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ICPSR Collection Development: Lessons Learned & Moving Forward

ICPSR Collection Development: Lessons Learned & Moving Forward. NDIIPP Partner Meeting Collecting Digital Content Going Forward: Lessons Learned and New Initiatives Washington, D.C. 10 July 2008. Overview.

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ICPSR Collection Development: Lessons Learned & Moving Forward

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  1. ICPSR Collection Development: Lessons Learned & Moving Forward NDIIPP Partner Meeting Collecting Digital Content Going Forward: Lessons Learned and New Initiatives Washington, D.C. 10 July 2008

  2. Overview • We have been successful at preserving important social science data for decades. • Yet, important social science data have been lost despite our best efforts. • We want to leverage the processes we developed in the Data-PASS project to avoid future loss.

  3. Social Science Data Preservation Successes

  4. ICPSR’s Data-PASS Efforts

  5. Collection Development Successes • High response rate (44%) • Most PIs indicated that they wanted to be “Good Citizens” and help. “This sounds like an exciting project.” “I hope your project is successful because I think that it is important.” “Good luck with the project.”

  6. Collection Development Successes • Many of the data we have received are important mid-sized collections, which might not otherwise be collected. • Around half of PIs still have access to data (49%)

  7. 57 Commitments to Deposit • “U.S.-Sweden Cooperative Science: Demographic Change in Nineteenth-Century Sweden” (Katherine Lynch, 1989, NSF #8912792) • “Studies of the Economic Impacts of Global Environmental Change” (Nordhaus, 1991, NSF #9024860) • “Social Support and Mental Health in a Black Community” (Dressler, 1980, NIH #1R01MH033943)

  8. Recent Acquisitions • “Massachusetts Occupational Survey: 1953-1954” (Kahl) • “Hurricane Andrew: Its Impact on Law and Social Control” (Akers) • “Attitudes Toward Women and Work” (Huber)

  9. Challenges • “Good Citizens” = high willingness but no time, money, or resources to submit to us.

  10. Challenges • A good number (24%) of PIs no longer could access their data.

  11. Archival Status/Availability of Identified Data (n=1,668)

  12. Data Discarded • “It was just too long ago, I generally keep data for something like 10 years beyond the last time I do something with them.” • “Data cards were destroyed long after several articles were published from them.” • “Destroyed, in accord with APA 5-year post-publication rule.” • Hardware Problems • “Some data were collected, but the data file was lost in a technical malfunction.” • “Electronic version was lost in a computer failure.”

  13. Destroyed for Confidentiality Reasons “The material…was considered sensitive data. Institutional review boards, and the state registry board from which some data were derived required us to promise to destroy the data after a certain period of time, which was done a year or so after publication.” “When the project ‘hibernated’ it was eventually decided to destroy the records in order to protect the identities of the subjects. I destroyed the records about a year ago when the building that housed them was torn down….Since I could no longer assure the confidentiality of the records, I destroyed them.” Acts of Nature “The data from the studies were on punched cards that were destroyed in a flood in the department in the early 80s.”

  14. Discarded or Lost in a Move • “The data were on punched cards, which probably were discarded (after a decade or more of non-use) in one of several office moves.” • “They were all published in journals, and when I moved from [University A] to [University B], I discarded them.” • “As I retired and gave up my office last year, I disposed of the data tape at that time. Unfortunately, I simply didn’t have the room to store these data sets at my house.” • “I lost track of them during several moves over the past 20 years.” • “I had the data (ASCII and SPSS system files) on old floppy discs and they seem to have been lost in a move.”

  15. Obsolescence “….a good deal of…data from the study were also entered (sometime in the late 1970s) onto the [University’s] mainframe in an early version of SPSS. I sincerely doubt that they are still accessible in electronic form—is there even such a thing as a mainframe any more?” “The data were backed up several times using different media, but eventually, the media became obsolete and I did not transfer it to newer technologies.” “Speech recordings stored on a LISP Machine…, an experimental computer which is long obsolete.”

  16. Simply Lost “This was a long time ago, university has changed its computer systems several times since. I am not able to locate the data.” “I really cannot recall. It was decades ago, and I subsequently moved to new positions in various universities. The records of the responses obtained are forever lost.” “I don’t honestly know where they would be now. For all I know, they are on a [University] server, but it has been literally years and years since the research was done, and my files are long gone.”

  17. Challenges • Some PIs have data, but data are in obsolete formats, without sufficient documentation, or require massive clean-up.

  18. Proprietary Formats of Data Not Archived And Also… .aiff, ArcView (ESRI), ARLEQUIN, ATLAS.ti, AVI, Binary (UWAR format), BMPD, Brainvoyager, Bvh, Coldfusion database, .DAT, Dbase, DICOM, Digital Voice Files, .DSS, E-PRIME, EQS, EVIEWS workfile, FLASH, FSL files, GAUSS, HTML, iMovie, JPG, JMP databases, MAXQDA, Molecular workbench database, MP3, MySQL, NEXUS, .NSP, NVivo, Paradox data files, PHYLIP, QDS, REFLEX, Rich Text Format, SALT transcription format, Sawtooth WinCati, Sequencer, SQL, Statistica, Statmost, STRUCTURE, Systat, Teleform, Trimble Pathfinder spatial coordinate raw data files, TSP, Viso, WordPerfect. Last updated 8/17/2007

  19. Storage Media of Data Not Archived And Also… cassette tape, DAT tape, digital video tape, McBee cards, minidisks, mini digital video tapes, network drive, QIC-80 tape, reel-to-reel audio tape, reel-to-reel videotapes, Sun UNIX network, 16mm film. Last updated 8/17/2007

  20. Collection Development Policy Update

  21. Specific Updates • Regular harvests of NIH & NSF awards. • Early, ongoing contact with PIs. • Outreach & Education. • Peer Network.

  22. Outreach & Education

  23. Peer Network

  24. Collection Development Policy Update

  25. Jared LyleICPSR(734)763.6075lyle@umich.edu

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