Surfacing the Academic Long Tail (SALT)


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  • Relatively plain sailing ‘til now – followed the guidelines of Dave Pattern to extract the data and create the appropriate algorithms for the recommenderThis is pulling from the API (which is not public yet, but will be)
  • Building on the work of MOSAIC, the SALT project will focus on a different use case where the barriers encountered by MOSAIC can be overcome. Academic researchers in the humanities make up a vast proportion of the institutional library OPAC usage. Much of the work of these researchers is monograph-based, and recent findings from Mimas and RIN indicate that a high level of postgraduate research centres on the use of unique or rare items held across the UK. These are the collections that make up the ‘Long Tail’ of UK research library collections. SALT will test the ‘long tail’ hypothesis in relation to advanced academic users of long tail collections held in UK research libraries which hold some of the richest heritage collections in the world. We will investigate how issues of relevance and frequency of borrowing might shift within the particular use case of humanities research, where low level of borrowing of rare or niche items may not necessarily equate with lower relevance to end users whose search behaviour is typically centrifugal and exploratory. We will look at the relationship between key critical texts and commonly read humanities secondary or primary research monographs that might occupy the ‘head’ of frequently borrowed items and follow activity trails to explore the relevancy of long tail, lesser borrowed, lesser known niche items.
  • Surfacing the Academic Long Tail (SALT)

    1. 1. SALT <br />Surfacing the Academic Long Tail <br />Joy Palmer, Mimas #jiscsalt<br /><br />
    2. 2. Hypothesis…Library circulation activity data can be used to support humanities research by surfacing underused ‘long tail’ library materials through search<br />
    3. 3. And also… how sustainable would an API-based national shared service be?Can such a service support users and also library workflows such as collections management?RLUK, M25, Leeds University, Cambridge University, Sussex University.<br />
    4. 4. Mimas delivers several key JISC national library & bibliographic services<br />Copac <br />Archives Hub<br />Zetoc <br />Journals Usage Stats Portal <br />
    5. 5. --Aggregation of 50+ research & specialist libraries--50 million records +--1 million search sessions per month--Primary use case – locating long tail materials<br />
    6. 6. --John Rylands University Library: --1.3 million bib records--600,000 search sessions per month--23% of records unique (cross checked against WorldCat)--40,000 students10 years of circulation data<br />
    7. 7. Why aren’t we there yet?<br />
    8. 8. Building on JISC MOSAIC<br />(a.k.a: going for the low hanging fruit)<br /><ul><li>Different use case (simpler)
    9. 9. Means barriers over extracting right data are lower
    10. 10. Lessens concerns over data privacy
    11. 11. Better odds for buy-in?</li></li></ul><li>In our contexts we need to articulate<br />user demand <br />benefits<br />value<br />sustainability<br />Where’s the BUSINESS CASE?<br />
    12. 12. arts & humanities researchers borrow books…<br />
    13. 13. market research reveals these users as…<br />Centrifugal searchers<br />‘Berry-pickers’ from various trails<br />Quite isolated and prone to pit-falls<br />
    14. 14. And increasingly they just don’t ask librarians…They ask their tutors and each other where to look…<br />
    15. 15. Researchers are suspicious about UGC, especially ratings & reviews, but….<br />they could see the immediate benefit of‘tacit’ recommender functions….<br />
    16. 16.
    17. 17.
    18. 18.
    19. 19. What if?<br />this represented a national aggregation of data gathered from the usage activity of these researchers, collected as they worked with a national aggregation of unique or rare research collections?<br />
    20. 20. In humanities research it’s<br />all the way<br />
    21. 21. What can this mean?<br />Surfacing and increasing usage of hidden collections ( & demonstrating value)<br />Providing new routes to discovery based on use and disciplinary contexts (not traditional classification).<br />Powering ‘centrifugal searching’ and discovery through serendipity<br />Enabling new, original research – academic excellence…<br />
    22. 22.
    23. 23. Next steps…<br />Implement in JRUL OPAC<br />Test hypothesis with academic users<br />Share lessons & consider possible next steps with additional contributors<br />Collect feedback from collections managers – useful for collections development & assessment?<br />Analyse issues for sustainability<br />
    24. 24. <ul><li>Target academic researchers looking for long tail items
    25. 25. Examine relationship between relevance and frequency of borrowing
    26. 26. Does frequency of borrowing correlate to increased relevancy?
    27. 27. How should it look?</li></li></ul><li>Relation between key critical texts at the nose<br />And the other stuff here <br />
    28. 28. Can we make the data work harder to solve other shared problems?<br />
    29. 29. Issues for sustainability<br />Is there a clear-cut case for a national shared service here?<br />Data model: <br />data out = easy<br />data in = not so much<br />Licensing & Attribution: collective ownership of a collective pot?<br />Is proof of our hypothesis key to sustainability?<br />
    30. 30. A virtuous circle<br />Thanks for listening<br />