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Application Profiles Decisions for Your Digital Collections. Expectations.
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Expectations “Metadata is expected to follow existing and emerging standards in order to facilitate integrated access to multiple information providers over the web. However, there are many new standards, and most of them are still under development . . .
The plot thickens . . . . And it is rare that the requirements of a particular project or site can all be met by any one standard “straight from the box.” . . . and there are no easy answers
The not-so-easy answer • Metadata application profiles • Tailor complex schemas for project-specific usage • Collaborate with all project stakeholders
tgm lcsh local w3cdtf lcnaf dacs aacr2 local cco teimods mets mix ead marc dc local premis
Application profiles: Basic Definition schemas which consist of data elements drawn from one or more namespaces, combined together by implementers, and optimized for a particular local application. -- Heery, R. and Patel, M. Application profiles: mixing and matching metadata schemas. Ariadne 25, Sept. 24, 2000 http://www.ariadne.ac.uk/issue25/app-profiles/intro.html
Example Australia Government Locator Service Manual http://www.egov.vic.gov.au/pdfs/AGLSmanual.pdf Title Identifier Creator Date Publisher Contributor Language Subject Description Type Format Coverage Source Relation Rights Availability Function Audience Mandate
Basic Definition (cont.) An application profile is an assemblage of metadata elements selected from one or more metadata schemas and combined in a compound schema. -- Duval, E., et al. Metadata Principles and Practicalities D-Lib Magazine, April 2002 http://www.dlib.org/dlib/april02/weibel/04weibel.html
Profile features • Selection of applicable elements, sub-elements and attributes • Interpretation of element usage • Element constraints • Mandatory, optional or recommended • Repeatable or non-repeatable • If repeatable, maximum no. of occurrences • Fixed or open values • Authority controlled or not
Designing of Application Profiles • Select “base” metadata namespace • Select elements from other metadata name spaces • Define local metadata elements • Enforcement of applications of the elements • Cardinality enforcement • Value Space Restriction • Relationship and dependency specification
Select “base” metadata namespace Select elements from other metadata name spaces Define local metadata elements Enforcement of applications of the elements Cardinality enforcement Value Space Restriction Relationship and dependency specification -- Dublin Core --13 elements (no source, no relation) --thesis.degree -- some changed from “optional to “mandatory” -- recommended default value, in addition to DC’s -- new refinement terms
DC-Lib A library application profile will be a specification that defines the following: • required elements • permitted Dublin Core elements • permitted Dublin Core qualifiers • permitted schemes and values (e.g. use of a specific controlled vocabulary or encoding scheme) • library domain elements used from another namespace • additional elements/qualifiers from other application profiles that may be used (e.g. DC-Education: Audience) • refinement of standard definitions
… use terms from multiple namespaces The DC-Library Application Profile uses terms from two namespaces: • DCMI Metadata Terms [http://dublincore.org/documents/dcmi-terms/] • MODS elements used in DC-Lib application profile [http://www.loc.gov/mods] • The Usage Board has decided that any encoding scheme that has a URI defined in a non-DCMI namespace may be used.
Can an AP declare new metadata terms (elements and refinements) and definitions? "If an implementor wishes to create 'new' elements that do not exist elsewhere then (under this model) they must create their own namespace schema, and take responsibility for 'declaring' and maintaining that schema." Heery and Patel (2000) Dublin Core Application Profile Guidelines [CEN, 2003] also includes instructions on "Identifying terms with appropriate precision" (Section 3) and "Declaring new elements" (Section 5.7)
Creating Metadata Records • The “Library Model” • Trained catalogers, one-at-a-time metadata records • The “Submission Model” • Creators (agents) create metadata when submitting resources • The “Automated Model” • Automated tools create metadata for resources • “Combination Approaches”
The Library Model • Records created “by hand,” one at a time • Shared documentation and content standards (AACR2, etc.) • Efficiencies achieved by sharing information on commonly held resources • Not easily extended past the granularity assumptions in current practice
The Submission Model • Based on creator or user generated metadata • Can be wildly inconsistent • Submitters generally untrained • May be expert in one area, clueless in others • Often requires editing support for usability • Inexpensive, may not be satisfactory as an only option
The Automated Model • Based largely on text analysis; doesn’t usually extend well to non-text or low-text • Requires development of appropriate evaluation and editing processes • Still largely research; few large, successful production examples, yet • Can be done in batch • Also works for technical as well as descriptive metadata
Content “Storage” Models • “Storage” related to the relationships between metadata and content • These relationships affect how access to the information is accomplished, and how the metadata either helps or hinders the process (or is irrelevant to it)
Common “Storage” Models • Content with metadata • Metadata only • Service only
Content with metadata • Examples: • HTML pages with embedded ‘meta’ tags • Most content management systems (though they may store only technical or structural metadata • Text Encoding Initiative (TEI) • Often difficult to update
Metadata only • Library catalogs • Web-based catalogs often provide some services for digital content • Electronic Resource Management Systems (ERMS) • Provide metadata records for title level only • Metadata aggregations • Using OAI-PMH for harvest and re-distribution
Service only • Often supported partially or fully by metadata • Google, Yahoo (and others) • Sometimes provide both search services and distributed search software • Electronic journals (article level) • Linked using “link resolvers” or available independently from websites • Have metadata behind their services but don’t generally distribute it separately
Common Retrieval Models • Library catalogs • Based on a consensus that granular metadata is useful • Web-based (“Amazoogle”) • Based primarily on full-text searching and link- or usage-based relevance ranking • Portals and federations • Service provider model
Nine Questions to Guide You in Choosing a Metadata Schema • Who will be using the collection? • Who is the collection cataloger (a.k.a. metadata creator)? • How much time/money do you have? • How will your collection be accessed? • How is your collection related to other collections?
Nine Questions to Guide You in Choosing a Metadata Schema • What is the scope of your collection? • Will your metadata be harvested? • Do you want your collection to work with other collections? • How much maintenance and quality control do you wish?
Decisions for Your Digital Collection • 1. Considering metadata in a larger project setting • Organization-wide collaborative • Library • Special collections • Archives • Academic departments, business departments • State-wide collaborative projects • E.g., Ohio Memory • Nation-wide projects • E.g., American Memory
Decisions for Your Digital Collection • Similar or related disciplines • E.g., architecture projects, art projects • Similar or related media • E.g., multimedia database, image galleries, visual resources repositories, manuscript collections, company procedure documents …
Principles to be considered • Interoperability • Your data can be integrated into a larger project. • Your data structure allows others to join you. • Metadata reuse • Existing MARC or EAD records can be reused.
Principles to be considered • Simplicity • High quality original data • Ensure best quality. • One-time project vs. ongoing projects – considering long life. Few revision chances in the future.
2. Knowing the difference • “Object"/"work" vs. reproduction • Textual vs. non-textual resources • Document-like vs. non-document-like objects • Collection-level vs. item-level
How to describe …? • Describe what? • The image itself? Or • The building? • The building as a building? Or • A building which has a historical importance?
Work vs. Image • A work is a physical entity that exists, has existed at some time in the past, or that could exist in the future. • An image is a visual representation of a work. It can exist in photomechanical, photographic and digital formats.
Work vs. Image • A digital collection needs to decide what is the entity of their collection: • works, • images, or • both? • How many metadata records are needed for each entity? • Some part of the data can be reused. • E.g., one work has different images or different formats
Document-like vs. non-document-like Each object usually has the following characteristics: • being in three dimensions, • having multiple components • carrying information about history, culture, and society, and • demonstrating in detail about style, pattern, material, color, technique, etc.
Textual vs. Non-textual • Text: • Would allow for full text searching or automatic extraction of keywords. • Marked by HTML or XML tags. • Tags have semantic meanings. • Non-textual, e.g., images: • Only the captions, file names can be searched, not the image itself. • Need transcribing or interpreting. • Need more detailed metadata to describe its contents. • Need knowledge to give a deeper interpretation.
Determining What Metadata is Needed • Who are your users? (current as well as potential) (e.g., library or registrarial staff, curators, professors, advanced researchers, students, general public, non-native English speakers) • What information do you already have (even if it’s only on index cards or in paper files)? • What information is already in automated form? • What metadata categories are you currently using? Are they adequate for all potential uses and users? Do they map to any standard? • What is an adequate “core” record? • Is your data clean and consistent enough to migrate? (You may consider re-keying in some cases.)
Data Standards: Essential Steps • First Step: Select and Use Appropriate Metadata Elements • Data Structure Standards (a.k.a. metadata standards) • Elements describing the structure of metadata records: What elements should a record include? • Meant to be customized according to institutional needs • MARC, EAD, MODS, Dublin Core, CDWA, VRA Core are examples of data structure standards
A Typology of Data Standards • Data structure standards (metadata element sets): MARC, EAD, Dublin Core, CDWA, VRA Core, TEI • Data value standards (vocabularies): LCSH, LCNAF, TGM, AAT, ULAN, TGN, ICONCLASS • Data content standards (cataloging rules): AACR (RDA), ISBD, CCO, DACS • Data format/technical interchange standards (metadata standards expressed in machine-readable form): MARC, MARCXML, MODS, EAD, CDWA Lite XML, Dublin Core Simple XML schema, VRA Core 4.0 XML schema, TEI XML DTD
Data Standards: Essential Steps • Second Step: Select and Use Vocabularies, Thesauri, & local authority files • Data Value Standards • Data values are used to “populate” or fill metadata elements • Examples are LSCH, AAT, TGM, MeSH, ICONCLASS, etc., as well as collection-specific thesauri & controlled lists • Used as controlled vocabularies or authorities to assist with documentation and cataloging • Used as research tools – vocabularies contain rich information and contextual knowledge • Used as search assistants in database retrieval systems or with online collections
Data Standards: Essential Steps • Third Step: Follow Guidelines for Documentation • Data Content Standards • Best practices for documentation (i.e. implementing data structure and data value standards) • Rules for the selection, organization, and formatting of content • AACR (Anglo American Cataloguing Rules), CCO (Cataloging Cultural Objects), DACS (Describing Archives: A Content Standard), local cataloging rules
Data Standards: Essential Steps • Fourth Step: • Select the Appropriate Format for Expressing/Publishing Data • DATA FORMAT STANDARDS • How will you “publish” and share your data in electronic form? • How will service providers obtain, add value to, and disseminate your data? • Some candidates are Dublin Core XML; MARC21; MARC XML; CDWA Lite XML schema; MODS, etc.