1. Cornell Institute for Digital Collections Metadata and Cross-Collection Searching in Luna’s Insight

2. Cornell Institute for Digital Collections Problem: Integrating Access to Visual Collections Diverse visual resources and descriptions –Multiple repositories at Cornell, multiple digital collections, distributed digital collections –Different discovery methods and metadata formats Searchers are on their own to be aware of collections, know how to link to them, and search different interfaces

3. Cornell Institute for Digital Collections 1 st Solution: A Shared Union Catalog for Images Adopted MultiMIMSY 2000 from Willoughby Associates –Museum collections management software –Moved data from stand-alone applications into it –For the past 4 years, have worked on developing shared standards and practices

4. Cornell Institute for Digital Collections MIMSY Demo

5. Cornell Institute for Digital Collections Standards Issues No museum descriptive standard –CIDOC reference framework as a glue? We have tried to follow VRA 3.0 Use AAT, ULAN, TGN, for data values

6. Cornell Institute for Digital Collections Is VRA 3.0 too complex? [example]

7. Cornell Institute for Digital Collections 2nd Integration Solution: Insight from Luna Imaging Addresses issues of collection diversity –Can search multiple collections at once Addresses issues of metadata diversity –Maps data to a common standard –Allow searching across multiple heterogeneous collections

8. Cornell Institute for Digital Collections Insight Demo Selected features: –General search and display attributes –Cross-collection searching –Variable metadata displays –Annotation tool –Support for formats

9. Cornell Institute for Digital Collections Insight’s support of descriptive complexity Controlled vocabulary lists and repeating values for fields; Hierarchical structures and values; Groups of fields that should be treated together, e.g., artist name, life dates, nationality; Display order of values, fields, and groups of fields

10. Cornell Institute for Digital Collections

12. Values Insight v3 Data Structure ObjectsPeopleLocation Hierarchy Events Source Data Tables FieldGroups Mapping Tables TablesJoinsFields Terms Inverted Index Tables Replicates source data in a format common to all Insight collection databases

13. Insight Virtual Collection Manager Collection Manager Repository B Repository C Repository A Virtual Collection A Virtual Collection B Virtual Collection C Virtual Collection D

15. Cornell Institute for Digital Collections Standards Field Mapping Mapping collection fields to standard fields to allow searching across separate collection databases Maps Artist Name to CDWA FieldID 102 (Creation-Creator-Identity- Names) Field Mapping Results for “Artist Name” StandardIDStandardNameFieldIDDisplayNameMappingStandardMappingStandardFieldID 1ObjectID9MakerCDWA102 2DublinCore6CreatorCDWA102 3VRA6CreatorCDWA102 4VRA v3.016CreatorCDWA102 4VRA v3.019Personal NameCDWA102 5CIMI68Creator NameCDWA102 5CIMI79Creator GeneralCDWA102 6USMARC10Main EntryCDWA102 6USMARC11Added EntryCDWA102 15Dalton Museum6Artist NameCDWA102

16. Cornell Institute for Digital Collections “Built-in” Metadata Standards Dublin Core MARC VRA 2.0 VRA 3.0 CDWA You can add whatever you like

17. Cornell Institute for Digital Collections Implementation: Data into Insight Currently, export desired data as Text files, clean it up, and import into Insight This year – link tables between the two systems?

18. Cornell Institute for Digital Collections What is ahead for Insight? Development of stand-alone cataloging tool (May?) Further support for hierarchical objects –Books, letters Links to LDAP and Kerberos authentication GIS support