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The Dataverse Network: An Infrastructure for Data Sharing Gary King - PowerPoint PPT Presentation

Jan 04, 2024 •363 likes •1.89k views

The Dataverse Network: An Infrastructure for Data Sharing Gary King Institute for Quantitative Social Science Harvard University (8/14/08 talk at UseR! 2008, Technische Universit at, Dortmund, Germany) (8/14/08 talk at UseR! 2008

  1. What About a Centralized Data Access Solution? Highly desirable when feasible Works great in astronomy, etc., when data formats are universal, goals are common, and agreements are in place Impossible when data are heterogeneous in format, origin, size, effort needed to collect or analyze, IRB access rules, etc. Why don’t researchers put data in public archives? The Archive gets the credit Upon questioning: they want credit, control, and visibility (So why don’t they worry about print publishers getting all the credit? Gary King (Harvard) Dataverse Network 4 / 21

  2. What About a Centralized Data Access Solution? Highly desirable when feasible Works great in astronomy, etc., when data formats are universal, goals are common, and agreements are in place Impossible when data are heterogeneous in format, origin, size, effort needed to collect or analyze, IRB access rules, etc. Why don’t researchers put data in public archives? The Archive gets the credit Upon questioning: they want credit, control, and visibility (So why don’t they worry about print publishers getting all the credit? Lack of data citations!) Gary King (Harvard) Dataverse Network 4 / 21

  3. What About a Centralized Data Access Solution? Highly desirable when feasible Works great in astronomy, etc., when data formats are universal, goals are common, and agreements are in place Impossible when data are heterogeneous in format, origin, size, effort needed to collect or analyze, IRB access rules, etc. Why don’t researchers put data in public archives? The Archive gets the credit Upon questioning: they want credit, control, and visibility (So why don’t they worry about print publishers getting all the credit? Lack of data citations!) We propose: technological solutions to these political problems Gary King (Harvard) Dataverse Network 4 / 21

  4. Requirements for Effective Data Sharing Infrastructure Gary King (Harvard) Dataverse Network 5 / 21

  5. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Gary King (Harvard) Dataverse Network 5 / 21

  6. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Gary King (Harvard) Dataverse Network 5 / 21

  7. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Gary King (Harvard) Dataverse Network 5 / 21

  8. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Gary King (Harvard) Dataverse Network 5 / 21

  9. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Gary King (Harvard) Dataverse Network 5 / 21

  10. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted Gary King (Harvard) Dataverse Network 5 / 21

  11. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, Gary King (Harvard) Dataverse Network 5 / 21

  12. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, Gary King (Harvard) Dataverse Network 5 / 21

  13. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Gary King (Harvard) Dataverse Network 5 / 21

  14. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Gary King (Harvard) Dataverse Network 5 / 21

  15. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Legal Protection: Gary King (Harvard) Dataverse Network 5 / 21

  16. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Legal Protection: Journals have liability protection for print; none for data Gary King (Harvard) Dataverse Network 5 / 21

  17. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Legal Protection: Journals have liability protection for print; none for data In the U.S., if you put data on the web without IRB approval, you are violating federal regulations Gary King (Harvard) Dataverse Network 5 / 21

  18. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Legal Protection: Journals have liability protection for print; none for data In the U.S., if you put data on the web without IRB approval, you are violating federal regulations (IRB approval must be for data distribution, not merely for the study) Gary King (Harvard) Dataverse Network 5 / 21

  19. Requirements for Effective Data Sharing Infrastructure Recognition, for authors, journals, etc. in (1) citations to data, (2) citations to associated articles, and (3) visibility on the web. Public Distribution, without permission from the author Authorization: fulfill requirements the author originally met Validation: check that data exists, without authorization Persistence Decades from now. . . . Verification: data remains unchanged, even if converted from SPSS to Stata to R, from a PC to a Mac to Linux, and from 8 inch magnetic tape to 5.25 inch floppies to a DVD. Ease of Use Neither editors nor authors employ professional archivists Legal Protection: Journals have liability protection for print; none for data In the U.S., if you put data on the web without IRB approval, you are violating federal regulations (IRB approval must be for data distribution, not merely for the study) Solution must not require lawyers (we’ve automated the IRB) Gary King (Harvard) Dataverse Network 5 / 21

  20. Rules for Citing Printed Matter Gary King (Harvard) Dataverse Network 6 / 21

  21. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Gary King (Harvard) Dataverse Network 6 / 21

  22. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. First author (last name first) Gary King (Harvard) Dataverse Network 6 / 21

  23. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Second author Gary King (Harvard) Dataverse Network 6 / 21

  24. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Third author Gary King (Harvard) Dataverse Network 6 / 21

  25. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Year Gary King (Harvard) Dataverse Network 6 / 21

  26. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Article title Gary King (Harvard) Dataverse Network 6 / 21

  27. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Journal (no longer exists) Gary King (Harvard) Dataverse Network 6 / 21

  28. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Volume number Gary King (Harvard) Dataverse Network 6 / 21

  29. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Issue number Gary King (Harvard) Dataverse Network 6 / 21

  30. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Season Gary King (Harvard) Dataverse Network 6 / 21

  31. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Pages Gary King (Harvard) Dataverse Network 6 / 21

  32. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Special formatting codes Gary King (Harvard) Dataverse Network 6 / 21

  33. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Special indentation Gary King (Harvard) Dataverse Network 6 / 21

  34. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Citations: rule-based, precise, redundant Gary King (Harvard) Dataverse Network 6 / 21

  35. Rules for Citing Printed Matter Kim, Jae-On, Norman Nie, and Sidney Verba. 1977. “A Note on Factor Analyzing Dichotomous Variables: The Case of Political Participation,” Political Methodology, Vol. 4: No. 2 (Spring): Pp. 39–62. Print Citations Work: authors don’t think publishers get all the credit; cited articles can be found; copyeditors don’t need to see the original to know it exists; the link from citation to print persists Gary King (Harvard) Dataverse Network 6 / 21

  36. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== Gary King (Harvard) Dataverse Network 7 / 21

  37. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author Gary King (Harvard) Dataverse Network 7 / 21

  38. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author 2 Year Gary King (Harvard) Dataverse Network 7 / 21

  39. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author 2 Year 3 Title Gary King (Harvard) Dataverse Network 7 / 21

  40. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author 2 Year 3 Title 4 Unique Global Identifier: will work after URLs stop working Gary King (Harvard) Dataverse Network 7 / 21

  41. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author 2 Year 3 Title 4 Unique Global Identifier: will work after URLs stop working 5 Linked to a Bridge Service (presently a URL: http://id.thedata.org/hdl%3A1902.4%2F00754 ) Gary King (Harvard) Dataverse Network 7 / 21

  42. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== 1 Author 2 Year 3 Title 4 Unique Global Identifier: will work after URLs stop working 5 Linked to a Bridge Service (presently a URL: http://id.thedata.org/hdl%3A1902.4%2F00754 ) 6 Universal Numeric Fingerprint (UNF) Gary King (Harvard) Dataverse Network 7 / 21

  43. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== Annals of Applied Statistics [Distributor]; 1 Author 2 Year 3 Title 4 Unique Global Identifier: will work after URLs stop working 5 Linked to a Bridge Service (presently a URL: http://id.thedata.org/hdl%3A1902.4%2F00754 ) 6 Universal Numeric Fingerprint (UNF) 7 Standard rules for adding citation elements Gary King (Harvard) Dataverse Network 7 / 21

  44. A New Citation Standard for Numeric Data Sidney Verba, 1998, “Political Participation Data”, hdl:1902.4/00754, UNF:3:6:ZNQRI14053UZq389x0Bffg?== Annals of Applied Statistics [Distributor]; NORC [Producer]. 1 Author 2 Year 3 Title 4 Unique Global Identifier: will work after URLs stop working 5 Linked to a Bridge Service (presently a URL: http://id.thedata.org/hdl%3A1902.4%2F00754 ) 6 Universal Numeric Fingerprint (UNF) 7 Standard rules for adding citation elements Gary King (Harvard) Dataverse Network 7 / 21

  45. Data to Universal Numeric Fingerprints Gary King (Harvard) Dataverse Network 8 / 21

  46. Data to Universal Numeric Fingerprints 1 4 4 21 121   · · · 1 2 2 91 212 · · ·    1 9 2 72 104  · · ·     0 2 2 2 321 · · ·     1 6 2 12 204 · · ·     1 9 4 52 311 · · ·     0 3 2 23 92 · · ·     0 2 5 91 212 · · ·     0 5 8 91 91 · · ·     1 9 1 72 104  · · ·    . . . . . ... . . . . .   . . . . .   1 2 2 91 212 · · · Gary King (Harvard) Dataverse Network 8 / 21

  47. Data to Universal Numeric Fingerprints 1 4 4 21 121   · · · 1 2 2 91 212 · · ·    1 9 2 72 104  · · ·     0 2 2 2 321 · · ·     1 6 2 12 204 · · ·     1 9 4 52 311 · · ·   = ⇒ ZNQRI14053UZq389x0Bffg?==   0 3 2 23 92 · · ·     0 2 5 91 212 · · ·     0 5 8 91 91 · · ·     1 9 1 72 104  · · ·    . . . . . ... . . . . .   . . . . .   1 2 2 91 212 · · · Gary King (Harvard) Dataverse Network 8 / 21

  48. Advantages of UNFs Gary King (Harvard) Dataverse Network 9 / 21

  49. Advantages of UNFs UNF is calculated from the content not the file: . Gary King (Harvard) Dataverse Network 9 / 21

  50. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, . Gary King (Harvard) Dataverse Network 9 / 21

  51. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, . Gary King (Harvard) Dataverse Network 9 / 21

  52. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, . Gary King (Harvard) Dataverse Network 9 / 21

  53. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, . Gary King (Harvard) Dataverse Network 9 / 21

  54. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, . Gary King (Harvard) Dataverse Network 9 / 21

  55. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Gary King (Harvard) Dataverse Network 9 / 21

  56. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Gary King (Harvard) Dataverse Network 9 / 21

  57. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties Gary King (Harvard) Dataverse Network 9 / 21

  58. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties UNFs convey no information about data content Gary King (Harvard) Dataverse Network 9 / 21

  59. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties UNFs convey no information about data content OK to distribute for highly sensitive, confidential, or proprietary data Gary King (Harvard) Dataverse Network 9 / 21

  60. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties UNFs convey no information about data content OK to distribute for highly sensitive, confidential, or proprietary data Copyeditor can validate data’s existence even without authorization Gary King (Harvard) Dataverse Network 9 / 21

  61. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties UNFs convey no information about data content OK to distribute for highly sensitive, confidential, or proprietary data Copyeditor can validate data’s existence even without authorization The citation refers to one specific data set that can’t ever be altered, even if journal doesn’t keep a copy Gary King (Harvard) Dataverse Network 9 / 21

  62. Advantages of UNFs UNF is calculated from the content not the file: Its the Same UNF regardless of changes in computer hardware, storage medium, operating system, statistical software, database, or spreadsheet software. Cryptographic technology: any change in data content changes the UNF. (cannot tinker after the fact!) Noninvertible properties UNFs convey no information about data content OK to distribute for highly sensitive, confidential, or proprietary data Copyeditor can validate data’s existence even without authorization The citation refers to one specific data set that can’t ever be altered, even if journal doesn’t keep a copy Future researchers can quickly check that they have the same data as used by the author: merely recalculate the UNF Gary King (Harvard) Dataverse Network 9 / 21

  63. Web 2.0 Terminology Gary King (Harvard) Dataverse Network 10 / 21

  64. Web 2.0 Terminology Software: find CD, install locally, Gary King (Harvard) Dataverse Network 10 / 21

  65. Web 2.0 Terminology Software: find CD, install locally, hit next, Gary King (Harvard) Dataverse Network 10 / 21

  66. Web 2.0 Terminology Software: find CD, install locally, hit next, hit next, Gary King (Harvard) Dataverse Network 10 / 21

  67. Web 2.0 Terminology Software: find CD, install locally, hit next, hit next, hit next. . . Gary King (Harvard) Dataverse Network 10 / 21

  68. Web 2.0 Terminology Software: find CD, install locally, hit next, hit next, hit next. . . Web application software: no installation; load web browser and run (Dataverse Network Software) Gary King (Harvard) Dataverse Network 10 / 21

  69. Web 2.0 Terminology Software: find CD, install locally, hit next, hit next, hit next. . . Web application software: no installation; load web browser and run (Dataverse Network Software) Host: The computers where the web application software runs (universities, archives, libraries) Gary King (Harvard) Dataverse Network 10 / 21

  70. Web 2.0 Terminology Software: find CD, install locally, hit next, hit next, hit next. . . Web application software: no installation; load web browser and run (Dataverse Network Software) Host: The computers where the web application software runs (universities, archives, libraries) Virtual host: Where the web application software seems to run, but does not (web sites of: authors, journals, granting agencies, research centers, universities, scholarly organizations, etc.) Gary King (Harvard) Dataverse Network 10 / 21

  71. http://www.peterson.com http://dvn.iq.harvard.edu/peterson Dataverse po wered by the Network ™ Pr oject Your web site Your dataverse branded as your web site but served by the Dataverse Network, therefore re- quiring no local installation and providing an enormous array of services Gary King (Harvard) Dataverse Network 11 / 21

  72. Dataverse po wered by the Network ™ Pr oject Gary King (Harvard) Dataverse Network 12 / 21

  73. Dataverse po wered by the Network ™ Pr oject Gary King (Harvard) Dataverse Network 13 / 21

  74. po wered by the Dataverse Network ™ Pr oject Gary King (Harvard) Dataverse Network 14 / 21

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