flora and fauna of the great lakes region:

ASC Annual MeetingJune 9, 2001 University of Michigan - University Library

Flora and Fauna of the Great Lakes

Region:

A Multimedia Digital Collection

QuickTime™ and aGIF decompressor

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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

Fish Catalog

MammalsDatabases

Fungus Catalog

Field Notes(New Content)

Digital Library Metadata

ImageMetadata


Mammals Example

Primary IDLastModCatalogAlcohol

SkeletonClear Stain

GenusSpecies

SubspeciesField[#]

DeterminerCataloger

Type StatusSize

Remarks

Location IDLOCAcronym

LOCDATELatitude DLatitude M Latitude S

Longitude DLongitude MLongitude SLatitude NS

Longitude EWMap DataMap CodeLocField

OceanContCountryState CountyGear

CollectorsLocTimeDrainage

Temp.Depth

LocalityLocRemarks

(incl. OrigField #)

UMMZ Zoology Database Fields

Table 1 Table 2

Museum no.

Sex

Skin

Skull

Skeleton

Fluid

Baculum

Glans

Karyotype

Field no.

Day

Month

Year

Country

Locality

Collector LN

Initials

2nd collector

Initials

Order

Family

Genus

Species

Subspecies

Batch no.

Comments

# individuals

State

County

Rodents database fields


Augmenting Existing Content

• Common Names

• Geospatial Referencing

• Dates


Collections Databases—Federation

Fish Catalog

MammalsDatabases

Fungus Catalog

Field Notes(New Content)


ImageMetadata

Species Name Lookups

GeospatialLookups

ISO Date

Lookups


Collections Databases—Augmentation

Fish Catalog

MammalsDatabases

Fungus Catalog


Species Name Lookups

GeospatialLookups

ISO Date

Lookups


New Databases

• Field Notes

• Image Metadata


Field Notes Structure—Collection Event

Collection Event

Species Account

Field Catalog

(projected)

CollectorData

Species Table Volume

Data

Page ImageTable

Location Data

JoinFile


Field Notes Structure—Species Account

Collection Event

Species Account

Field Catalog

(projected)

CollectorData

Species Table Volume

Data

Page ImageTable

Location Data

JoinFile


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

Frog=Work

Photo of Frog=Image

Digitized Photo of Frog(derived image)

Frog=Work PhotographerPhotograph Date

CollectorCollection Date

DigitizerDigitization

Date

Data is inherited


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.

flora and fauna of the great lakes region:

Documents

digital library content

new subjects

new audiencesopportunity

existing architecture

museum collection databases

collection database

bibliographic data

electronic text