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OCLC’s iCAS Service as a Tool for Collection Managers

OCLC’s iCAS Service as a Tool for Collection Managers. Richard Ovenden Director of Collections, Edinburgh University Library. Outline. OCLC’s iCAS Service (Interactive Collection Analysis System) Pilot Project Findings Recommendations Personal View Summary. OCLC’s iCAS Service.

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OCLC’s iCAS Service as a Tool for Collection Managers

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  1. OCLC’s iCAS Service as a Tool for Collection Managers Richard OvendenDirector of Collections,Edinburgh University Library

  2. Outline • OCLC’s iCAS Service (Interactive Collection Analysis System) • Pilot Project • Findings • Recommendations • Personal View • Summary

  3. OCLC’s iCAS Service • OCLC Lacey Product Center • ACAS: Automated Collection Assessment and Analysis Services • Based on WLN Conspectus • Individual Collection analyses • Combined analyses with overlap and uniqueness measures • Emphasis on publication dates

  4. Pilot Project • Funded by CURL/RSLP • Partners: Hull, Imperial College London, Edinburgh, SOAS, Liverpool, Natural History Museum • Reported February 2002

  5. Process (1) • 2, 767, 669 book format bibliographic records submitted • De-dupe • Matched No Call Number items against identical records within CURL

  6. Process (2) • Match against WorldCat • This found an additional 570, 075 records with call numbers • Rejected 12, 802 ‘ill-formed’ call numbers • Total number of records analysed 2, 268, 225 (84%) • Only 16% of records could not be processed because they could not be allocated a call-number.

  7. Number/Percent of Records Analysed

  8. Group Analysis

  9. Findings (1) • Data Problems • mapping inconsistencies (SOAS mapping to Celtic/Chinese) • Classification • Hull had used wrong country subdivisions in some parts of the LC schedules • Matching against other utilities vs. local classification needs • Methodologies: COPAC • Loading variants Liverpool, Edinburgh

  10. Findings (2) • Conspectus • The sub-divisions perceived as weak in some areas (eg biology) • Need to update categories: Where is Artificial Intelligence? • Fit with collections not good. Vet Medicine in Agriculture? • Quality & relevance of data • Unique items? • Accuracy of Conspectus match at highly granular level

  11. Findings (3) • Automated matching routines are rough & ready • Special topics in Computer Science included two books 1800-1899 • Date breakdowns - currency indications • Based only on OPAC data • Subject groupings don’t fit collections –compare holdings by site library? • Brings out worst in Librarians • Brings out worst in Chief Librarians

  12. Recommendations • Realise limitations • Know your data (=know your collections) • Talk to your colleagues (cataloguers, systems, as well as collections specialists) • Record data decisions (eg CURL uploads) • Collections analysis not collection description (OPAC analysis …)

  13. Personal View • Labour saving • Objectivity? • Adjunct to other methods • People still matter • Resource sharing • Compare like with like? • Compare against regional centre? • Basis for funding collaborative collection management?

  14. Summary • ICAS has value • ICAS has limitations • ICAS costs money • ICAS has potential for UK libraries • It does exactly what it says on the tin

  15. FIN • richard.ovenden@ed.ac.uk

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