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These use cases are work in progress...

The work of LD4L is heavily informed by use cases. If Linked Data is going to be "fit for purpose" and deliver value in a library context, its essential to know the users, uses and hoped for benefits for leveraging linked data. 

Building on and distilled from the preliminary use cases and a revised subset , this page represents a further refined set of use cases to guide the ontology and engineering work for the project. The use cases divide into six "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.

Related Work in Use Cases from LD4L

Other LD Use Case Resources

 

Cluster 1: Bibliographic + curation data

Use Case 1.1: Build a virtual collection

Example story: As a faculty member or librarian, I want to create a virtual collection or exhibit 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>.

This use case is focused on individual creation of a set of resources – a virtual collection or exhibit – along with descriptive information for the collection and possible arrangement and annotation of resources in the collection. The base use case is that any virtual collection will be publicly accessible and thus authentication is required only for user creating the collection (and not for users of the collection). The inter-institutional aspect of this use case is not supported by current systems. It is expected that the collection URI will support both human and LD views.

Use Case 1.2: Tag scholarly information resources to support reuse

Example story: As a librarian, I would like to be able to tag scholarly information resources from one or 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/tags to selectively feed back into the discovery environment without having to modify the catalog records.

This use case is similar to 1.1 in a number of ways, and perhaps the data model would be the same. However, the likely scale of tagging is much larger (perhaps selection of O(100,000) items to populate a virtual subject library from the central library catalog) and does not include the need for detailed curation (textual annotation, ordering, structure, description). It is expected that such tagging would also be maintained/curated over long periods whereas virtual collections may be disposable or ephemeral. It is also expected that creation and maintenance might be a collaborative process.

There are possible ties to shared selection work such as that between Stanford and Berkeley, and between Columbia and Cornell.

Cluster 2: Bibliographic + person data

Use Case 2.1: See and search on works by people to discover more works, and better understand people

Example story: 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.

Example story: As a researcher, I’d like to see a list of works from the most prolific authors in my field at my institution and at other institutions.

This use case is demonstrates pivoting on combined profile system and catalog data, perhaps even between systems. Need to be careful to distinguish demonstration of this use case from the faculty finder functionality of existing profile systems. Must understand how much catalog data adds (perhaps more in non-journal disciplines) and value of cross-institutional data.

Cluster 3: Leveraging external data including authorities

Use Case 3.1: Search with Geographic Data for Record Enrichment and Pivoting

Example story: As a researcher, I'd like to see the geographic context of my search results, and be able to pivot, extend or refine a search with a single click, in order to better assess found resources, find related resources, and filter or expand search results to broaden or narrow a search on the fly.

Use Case 3.2: Search with Subject Data for Record Enrichment and Pivoting

Example story: As a researcher, I'd like to see the subject area contexts for my search results and be able to pivot, extend or refine a search with a single click, in order to better assess found resources, find related resources, and filter or expand search results to broaden or narrow a search on the fly.

Use Case 3.3: Search with Person Data for Record Enrichment and Pivoting

Example story: As a researcher, I'd like to see the person contexts for my search results and be able to pivot, extend or refine a search with a single click, in order to better assess found resources, find related resources, and filter or expand search results to broaden or narrow a search on the fly. 

This use case is distinguished from cluster 2 because the focus is person data from external sources rather than from profiling systems of the collaborating institutions. 

Use Case 3.4: Authority tool for more accurate data entry

Example story: As a librarian I want my student catalogers to be guided through selection of vocabulary terms to improve both their accuracy and speed. Furthermore, I want to data entry tool to auto-configure the vocabulary terms based on knowledge of the ontology and vocabulary we have decided to use for our project.

This use case addresses the need to improve data creation accuracy. In current systems with controlled vocabularies expressed as simple strings there are frequent errors in entry. In linked data systems URIs from controlled vocabularies are used to avoid some of the problems of simple strings but applications need ways for users to efficiently and accurately select terms. Typeahead lookup to improve reduces user effort/time while also improving data quality on entry and curation.

Cluster 4: Leveraging the deeper graph (via queries or patterns)

Use Case 4.1: Identifying related works

Example story: As a scholar, I would like to find all the images associated with various instances of a work sorted by time, so that I can see how the depictions of or illustrations in a work have changed over time.

Example story (GloPAD specific): 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.

The essence of this use case is making use of complex graph relationships via queries or patterns (rather than direct connections) to allow discovery that would not be possible without the semantics of different relationships between items and types of items included in the graph. User stories and demonstrations will be somewhat tied to available data because detailed information and relationships will not be available for all resources.

Use Case 4.2: Leverage the deeper graph to surface more relevant works

Example Story: 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.

In this use case the importance calculated will reflect importance in the scholarly world and will be different from those in commodity systems, as well as including items that would not appear in commodity systems (e.g. manuscripts). A benchmark will be doing better than Amazon with richer results.

Cluster 5: Leveraging usage data

Use Case 5.1: Research guided by community usage

Example story: 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

This use case requires understanding of the relevant community of the user. This would require them to be authenticated and community inferred by some means/data from their identity, or for community to be specified as part of the discovery process, or for community to be inferred as part of the discovery process.

Use Case 5.2: Be guided in collection building by usage

Example story: As a librarian, I would like help building my collection by seeing what is being used by students and faculty.

Example story: As a subject librarian, I would like to see what resources in my subject area are heavily used at peer institutions but are not in my institution’s collection.

This use case is essentially a business analytics tool that would help libraries make best use of collection building activities and funds. This would be useful at both institutional or cross-institutional levels.

Cluster 6: Three-site services 

Use Case 6.1: Cross-site search

Example story: As a scholar I don’t want my discovery process to be constrained by the collection boundaries of my university yet I want to retain the detailed coverage of special collections that are important in my field (can we give a good example field). I want results ranked by the scholarly value, not simply popularity in the public eye.

This use case demonstrates the power of sharing library data as linked open data. It demonstrates what could be done by a third party as a result of data exposed from this work, and what any of the partners could do by integrating data from the other partners.

 

 

 

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