARIABLE 6 OPINION OF COUNTRY OVERALL DECK 1/35

9. What do early digital codebooks have in common? 1. Tied to a particular physical layout of a data file 2. Each uses its own special syntax. VARIABLE 6 OPINION OF COUNTRY OVERALL DECK 1/35 D HUFAMINC 2 39 CITY $ 77-94

10. What do early digital codebooks have in common? 3. Some included information intended for human consumption. Q1. THINKING ABOUT THE COUNTRY OVERALL, DO YOU THINK THINGS IN THE U.S. ARE GENERALLY GOING IN THE RIGHT DIRECTION, OR DO YOU FEEL THINGS ARE SERIOUSLY OFF ON THE WRONG TRACK? VALUE LABEL VALUE N OF CASES ----------- ----- ---------- RIGHT DIRECTION 1 223 WRONG TRACK 2 237 NO OPINION 8 48 NOT APPLICABLE* 9 500 ------- TOTAL 1008 *NOT FORM A

11. PDF Osiris dictionary SPSS cards CBLT Book Osiris SPSS Problems of early digital codebooks (part 1)

12. PDF Osiris dictionary SPSS cards CBLT Book Osiris SPSS (user has to re-create information in order to re-use information) Machine “readable” but not Machine “actionable”

13. XML helps solve the problem XML is not tied to any single piece of software. XML is designed to be easily parsed by computer. XML is (to some extent) self-documenting or self-descriptive. XML can include information intended both for humans and machines. XML is non-proprietary, open, flexible.

14. XML helps solve the problem Many tools exist to read/convert XML. (Java, javascript, perl, PHP, etc.) XSL and XSLT were created explicitly for converting XML. With them XML can be converted to HTML, PDF, other XML, etc. XML is highly structured so it can be predictably converted.

15. DDI 1 and 2 1.0 DOCUMENT DESCRIPTION 2.0 STUDY DESCRIPTION 3.0 DATA FILES DESCRIPTION 4.0 VARIABLE DESCRIPTION 5.0 OTHER STUDY-RELATED MATERIALS Built to emulate early code BOOKS and digital Codebooks…

16. Problems of early digital codebooks (part 2) Static, inflexible. Meant to document the end point of research -- Views research as linear. Hard to re-use the information for new research.

17. Problems of DDI 1 and 2 Emulated the Code Book Not flexible enough We could do so much more…

18. Three Stages of Technological Change Type of ChangeCharacterized by ModernizationDoing what we’ve always done, but using technology to do more and to increase efficiency InnovationDoing things we’ve wanted to do, but could not do without the technology TransformationDoing things that we didn’t imagine until technology made it possible.

19. Three Stages of Technological Change Type of ChangeCharacterized by Early digital codebooks Doing what we’ve always done, but using technology to do more and to increase efficiency DDI 1 and 2Doing things we’ve wanted to do, but could not do without the technology DDI 3Doing things that we didn’t imagine until technology made it possible.

20. Three Stages of Technological Change Type of ChangeCharacterized by Early digital codebooks Making codebooks machine readable DDI 1 and 2Making codebooks re-usable, even machine actionable… DDI 3Re-thinking “documentation” Re-thinking the research process

21. DDI 1 and 2 Document Description Study Description Data Files Description Variable Description Other Study-Related Materials

22. DDI 1 and 2 Document Description Study Description Data Files Description Variable Description Other Study-Related Materials Study Concept Data Collection Data Processing Data Distribution Data Archiving Data Discovery Data Analysis Repurposing DDI 3

23. Life Cycle of Research, Data, Documentation

24. A modular approach Study Unit - Research question - Funding - Concepts - Background research

25. A modular approach Study Unit Data Collection - Instrument - Data collection process - Questionnaire

26. A modular approach Study Unit Data Collection Logical Product - Intellectual content of data - Relationship to questions and concepts - Relationship to processing (recodes, weighting, derivations, imputations)

27. A modular approach Study Unit Data Collection Logical Product Physical Data Product - Describes the structure (microdata, tabular, aggregate, Ncube…) (e.g., STF 1A)

28. A modular approach Study Unit Data Collection Logical Product Physical Data Product Physical instance - Each describes a single data file (e.g., STF1 A by state...each state is an instance)

29. A modular approach Study Unit Data Collection Logical Product Physical Data Product Physical instance “Instance” -An instance module “wraps” the other modules. Like a table of contents to a group of studies and files and modules it brings everything together.

30. A modular approach Study Unit Data Collection Logical Product Physical Data Product Physical instance “Instance” Archive - Each archive can add its own local information with an archive module.

31. A modular approach Study Unit Data Collection Logical Product Physical Data Product Physical instance “Instance” Archive

32. A modular approach (but wait… there’s more!) Group module - Describe concepts, questions, and variables that occur in several studies. - Describe a series (e.g., CBP, CPS, Eurobarometer) - Describe a collection of studies (not a series) and identify the common comparable concepts, questions and variables.

33. A modular approach (but wait… there’s more!) Group module Comparative module -The Comparative module contains information for comparing concepts, questions, and variables between or among Study Units that have been housed in a Group.

34. A modular approach (but wait… there’s more!) Group module Comparative module Conceptual components module - Describe concepts and their relationships as concept groups. - Use known vocabularies and can indicate the level of similarity between two concepts by describing the extent of difference.

35. A modular approach Study Unit Data Collection Logical Product Physical Data Product Physical instance “Instance” Archive Group module Comparative module Conceptual components module