Mosaic Wolverhampton

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Short pre-recorded presentation for the JISC MOSAIC Project event in Wolverhampton.

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  • 2009 value is predicted
  • 2009 value is predicted
  • Mosaic Wolverhampton

    1. 1. Library & Service Perspective The University of Huddersfield Experience Dave Pattern Library Systems Manager University of Huddersfield, UK [email_address]
    2. 2. preamble <ul><li>These slides are available at… </li></ul><ul><ul><li>http://slideshare.net/daveyp </li></ul></ul><ul><li>Please remix and reuse these slides! </li></ul><ul><ul><li>creativecommons.org/licenses/by-nc-sa/3.0 </li></ul></ul><ul><li>Have you remembered to switch your phone on? </li></ul><ul><ul><li>please feel free take photos, record audio, live blog, tweet (@daveyp, #jiscmosaic), etc </li></ul></ul>
    3. 3. University of Huddersfield Library <ul><li>Medium sized UK University </li></ul><ul><ul><li>20,000 students and 2,000 staff </li></ul></ul><ul><ul><li>Library holds over 240,000 books </li></ul></ul><ul><li>Current LMS/ILS Horizon installed in 1996 </li></ul><ul><ul><li>over 3 million borrowing (“circ”) transactions stored in the DB </li></ul></ul>
    4. 4. library catalogue
    5. 5. <ul><li>General unhappiness with vendor product </li></ul><ul><li>“In-house” enhancements to the existing OPAC… </li></ul><ul><ul><li>user suggestions from surveys </li></ul></ul><ul><ul><li>“Web 2.0” inspired features </li></ul></ul><ul><ul><li>borrowing good ideas from other web sites </li></ul></ul><ul><ul><li>new features launched with no/low publicity </li></ul></ul><ul><ul><li>“perpetual beta” </li></ul></ul>background summer 2005
    6. 6. suggestions based on circ data “people who borrowed this…”
    7. 7. suggestions based on circ data “people who borrowed this…”
    8. 8. borrowing profile average loans per month average number of book loans per month
    9. 9. feature usage “people who borrowed this…” average number of clicks per month on “people who borrowed this” suggestions
    10. 10. getting personal! suggestions for what to borrow next
    11. 11. building better new book lists course specific RSS feeds
    12. 12. building better new book lists course specific RSS feeds
    13. 13. the impact
    14. 14. the impact on borrowing range of stock borrowed per year number of unique titles (bib#) borrowed per calendar year (2009 figure is predicted) borrowing suggestions added to catalogue at start of 2006
    15. 15. the impact on borrowing average number of books borrowed average number of books borrowed per active borrower per calendar year (2009 predicted)
    16. 16. keyword cloud
    17. 17. catalogue keyword searches guided searches
    18. 18. catalogue keyword searches guided searches
    19. 19. catalogue keyword searches guided searches
    20. 20. catalogue keyword searches guided searches
    21. 21. usage data for staff
    22. 22. usage data for staff loans per academic school
    23. 23. usage data for staff loans per academic school
    24. 24. usage data for staff borrowing profile
    25. 25. open data
    26. 26. <ul><li>http://library.hud.ac.uk/usagedata/ </li></ul><ul><ul><li>prompted by the JISC Tile Project </li></ul></ul><ul><ul><li>aggregated usage data for 2 million circulation transactions, covering around 80,000 book titles </li></ul></ul><ul><ul><li>recommendation data for over 37,000 titles </li></ul></ul><ul><ul><li>simple XML format </li></ul></ul><ul><ul><li>Open Data Commons / CC0 licence </li></ul></ul>library usage data release “if you love something, set it free…”
    27. 27. <ul><li>Data released on 12th Dec 2008… </li></ul><ul><li>… 2 days later, Patrick Murray-John at University of Mary Washington converted the data into a semantic version (RDF) </li></ul><ul><ul><li>Patrick’s blog post at http://bit.ly/noJD </li></ul></ul><ul><ul><li>Talis podcast at http://bit.ly/z6yjF </li></ul></ul>library usage data release “if you love something, set it free…”
    28. 28. library usage data BA (Hons) Multimedia Design
    29. 31. non-usage
    30. 32. Non/Low-usage Project 2005 onwards
    31. 33. Non/Low-usage Project 2005 onwards
    32. 34. summary
    33. 35. final thoughts… <ul><li>Serendipity is helping change borrowing habits </li></ul><ul><li>Analysis of usage data allows greater insights in how our services are used (or not used!) </li></ul><ul><li>What benefits can be derived if we were all sharing our data? </li></ul>

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