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Cluster 5: Leveraging usage data
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Use Case: Research guided by community usage
As a researcher, I want to find what is being used (read, annotated, bought by libraries, etc.) by the scholarly communities not only at my institution but at others, and to find sources used elsewhere but not by my community
Potential Demonstrations
A1. In institutional and/or consortial catalog discovery UI, return search results in order of usage rank, and allow filtering on usage-rank ranges
A2. In catalog UI, use heat-mapping within virtual shelves of selected clusterings of catalog items (by subject, uniform title, author's works, etc.) to visualize usage rank
A3. In catalog UI, allow users to see raw component scores of scaled usage rank
A4. In catalog UI, have feature for exporting result sets in preferred format (CSV, JSON, XML, etc.)
A5. In consortial catalog UI, have feature to allow viewing comparative usage data across institutions
Implementation Notes
Data Sources Needed
- MARC bibliographic and holdings records
- Usage data (expressed as a scaled score) and including whichever of the following might be available at the local institution
- Circulation data (checkouts, checkins, renewals, recalls), transaction patrons described by status category (faculty, grad student, undergrad, etc.)
- Course reserves data
- Course text data
- Acquisitions data (how many libraries acquired the resource)
Engineering Work
A1, A2 and A3 prototyped at stacklife.harvard.edu
Each institution would choose for its scaled score implementation its own data components and weighting and aggregation algorithms
Use Case: Be guided in collection building by usage
As a librarian, I would like help building my collection by seeing what is being used by students and faculty, and what's being used at other universities
Potential Demonstrations
A1. In institutional and/or consortial tool's UI, return search results organized by subject class and sub-class and scaled usage score
Implementation Notes
Data Sources Needed
- MARC bibliographic and holdings records
- Usage data (expressed as a scaled score) and including whichever of the following might be available at the local institution
- Circulation data (checkouts, checkins, renewals, recalls), transaction patrons described by status category (faculty, grad student, undergrad, etc.)
- Course reserves data
- Course text data
- Acquisitions data (how many libraries acquired the resource)
- LoC classification outline (650,000 records)
Engineering Work
Prototyped at http://hlslwebtest.law.harvard.edu/analytics-dash/sketches/final/
LoC classification classes and sub-classes need to be expressed in all-inclusive top-down hieararchy
LoC class numbers need to be assigned to each resource -- either natively by cataloger or algorithmically