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Background

What makes a good use case?

  • which of them is really linked data enabled, vs. what you could do with MARC if put in a big database
  • what can be done easily with linked data, even if it could be done without
  • examples tying library data together with other information
  • and working across institutions

Implementation Question:

What intersections of our data sources will be strong enough to support compelling use cases?

Goals

We need to choose a key set of use cases that address the challenges articulated in the grant proposal for the project. Here are some specific points that we want to make sure that the use cases address:

  1. Pragmatic value. They have real value to our core constituencies: librarians, researchers, teachers, students, etc.
  2. Community added-value. They leverage the unique value that librarians and scholars add to materials when they select, annotate, or reference the resources.

  3. Cross-institutional data. They clearly demonstrate the value in combining data from our three different institutions - ideally in a way that shows how that value will grow as more institutions join in.
  4. Leverage existing data and services. They leverage existing efforts in this space
  5. Integration into the Web. They show how research libraries can integrate with existing popular and useful Web sites and services, e.g., Wikipedia.
  6. Cross-discipline. They show examples from a variety of disciplines.
  7. Help core missions. They demonstrate value for teaching and learning, and research.
  8. Multi-data. They cover a broad range of scholarly information resource types.
  9. Unusual data. They show how non-traditional data can be useful.
  10. Media "photogenic": They clarify to the mainstream media the value of LOD and this project, and excite that media about the prospects
  11. They show interesting ways to use the aggregated data for analysis or visualization.
  12. They take advantage of data on how the materials are being used.

We need to be careful to not put in effort in areas where other projects are already working.

Clustering use cases

The purpose of this table is to group use cases into 5 clusters (columns) to help identify a small set of exemplar implementable use cases that can be the focus of engineering work. Good candidates to exemplar or to be merged to form an exemplar are marked "top".

Goalbib+curationbib+person dataleverage external authoritiesleverage deeper graph via queries or patternsleverage usage datano cluster

use cases by cluster in priority groupings

top: 3, 11, 24, 35 |   | challenges: 18top: 6, 9top: 22, 23 | med: 19, 25, 28, 42 | challenges: 5, 21, 32 | low: 15top: 12, 38 | med: 29 | challenges: 14, 17, 41 | low: 39top: 1 | med: 16 | challenges: 10,26challenges: 30,31,33,34,37, 27 | low 2,4,7,8,13,40

pragmatic

3 22, 424116 

community added-value

35   1 

cross-institutional data

3 5, 22, 32381631
leverage existing external authorities  23, 19, 25, 42, 21, 1538 8
leverage researcher networking data 623  7
leverage existing sources of LOD  22, 23, 4238, 41  
integration out into the Web  22, 19   
cross-discipline      
help core missions   12, 14, 38  
multi-data      
highlight unusual data  512, 141, 16 
media "photogenic"  22, 4212, 17, 41  
interesting analysis or visualization11  38, 17  
take advantage of usage data    1, 16, 10, 26 

 

 

Bib + Curation

Build a virtual collection

Build Lists that make metadata reusable

Tag items in cross-silo ways

Finding selected or highlighted works

 

Bib + Person 

Highlight faculty's work

Identify who an author influenced (question)

External Authorities

Find unexpected resources through OPAC searches

Pivot on works to explore more contexts

Acquire related works

Work based discovery

Form fill for IR deposit

Topic intersection of related authors

Deeper Graph Interactions

Find associated works better than Amazon

Identify related works

Increase the sophistication of query and display

 

Usage

Research guided by community usage

Guided in collection building by usage

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