Annette Day and Hilary Davis; NCSU Libraries; Charleston Conference 2010

1. Collection Intelligence: Using data driven
decision-making in collection management
Annette Day
Hilary Davis
North Carolina State University Libraries
Charleston Conference
November 6, 2010

2. Today’s Presentation
 Using data to inform and articulate collections decisions
 NCSU Libraries’ projects
 Journal cancellation project
 Collections Views tool
 Return on investment for
journal backfiles

3. Maintaining a balance
 Articulate and explain our decisions
 Show our collection intelligence
Flickr: RayBanBro66

4. Data can help
 Data-informed collection management
 Types of Data
 Cost
 Use
 Formats
 Owned or Leased
 Citation and publication patterns
 Impact Factors
 Regional holdings
 Editorial activity
 Ways to use Data
 Show value/ROI
 Use is high and increasing
 Test assumptions about the collections
 Fit and alignment with campus
Flickr: quinn.anya

5. NCSU Context
 ~31,000 students
 ~8,000 faculty
 $10 million collection budget
 4 million volumes
 1 main library
 4 branch libraries
 Campus Strength Areas
 Engineering, Architecture, Agriculture, Science, Technology,
Veterinary Medicine
Flickr: rshannonsmith, ncsunewsdept, Angela De Marco

6. Collections Review 2009/2010

7. Collections Review Project 2009/2010
 15% cut in collections budget = $1.5 million
 Significant journal cuts
 1,112 journals proposed for cancelation
 Cost for each title
 Package bundle or piecemeal?
 Usage statistics
 Impact factor (where available)
 Publication and citation data
 Alternative access points

8. Gathering Campus Feedback
 Low barrier to entry to encourage feedback
 Created informational website
 Authenticated Webform
 Captured departmental affiliation and rank
 Sortable
 Saveable
 Downloadable
 Admin features

12. Feedback Received
 1,365 users  700 submitted feedback
 12,710 title rankings
 Lots of data; how to make sense of it all?!
 Weighted approach
 Minimize impact of ranking journals outside discipline/research
 Cost per use
 Additional data metrics

13. Processing the Feedback
 Weighted Ranking – college affiliation and journal subject
 Favored rankings most closely aligned with a user’s
research/teaching (weight of 1.0)
 Minimized tangential/unrelated rankings (weight of 0.1)
 Priority to “Must keep” rank (10 points)
 Multiplied ranking points by the association weight and the
total number of rankings, then summed
 Higher the number, the more campus wants to keep it

14. Ranking Patron's Department Patron's College % Match
Weighted
Ranking
Weighted
Ranking x
% Match
(Weighted
Ranking x
% Match)
x Total #
Rankings
Sum
((Weighted
Ranking x %
Match) x Total
# Rankings)
Can Cancel Entomology Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Agricultural & Life Sciences Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Poultry Science Agriculture and Life Sciences 0.5 1 0.5 7.5 621
Can Cancel Engineering Engineering 0.8 1 0.8 12 621
Can Cancel
Humanities & Social
Sciences Humanities & Social Sciences 0.1 1 0.1 1.5 621
Can Cancel
Physical & Mathematical
Sciences Physical & Mathematical Sciences 1 1 1 15 621
Can Cancel Chemistry
Physical and Mathematical
Sciences 1 1 1 15 621
Can Cancel Chemistry
Physical and Mathematical
Sciences 1 1 1 15 621
Can Cancel Mathematics
Physical and Mathematical
Sciences 1 1 1 15 621
keep if
possible Engineering Engineering 0.8 5 4 60 621
keep if
possible Veterinary Medicine Veterinary Medicine 0.5 5 2.5 37.5 621
Must keep Electrical Engineering Engineering 0.8 10 8 120 621
Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621
Must keep Physics Physical & Mathematical Sciences 1 10 10 150 621
Example: Astronomy Letters

15. Processing the Feedback – Other metrics
 Cost per use
 Other data points
 Use data
 Impact factor
 Publication and citation data
 Resulting Formula
 Sum of the following:
 Average of 2 most recent years of use data
 Number of cites
 (2 x Number of publications) x (impact factor +1)
 More weight to data points we valued highly and reflected journal’s
relevance

16. Journal Title Price 2007 Use
2008
Use
Impact
Factor
LJUR
Pubs
LJUR
Citations
Data
Metric
Cost per
Use
Weighted
Ranking
Environmental Progress $486.00 64 67 1 0 11 24.62 $7 165.2
Robotics and autonomous systems $1,841.00 107 200 0.633 3 12 34.41 $12 536
Computational intelligence $858.00 23 76 1.972 2 4 26.72 $17 536
Sensor Review $2,972.00 156 84 2.40 $25 109.9
Journal of environmental science and
health - part A $3,886.00 99 164 0.967 1 36 79.92 $30 625.3
Information Processing Letters $2,238.00 42 83 0.66 2 10 25.32 $36 378.9
Materials Science and Technology $2,180.00 57 55 0.713 0 0 1.92 $39 1086.4
Separation science and technology $8,678.00 56 172 1.048 0 28 62.01 $76 284.9
Circuits, Systems, and Signal
Processing $1,407.00 12 18 0.456 0 2 3.35 $94 369.9
Distributed and Parallel Databases $927.00 6 11 0.771 0 1 2.07 $109 71.4
Applied Artificial Intelligence $1,485.00 15 12 0.753 1 8 18.00 $110 347.4
Plastics, rubber and composites $1,489.00 11 10 0.431 0.30 $142 80.4
Acta Informatica $1,219.00 4 7 0.8 1 7 16.40 $222 1413.3
Cybernetics and Systems Analysis $3,368.00 8 16 0.24 $281 50.5
International Journal of Satellite
Communications and Networking $412.00 0 2 0.284 0.03 $412 254.8
Chemical Engineering Research and
Design $1,692.00 0 2 0.837 2 22 47.80 $1,692 151.2

17. Issues/Challenges
 What difficulties did we encounter?
 List of what we subscribe to and costs
 All data not available for every title
 Usage statistics
 Impact factor and publication/citation data
 Processing the data
 “Tune out” irrelevant rankings
 Imprecise weighting
 Data is instructive but not the final decision point
 Technical skills needed to create webform

18. Collection Views Database

19. Collection Views Database Project
 We needed to answer the
following questions:
 How do the NCSU Libraries‘
expenditures on resources support
the research and teaching needs of
diverse colleges and departments
at NCSU?
 What data exist that might help us
understand how our resource
expenditures look in terms of the
departments we serve?
Flickr: ncsunewsdept, egnowit

20. Data Types
 Library data
 Expenditure data
 Monographs (Quantity & Cost)
 Firm Order
 Approval Plan
 Serials (Cost)
 Databases (Cost)
 Subject Fund Codes
Examples:
• ENTO – Entomology
• GTEC – General Technology
• NATM – Atmospheric Sciences NRL*
• TDES – Textiles Design
Flickr: hemingway gyro

21. Data Types
 Academic Department Data
 NCSU Office of University Planning and Analysis
 Faculty Headcount
 Enrolled Student Headcount
 Graduate Students
 Undergraduate Students
 NCSU's Sponsored Programs & Regulatory
Compliance Services
 PhD Degrees Awarded
 Research Grant Income
Flickr: ncsunewsdept

22. Connecting the Data
 Map subject fund codes to departments
 Connect library expenditures and department demographics (e.g., $x
supports the Physics Dept)
 Present expenditure data and department data side-by-side
 No “right way” to map codes to departments
 A code could be applied to more than one department
 Expenditures associated with a code applies to departments in full
(no weighting/no splitting)
 Broad and narrow mappings

24. Collection Views Database
 An SQL database was created to store the data and the
mappings
 Only have to add new data – not rebuild relationships and
other data
 Flexible output options
 Web
 Custom queries
 Canned queries
 Data Portal

25. Data Portal

26. Outputs

27. Outputs

28. Outputs

29. Outputs

30. Quick Comparison Tool

31. Uses of Collection Views
 Distribution of collections budget/expenditures across
subject areas
 Is it what we expected?
 Is it in line with our knowledge of how specific
departments/disciplines use library resources?
 Cumulative impacts of collecting decisions over time
 Facilitates discussion on budget allocation
 Graphs and charts provide illustrations of impact

32. Issues/Challenges
 All depends on the mapping
 Considering adding weighted mappings
 Timely gathering of data
 Campus data not readily available
 SQL database programming skills
 Digital Library Initiatives

33. Journal Backfiles ROI

34. Journal Backfiles ROI Project
 Investment in online journal backfiles over many years
 Demonstrate value and impact of these purchases
 Usage statistics
 Fiscal effectiveness
 Non-traditional ROI approach
 Cumulative cost of backfiles compared to cumulative use
 Lower cost/use over time
Flickr: cambodia4kidsorg

35. Results!
$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
$7.00
2004 2005 2006 2007 2008 2009
cost/use
year
ROI - All backfiles: Cumulative cost over cumulative use
(first year of use includes one time price)
 Over 100 downloads daily (2008 and 2009 use data)
 Historical ROI is $1.07

36. How we calculated the metrics
 Data Sources
 Full text article downloads
 Cost data
 Every backfile purchased since 2003
 Initial purchase cost and annual fees
 Calculations
 Initial cost and annual fees carried over through years
 Cost divided by cumulative usage

37. One
Time
Price
2003
Use
Data
2003
Annual
Fee
2004
Use
Data
2004
Annual
Fee
2005
Use
Data
2005
Annual
Fee
2006
Use
Data
2006
Annual
Fee
2007
Use
Data
2007
Annual
Fee
2008
Use
Data
2008
Annual
Fee
2009
Use
Data
2009
$40,800 $800 $800 3145 $800 3253 $800 3918 $1,095 4697 $1,095 2871
Example
 RSC Archive
 Calculations
 ROI 2005
 Cumulative Cost/ Cumulative Use
 (One Time Price + Annual Fee 2004 + Annual Fee 2005)/Use data
2005
 no use available prior to 2005
 = $42,400/3145
 $13.48

38. Example
$0.00
$2.00
$4.00
$6.00
$8.00
$10.00
$12.00
$14.00
$16.00
2005 2006 2007 2008 2009
cost/use
ROI - RSC backfile: Cumulative cost over cumulative use
(first year of use includes one time price)
2005 = $13.48
2006 = $6.75
2007 = $4.27
2008 = $3.00
2009 = $2.58

39. Issues/Challenges
 Non-traditional ROI metric
 May need clarification
 Use data not always available from year of purchase
 Backfile use data is not always separate from current
journals

40. Final Thoughts
 Data is a powerful tool, but not the end-all, be-all!
 Moving Forward…..
 Continued use of data
 Build data skills competencies
 Tools
 Data manipulation and interpretation
 Data dashboard
 Expanded/Improved Tools
 Visualization

41. THANK YOU!
ANNETTE_DAY@NCSU.EDU
HILARY_DAVIS@NCSU.EDU