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Building on and distilled from the preliminary use cases, this page represents a more refined set of use cases to guide the ontology and engineering work for the project. The use cases divide into five "clusters" reflecting the data available to research institutions and libraries, and the core LD4L mission of leveraging the intellectual input of librarians, domain experts and scholars as they produce, curate and use scholarly information resources. These five clusters and associated use cases are:
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Data Sources Needed
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Engineering Work
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Use Case: Authority-enhanced Forms Entry
As a <cataloger,depositor into a repository,faculty entering profile data>, I'd like an authority-enhanced look-up service that suggests authorized forms of data when doing data entry, so that data entry is faster, easier, unambiguous and less prone to error.
Potential Demonstrations
A. Catalogers get...
B. IR form...
C. Faculty profiles...
Implementation Notes
Data Sources Needed
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Engineering Work
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Cluster 4: Leveraging the deeper graph (via queries or patterns)
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As a scholar, I would like to find all costume photographs and scene illustrations for various stagings and performances of the plays of a particular author or the operas of a particular composer, so that I can see how the visual look of performances of the plays or operas have changed over time.
Potential Demonstrations
A. Given an author or composer, find images associated with the works of that author or composer.
B. Results that separate out classes of works: images associated with plays rather than novels or short stories; composed operas rather than songs or instrumental pieces.
Implementation Notes
Data Sources Needed
- MARC records
- GloPAC (http://www.glopad.org/pi/en/) database @ Cornell
Engineering Work
- Translation of GloPAC data into LD compatible with LD4L ontology
- Understanding of Works and Instances in catalog and GloPAC data
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As a researcher, I would like to see resources in response to a search where the relevance ranking of the results reflects the "importance" of the works, based on how they have been used or selected by others, so that I can find important resources that might otherwise be "hidden" in a large set of results.
Potential Demonstrations
A. Do a "page-rank" style algorithm across the full linked data graph, assigning appropriate weights to certain kinds of annotations and relationships and reflecting those weights in the relevance ranking of search results for a set of common queries.
B. Boost the ranking of any resource that has external relationship links by a simple computation over those relationships.
Implementation Notes
Data Sources Needed
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Engineering Work
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Cluster 5: Leveraging usage data
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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
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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
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As a librarian, I would like help building my collection by seeing what is being used by students and faculty
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
- LoC classification outline (650,000 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
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