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Leveraging the deeper graph (via queries or patterns)
Leveraging usage data
Cluster: Bibliographic + curation data
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Use Case: Build a virtual collection.
As a faculty member or librarian, I want to create a virtual collection containing information resources from multiple collections across multiple universities either by direct selection or by a set of resource characteristics, so that I can share a focused collection with a <class, set of researchers, set of students in a disciplinary area>.
Potential Demonstrations
A.
Implementation Notes
Data Sources Needed
- MARC records
- Digital collections metadata
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Engineering Work
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Use Case: Tag scholarly information resources from multiple institutions to support reuse in multiple systems.
As a librarian,I would like to be able to 'tag' scholarly information resources from multiple institutions into curated lists, so that I can feed these these lists into subject guides, course reserves, or reference collections; I'd like these lists to be portable (into Drupal, into LibGuides, into Spotlight! or Omeka, into Sakai, e.g.) and durable; I'd like these lists to selectively feed back into the discovery environment without having to modify a MARC record..
Potential Demonstrations
- identify books on the virtual shelf of engineering reference handbooks
- identify "classic texts" in physics/astronomy/chemistry
- identify a "reference collection" for entomology
Implementation Notes
Data Sources Needed
- MARC records
- Digital collections metadata
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Engineering Work
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Cluster: Bibliographic + person data
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As a researcher, I'd like to see / search on works <by, about, cited by, collected, taught> by University faculty <in an OPAC, profiles system>, to discover works of interest based on connection to people, and to understand people based on their relation to works.
Potential Demonstrations
A1. A VIVO search results in a faculty list. Pivot by hitting a "see all publications by these researchers" link. This generates an OPAC results page or a VIVO results page with citations.
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C1. A search on "Stephen J. Gould" (a Harvard professor with archival materials at Stanford) shows works by, about, owned by, cited by, used in his courses, or held in his archive.
Implementation Notes
Data Sources Needed
- MARC records
- Journal articles (HWP data)
- VIVO / Harvard Profiles / Stanford CAP
- ORCID / VIAF / person authorities
- Gould Archival Finding Aid (C1)
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Engineering Work
- need URIs for all authors, researchers, people as subjects in MARC, article records, EAD
- relate URIs of all authors to VIAF, ORCID, etc.
- index affiliation data inot OPAC
- create bibliographic LD service for VIVO/CAP/Profiles to hit, return search results (use case A1)
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As a researcher, I'd like more context for my search results, and be able to pivot, extend or refine a search with a single click, in order to better assess foun resources, find related resources, and filter or expand search results to broaden or narrow a search on the fly.
Potential Demonstrations
A. <author,subject> searches in OPAC show a panel of related information for context
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D2. suggestions on a zero-results search page
Implementation Notes
Data Sources Needed
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Engineering Work
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Cluster: 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
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Engineering Work
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Use Case: Leverage the deeper graph to surface more relevant works
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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