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NISO Webinar on Usage Data
An Overview of Recent Usage Data Research
John McDonaldLibraries, Claremont University
ConsortiumMay 13, 2009
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Increased Interest in Usage Data• Ability to measure actions
• Usage
• Citation
• Relationships between resources
• Ability to analyze large datasets
• Computational power
• Data provided directly to librarians
• Standards for data and distribution
• Ability to demonstrate return on investment
• Management data
• Collections data
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New Ways to Collect Usage Data
• ISI Citation Data
• COUNTER reports
• Publisher provided data
• Web server logs
• Proxy server logs
• OpenURL resolver logs
• Google Analytics
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Theoretical Analysis of Usage Data
• Bollen’s Centrality Measures
• Rosvall & Bergstrom’s Scientific Communication Maps
• Davis’ Open Access studies
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Citation and Usage Data MeasuresBollen, Van de Sompel, Hagberg, Chute (2009). A principal component analysis of 39 scientific impact measures. arXiv. Available: http://arxiv.org/PS_cache/arxiv/pdf/0902/0902.2183v1.pdf
• A study of 39 journal measures, both standard bibliographic measures derived from citation and other measures derived from usage.
• Outcomes included that citation and usage are distinctly different events and measures based on them do not correlate closely.
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Figure 2 from Bollen et. Al. (2009).Usage based
measures
Citation Based
Measures
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Illustration of Citation NetworksRosvall & Bergstrom (2008). Maps of random walks on complex networks reveal community structure. PNAS Available: http://octavia.zoology.washington.edu/publications/RosvallAndBergstrom08.pdf
• A scientific map of the citation relationships between 6000+ ISI-indexed journals.
• Outcomes indicate that many basic science fields have bidirectional relationships with other fields, while most applied fields have uni-directional relationships with the basic science fields.
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Figure 3 from Rosvall & Bergstrom
• bergstromReciprocal
citation relationship
Non-reciprocal citation
relationship
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Analysis of Open Access citationsDavis (2008). Author-choice open access publishing in the biological and medical literature: a citation analysis. arXiv. Available: http://arxiv.org/PS_cache/arxiv/pdf/0808/0808.2428v3.pdf
• A study of 11journals where open access status was assigned randomly to articles to determine the citation advantage for OA articles.
• Outcomes included that OA articles were not more likely to accumulate citations than paid access articles.
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Table S2 from Davis (2008)
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Evidence Based Analysis of Usage Data• Betty’s Google Analytics of Local
Content
• Grigson’s Analysis of eBook Models
• Kinman’s Use of Sparklines
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Analyzing Local Usage Data• Betty (2009). Assessing Homegrown Library Collections:
Using Google Analytics to Track Use of Screencasts and Flash-Based Learning Objects. Journal of Electronic Resources Librarianship. Volume 21:1, 75 – 92.
• A study utilizing Google Analytics to track the use of web-based tutorials for library instruction.
• Outcomes included information about the total hits to each tutorial, usage throughout a tutorial, connection speed, browser software components
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Betty’s Tutorials Usage Results• One tutorial had 23% of its hits recorded by an
unintended audience
• Possible action: Better marketing/description of the content
• High hits for beginning & end of tutorials
• Possible action: Shorten or revise content in areas being skipped
• Most users had necessary software to view files
• Possible action: None needed
• A significant minority of users had dial-up access
• Possible action: Produce multiple versions
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Evaluating eBook Usage Data• Grigson (2009). Evaluating Business Models for E-Books
Through Usage Data Analysis: A Case Study from the University of Westminster. Journal of Electronic Resources Librarianship. Volume 21:1, 62-74.
• A study comparing usage of ebook packages provided by vendors with different acquisitions models (simultaneous users v. annual usage)
• Outcomes resulted in a clearly preferred model focusing on annual usage to accommodate the high peaks of usage during academic semesters.
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Table 1 from Grigson’s eBookUsage
Indicates peak-use periods of high demand for portions of the collections
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Evaluating Ebook Usage Data
Kinman (2008). Putting the Trees Back in the Forest: E-Resource Usage Statistics and Library Assessment. ER&L, March 18-21, 2008, Atlanta, GA. https://smartech.gatech.edu/bitstream/1853/20665/1/forest_trees_kinman.pdf
A description of a 5 year study on library services and resource usage, including a novel application of Tufte’s Sparklines http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR
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Kinman’s Application of Sparklines
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Future Directions for Usage Data Analysis • Auditing or compliance with standards
• Non-text media (eBooks, podcasts, etc.)
• Non-text subjects (i.e. Museums, Art)
• More robust database analysis
• Development of user-centered statistical standards
• Develop standard measures and standard tests to help in evaluation