Flora and Fauna of the Great Lakes Region: a multimedia Digital Collection



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tarix17.04.2018
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Flora and Fauna of the Great Lakes Region:


Collaborators

  • University Library -

    • Major investment in digital library content, infrastructure and architecture
  • Museum of Zoology and Herbarium

    • Strong collections; increasing pressure to serve a wider audience especially via the Web
  • Exhibits Museum of Natural History



Project Goals

  • To develop increased access to the Great Lakes region portions of the Museums collections

  • To develop an extensible infrastructure for putting natural history collections online

  • To explore, prototype and test tools for using the online resources for a variety of scholarly and educational purposes



Museum Goals

  • Additional support for digitizing collections

  • Opening their collections to new audiences

  • Opportunity to explore partnership with the Library

    • Support for maintenance and long-term access to their data


(screen shot)





Library Goals

  • Extend digital library support from the humanities to the natural sciences

  • Test the ability of the existing architecture to support new subjects and methods of inquiry

  • Extend digital library architecture to include cross-class searching - text, image, collection database records



Digital Library Holdings at the University of Michigan

  • Holdings as of March 2001

    • Full electronic text 31,558
    • Pages/images 6,684,342
    • Bibliographic Records 57,081,109
    • Words 2,536,312,477
    • Bytes 190,629,303,672


Digital Library Architecture at the University of Michigan

  • Image class

    • Federation of diverse collection databases
    • Support for image retrieval
  • Text class

  • Other classes such as bibliographic data and archival finding aids

  • Retrieval software available for licensing

  • Middleware is Open Source (DLXS)



New Challenge - Cross-Class Searching

  • To facilitate searching across text, image, and collection database records

  • To return results to users in ways that will be useful to them, rather than simply reflecting the characteristics of the underlying systems



New Content

  • Supplementing the museum collection databases with:

    • Field notes
      • Surrogate records and page images
    • Images
      • New photography and digitization of existing slides and negatives
    • Major monographs
      • Full text searching


New Audiences

  • Non-specialist users

    • Lifelong learners
    • Undergraduate students
    • K-12 users
  • Specialist users working in areas such as biodiversity research - working outside the confines of a single discipline



Metadata Challenges

  • Federate data from multiple existing databases

  • Augment data to serve new audience and uses

  • Create metadata for new materials

  • Provide basis for coherent shared displays of search results



Collections Databases—Federation



Mammals Example



Augmenting Existing Content

  • Common Names

  • Geospatial Referencing

  • Dates



Collections Databases—Federation



Collections Databases—Augmentation



New Databases

  • Field Notes

  • Image Metadata



Field Notes Structure—Collection Event



Field Notes Structure—Species Account



New Processes

  • Matching field content to authoritative forms for lookup construction (A miracle happens here)

  • Lookup export to originating databases (if time and funds permit)



Mapping Image Collections into the Image Services Metadata Structure

  • (what do art and fungi have in common?)



Dual Model of Image Representation

  • VRA Representation Model

  • Work=physical entity that exists, has existed at some time in the past, or that could exist in the future (fish, field notes page, painting, etc)

  • Image=a visual representation of a work



Work/Image Relationships

  • One work may have multiple image representations (picture of whole frog, frog parts, x-rays, etc.)

  • Images may have sequential derivations (photo is digitized, digital file has thumbnail, etc.)



Separating Work Data from Image Data



Why?

  • Work only needs to be described once

  • Image history is documented

  • Each image is associated with data (like creation dates) that are specific to its existence



Storing data in the Digital Library

  • Image database relates work and image metadata

  • Metadata exported as records with 1:1 correspondence with image file names

  • Digital Library stores metadata and image files using standard image class model core categories

  • User search calls up metadata and linked images



Desired result

  • Users can search across classes and collections using core fields (species, common name, location, date) or keywords

  • Users can search within collections using fields chosen from originating database

  • Displays can be customized to show common or custom field labels.



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