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D-Lib Magazine
June 2006

Volume 12 Number 6

ISSN 1082-9873

Metadata Interoperability and Standardization – A Study of Methodology Part I

Achieving Interoperability at the Schema Level


Lois Mai Chan
School of Library and Information Science
University of Kentucky

Marcia Lei Zeng
School of Library and Information Science
Kent State University

Red Line



The rapid growth of Internet resources and digital collections has been accompanied by a proliferation of metadata schemas, each of which has been designed based on the requirements of particular user communities, intended users, types of materials, subject domains, project needs, etc. Problems arise when building large digital libraries or repositories with metadata records that were prepared according to diverse schemas. This article (published in two parts) contains an analysis of the methods that have been used to achieve or improve interoperability among metadata schemas and applications, for the purposes of facilitating conversion and exchange of metadata and enabling cross-domain metadata harvesting and federated searches. From a methodological point of view, implementing interoperability may be considered at different levels of operation: schema level, record level, and repository level. Part I of the article intends to explain possible situations in which metadata schemas may be created or implemented, whether in individual projects or in integrated repositories. It also discusses approaches used at the schema level. Part II of the article will discuss metadata interoperability efforts at the record and repository levels.

1. Introduction

In response to the rapid development of digital libraries and repositories, many general and domain-specific metadata standards have been developed or proposed by various user communities. Even within the same subject domain or for the same type of resource, there are often two or more options of metadata standards. In building a large digital library or repository, an issue often encountered is that the participants may have used diverse schemas and description methods to create their metadata records. Ideally, users of such a digital library or repository "should be able to discover through one search what digital objects are freely available from a variety of collections, rather than having to search each collection individually" [Tennant, 2001]. In other words, users should not have to know or understand the methods used to describe and represent the contents of the digital collection. However, in reality, the diversity of standards for the description of varying types of resources, sometimes within the same digital library or repository, poses particular challenges to users as well as those who are responsible for managing these resources. Only if devices can be developed to attain interoperability will it be possible to facilitate the exchange and sharing of data prepared according to different metadata schemas and to enable cross-collection searching.

In recent literature, a great deal has been written about achieving interoperability among different metadata schemas. A methodological analysis of interoperability focusing on knowledge organization systems (KOS) was presented in a previous article by the authors [Zeng and Chan, 2004]. This article analyzes some of the methods currently used to achieve interoperability in a broader context, that is, among different metadata schemas and applications.

2. Definitions

2.1 Metadata Schema

A metadata schema consists of a set of elements designed for a specific purpose, such as describing a particular type of information resource [NISO, 2004]. As defined in the report of the American Library Association Committee on Cataloging: Description and Access (CC:DA) Task Force on Metadata: "A metadata schema provides a formal structure designed to identify the knowledge structure of a given discipline and to link that structure to the information of the discipline through the creation of an information system that will assist the identification, discovery, and use of information within that discipline" [CC:DA, 2000].

In the literature, the words "schema", "scheme", and "element set" have been used interchangeably to refer to metadata standards. In practice, the word "schema" usually refers to an entire entity including the semantic and content components (which are usually regarded as an "element set") as well as the encoding of the elements with a markup language such as SGML (Standard Generalized Markup Language) and XML (Extensible Markup Language). A metadata element set has two basic components:

  1. Semantics – definitions of the meanings of the elements and their refinements.
  2. Content – declarations or instructions of what and how values should be assigned to the elements.

For each element defined, a metadata standard usually provides content rules for how content should be included (for example, how to identify the main title), representation rules for content (for example, capitalization rules or standards for representing time), and allowable content values (for example, whether values must be taken from a specified controlled vocabulary or can be author-supplied, derived from text, or added by metadata creators working without a controlled term list.)

It is our observation that many metadata standards provided an element set without considering the encoding format in their preliminary versions. For example, Dublin Core (DC), VRA (Visual Resource Association) Core Categories, Categories for the Description of Works of Art (CDWA), and the Learning Object Metadata (LOM) were all published and accepted in terms of their semantics and content long before the specific encoding methods for their data models were published. On the other hand, a few other metadata standards, like the Encoded Archival Description (EAD) Document Type Definition (DTD), provided an encoded element set from the beginning. The EAD DTD, a standard for encoding archival finding aids currently using XML, was published a decade ago with an SGML DTD [Library of Congress, 2003].

In this article, the term "schema" is used to refer to all metadata standards being discussed, although most of the time the focus of the discussion will be on the semantics and content of the schemas.

2.2 Interoperability

There have been many attempts at defining the concept of interoperability. A few examples are given below:

"Interoperability is the ability of multiple systems with different hardware and software platforms, data structures, and interfaces to exchange data with minimal loss of content and functionality" [NISO, 2004].
"Interoperability is the ability of two or more systems or components to exchange information and use the exchanged information without special effort on either system" [CC:DA, 2000].
"Interoperability: The compatibility of two or more systems such that they can exchange information and data and can use the exchanged information and data without any special manipulation" [Taylor 2004, p. 369].

It is becoming generally accepted in the information community that interoperability is one of the most important principles in metadata implementation. Other basic metadata principles include simplicity, modularity, reusability, and extensibility [Duval et al., 2002; Zeng et al., 2003]. These principles inform metadata database design as well as other system-dependent developments. From the very beginning of a metadata project, the principles that enable user-centered and interoperable services should be foremost in design and implementation.

3. Metadata Interoperability Projects at Different Levels

In recent years, numerous projects have been undertaken by the many players and stakeholders in the information community to achieve interoperability among different metadata schemas and their applications. Ideally, a uniform standard approach would ensure maximum interoperability among resource collections. If all participants of a consortium or repository were required to use the same schema, such as the MARC (Machine-Readable Cataloging) format or the Dublin Core (DC), a high level of consistency would be maintained. This, of course, has been the approach in the library community for over a century and is the ultimate solution to the interoperability problem. However, although it is a conceptually simple solution, it is not always feasible or practical, particularly in heterogeneous environments serving different user communities where components or participating collections contain different types of resources already described by a variety of specialized schemas. The uniform standardization method is only viable at the beginning or early stages of building a digital library or repository, before different schemas are adopted by the participants. Examples include the MARC standards used in union catalogs of library collections and the Dublin Core-based Electronic Theses and Dissertations Metadata Standard (ETD-MS) used by members of the Networked Digital Library of Theses and Dissertations (NDLTD).

In many communities, the uniform standard approach may not be applicable, therefore other mechanisms of achieving interoperability must be adopted. From a methodological point of view, implementing interoperability may be considered at different levels: schema level, record level, and repository level. (Figure 1)

Image showing the various levels of metadata projects

Figure 1. Various levels of metadata projects.

Figure 1 intends to explain possible paths from creation of schemas to their applications in individual projects or in integrated repositories. The situations could be:

  1. A schema was created and applied to records for one or more particular projects.
  2. Elements from several schemas were considered. An application profile was established based on a number of schemas; then, the element set specified by the application profile was applied to records of particular project(s).
  3. Two or more existing databases containing metadata records were exchanged or integrated based on the matching elements of the schemas involved.
  4. Records from existing metadata collections were harvested or merged by a unified repository. These collections had applied different schemas or established their own application profiles before the harvesting.

Each of these approaches can have particular focuses, and interoperability efforts can take place at any level.

From another perspective, the results of interoperability efforts can be observed at different levels as well:

  1. Schema level – Efforts are focused on the elements of the schemas, being independent of any applications. The results usually appear as derived element sets or encoded schemas, crosswalks, application profiles, and element registries.
  2. Record level – Efforts are intended to integrate the metadata records through the mapping of the elements according to the semantic meanings of these elements. Common results include converted records and new records resulting from combining values of existing records.
  3. Repository level – With harvested or integrated records from varying sources, efforts at this level focus on mapping value strings associated with particular elements (e.g., terms associated with subject or format elements). The results enable cross-collection searching.

It should be noted that the models to be discussed in this article are not always mutually exclusive. Sometimes, within a particular project, more than one method may be used.

4. Achieving Interoperability at the Schema Level

Before a project selects and applies a metadata schema to its collection, an important step is to ensure that the data processed according to a given schema will result in a digital collection that is interoperable with other digital collections or systems. At the schema level, interoperability actions usually take place before operational level metadata records are created. The actions focus on the elements (independent of individual applications). Methods used to achieve interoperability at this stage mainly include: derivation, application profiles, crosswalks, switching-across, framework, and registry. What follows is a discussion of each of these methods.

4.1 Derivation

In this approach, a new schema is derived from an existing one. In a collection of digital databases where different components have different needs and different requirements regarding description details, an existing complex schema such as the MARC format may be used as the "source" or "model" from which new and simpler individual schemas may be derived. Specific derivation methods include adaptation, modification, expansion, partial adaptation, translation, etc. In each case, the new schema is dependent on the source schema.

This approach ensures a similar basic structure and common elements while allowing different components to vary in depth and details. For example, the TEI Lite is derived from the full Text Encoding Initiative (TEI). Both MODS (Metadata Object Description Schema) and MARC Lite are derived from the full MARC 21 standard. Changes could also occur in the encoding format (e.g., MARCXML), but the basic original content elements are retained (Figure 2).

A similar approach to derivation is translation of an existing schema into a different language. The content remains largely the same as the source schema. Examples include different language versions of the Dublin Core element set.

chart showing examples of schema derivation

Figure 2. Examples of schema derivation.

Yet another variation is the adaptation of an existing schema with modifications to cater to local or specific needs. This approach reflects the extensibility principle of metadata. Extensible metadata systems must allow for extensions and expansions so that particular needs of a given application can be accommodated. Examples of adaptation/modification include:

  • Elements proposed for the Dublin Core Metadata Element Set in education- and training-related applications and projects. The final working document added an audience element [DCMI Metadata Terms].
  • Electronic Theses and Dissertations Metadata Standard (ETD-MS). This standard uses 13 of the 15 Dublin Core elements and an additional element: [ETD-MS].
  • Gateway to Educational Materials (GEM) expansion of Dublin Core. Additional elements include: cataloging, essential resources, pedagogy, standards, and duration [GEM Element Set].
  • Rare Materials Descriptive Metadata developed by the Peking University Library. It uses 12 DC elements, plus edition and physical description as two local core elements and a collection history element for the third level extension [Yao et al., 2004].

It should be noted that the element sets following the extension approach are sometimes regarded as application profiles as well.

4.2 Application Profiles

Even within a particular information community, there are different user requirements and special local needs. The details provided in a particular schema may not meet the needs of all user groups. Therefore, based on the notion that metadata standards are necessarily localized and optimized for specific contents, the concept of "application profiles", a typical approach to accommodating individual needs, emerged [Johnston, 2003]. While a particular existing schema or schemas are used as the basis for description in a particular digital library or repository, individual needs are met through a set of specific application guidelines or policies established for a particular interest or user group.

Image showing an application profile consisting of metadata terms (elements and 	element refinements) drawn from one or more namespaces

Figure 3. Illustration of an application profile consisting of metadata terms (elements and element refinements) drawn from one or more namespaces (element sets).

According to the Dublin Core Metadata Initiative (DCMI) Usage Board, "an Application Profile (AP) is a declaration of which metadata terms an organization, information resource, application, or user community uses in its metadata" [Baker, 2003]. The use of application profiles ensures a similar basic structure and common elements, while allowing for varying degrees of depth and detail and for different user communities.

Application profiles usually consist of metadata elements drawn from one or more metadata schemas (see Figure 3), combined into a compound schema by implementors, and optimized for a particular local application [Heery and Patel, 2000; Duval et al., 2002]. For example, the Australasian Virtual Engineering Library's AVEL Metadata Set consists of nineteen elements. In addition to supporting 14 DC elements (excluding dc.source element), it also supports one AGLS (Australian Government Locator Service) metadata element (AGLS.Availability), one EDNA (Education Network Australia) element (EdNA.Review), and three Administrative elements (AC.Creator, AC.DateCreated, and AVEL.Comments).

An application profile (AP) may also be based on one single schema but tailored to different user communities. For example, DC-Library Application Profile (DC-Lib) clarifies the use of the DC metadata element set in libraries and library-related applications and projects. The DC Government Application Profile clarifies the use of DC in a government context [Cumming et al., 2001]. Another example is the Biological Data Profile of the National Biological Information Infrastructure (NBII), which is based on the Content Standard for Digital Geospatial Metadata (CSDGM) of the Federal Geographic Data Committee (FGDC).

A blurred area for AP implementors exists: Can an AP declare new metadata terms (elements and refinements) and definitions? The answer is that, by definition, an AP cannot "declare" new metadata terms and definitions. Heery and Patel (2000) highlighted the characteristics of Application Profiles that may draw on one or more existing namespaces, may introduce no new data elements, may specify permitted schemes and values and, can refine standard definitions. They commented further that "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." 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). An AP may also provide additional documentation on how the terms used are constrained, encoded, or interpreted for particular purposes [Baker, 2003]. In practice, then, the implementation of an application profile often involves the following steps: (1) selecting a "base" metadata namespace, (2) selecting elements from other metadata namespaces, (3) defining local metadata elements and declaring new elements' namespaces, and (4) enforcing application of the elements (including cardinality enforcement, value space restriction, and relationship and dependency specification) [Zhang, 2004; Duval et al., 2002].

The SCHEMAS Registry (to be discussed in 4.6 Metadata Registry section), a registry of application profiles maintained by the UK Office for Library and Information Networking (UKOLN), contains several metadata element sets as well as a large number of activity reports that describe and comment on various metadata related activities and initiatives.

4.3 Crosswalks

A crosswalk (Figure 4) is "a mapping of the elements, semantics, and syntax from one metadata scheme to those of another" [NISO, 2004]. Currently, crosswalks are by far the most commonly used method to enable interoperability between and among metadata schemas. This method begins with independent metadata schemas. Attempts are made to map or create crosswalks between equivalent or comparable metadata terms (elements and refinements). (Note that sometimes other terms are used to refer to "element," such as "field", "label", "tag", etc.) The mechanism used in crosswalks is usually a chart or table that represents the semantic mapping of data elements in one data standard (source) to those in another standard (target) based on the similarity of function or meaning of the elements [Baca et al., 2000].

Image showing the establishment of a crosswalk between two schemas

Figure 4. Establishing a crosswalk between two schemas.

Crosswalks allow systems to effectively convert data from one metadata standard to another. They enable heterogeneous collections to be searched simultaneously with a single query as if they were a single database (semantic interoperability). In recent years, major efforts in metadata mapping have produced a substantial number of crosswalks. Almost all schemas have created crosswalks to popular schemas such as DC, MARC, LOM, etc. Metadata specifications may also include crosswalks to a previous version of a schema as well as to other metadata schemas. An example is the VRA Core 3.0, which lists mapped elements in target schemas VRA 2.0 (an earlier version), CDWA, and DC.

The predominant method used in crosswalking is direct mapping or establishing equivalency among elements in different schemas. Metadata "mapping" refers to a formal identification of equivalent or nearly equivalent metadata elements or groups of metadata elements from different metadata schemas, carried out in order to facilitate semantic interoperability [Baca et al., 2000]. Quite a few metadata properties need to be brought into consideration in the mapping. According to the NISO document Issues in Crosswalking Content Metadata Standards [St. Pierre and LaPlant, 1998], common properties may include:

  • a semantic definition of each metadata element;
  • whether or not a metadata element is mandatory, optional, or mandatory based on certain conditions;
  • whether or not a metadata element may occur multiple times in the same record;
  • constraints due to the organization of metadata elements relative to each other, e.g., hierarchical parent-child relationships;
  • constraints imposed on the value of an element (e.g., free text, numeric range, date, or a controlled vocabulary); and
  • optional support for locally defined metadata elements.

Two approaches have been used in crosswalking practice. The "absolute crosswalking" approach requires exact mapping between the involved elements (say, vra.titledc.title) of a source schema (e.g., VRA Core) and a target schema (e.g., DC). Where there is no exact equivalence, there is no crosswalking (e.g., vra.technique → [empty space]) (see Figure 5). Absolute crosswalking ensures the equivalency (or closely-equivalent matches) of elements, but does not work well for data conversion. The problem is that data values in non-mappable space will be left out, especially when a source schema has a richer structure than that of the target schema. To overcome this problem, an alternative approach, "relative crosswalking", has been used to map all elements in a source schema to at least one element of a target schema, regardless of whether the two elements are semantically equivalent or not (e.g., vra.techniquedc.format) (also see Figure 5). The relative crosswalking approach appears to work better when mapping from complex to simpler schema (e.g., from MARC to DC, but not vice versa).

Chart showing absolute and relative crosswalking

Figure 5. Absolute and relative crosswalking.

One of the problems of crosswalking is the different degrees of equivalency: one-to-one, one-to-many, many-to-one, and one-to-none [Zeng and Xiao, 2001]. These situations occur in many metadata crosswalks. The level of details may extend from elements-only to elements-plus-qualifiers/refinements or sub-elements. However, usually only the names of the elements and their definitions are taken into consideration in a crosswalk.

Chart showing the different degrees of element equivalency

Figure 6. Different degrees of element equivalency in crosswalked schemas. A1 and B1 represent elements from A and B schemas.
Source: Zeng (2001)

This means that when mapping individual elements, often there are no exact equivalents. Meanwhile, many elements are found to overlap in meaning and scope. For this reason, data conversion based on crosswalks could create quality problems. This issue will be discussed further in section 2.1 Conversion of Metadata Records in Part II of the article.

4.4 Switching-across

While crosswalking works well when the number of schemes involved is small, mapping among multiple schemas is not only extremely tedious and labor intensive but also requires enormous intellectual effort. For example, a one-way crosswalk requires one mapping process (AB), and a two-way crosswalk requires two mapping processes (AB and BA). The process becomes more and more cumbersome when more schemas are involved. A four-schema crosswalk would require twelve (or six pairs of) mapping processes. As a result, using a switching schema (new or existing) to channel crosswalking among multiple schemas has become a well-accepted solution (see Figure 7).

Image showing switching-across for multiple schemas

Figure 7. Switching-across when multiple schemas are involved.

In this model, one of the schemas is used as the switching mechanism among multiple schemas. Instead of mapping between every pair in the group, each of the individual metadata schemas is mapped to the switching schema only. An example is Getty's crosswalk in which seven schemas all crosswalk to CDWA [Harpring et al., 2000].

Screen shot showing the CDWA crosswalk

Figure 8. Crosswalk of CDWA to seven schemas.
(Click here to see a larger version of Figure 8.)

4.5 Metadata Framework

A framework can be considered as a skeleton upon which various objects are integrated for a given solution (see Figure 9). The need for a metadata framework is best demonstrated by emerging digital preservation efforts. While many organizations have developed metadata for digital preservation in support of their own activities, such efforts have been conducted largely in isolation, lacking any substantial degree of cross-organizational coordination. It becomes obvious that a metadata framework is needed to represent a consensus of leading experts and practitioners and could be readily applied to a broad range of such activities [OCLC/RLG Working Group on Preservation Metadata, 2002]. In 2002, a conceptual framework for a generic digital archiving system emerged in the form of an Open Archival Information System (OAIS) reference model and was issued as a recommendation by the ISO Consultative Committee for Space Data Systems (CCSDS). It establishes a common framework of terms and concepts that comprise an Open Archival Information System, providing a basis for further standardization within an archival context.

Image shwoing the framework and associated schemas

Figure 9. A framework and the schemas associated with the framework.

Another example comes from the metadata framework currently used in the DLESE (Digital Library for Earth System Education) Discovery System. After a few years' exploration of establishing a framework for DLESE metadata based on IMS (Instructional Management Systems) Learning Resource Meta-data Specification, the Alexandria Digital Earth Prototype (ADEPT ) project, DLESE, and NASA's Joined Digital Library (JDL) decided in June 2001 to create an ADN metadata framework that all three organizations can use [ADN].

The purpose of the ADN framework, as stated on its web page, is to "describe resources typically used in learning environments (e.g., classroom activities, lesson plans, modules, visualizations, some datasets) for discovery by the Earth system education community" [ADN Framework webpage, 2005]. The content information in a metadata record includes the following categories of elements, among which Educational and Spatial & Temporal (highlighted by the authors) are unique:

  • General (title, language, keywords, subjects, description)
  • Educational (e.g., resource type, grade range)
  • Spatial & temporal
  • Relations (connecting resources to each other)
  • Rights (resource copyright and cost)
  • Technical (URL, browser, platform, plug-ins)
  • Resource catalogers and resource creators
  • Administrative data (ID number, metadata copyright)

(Source: ADN Framework webpage)

The examples cited above show that two approaches are possible for building a metadata framework: 1) establishing a framework before the development of individual schemas and applications, and 2) building a framework based on existing schemas. Regardless of which approach is used, the function of a metadata framework is to provide a suitable environment for the diverse audiences of involved communities.

4.6 Metadata Registry

The purpose of a metadata registry is fairly straightforward: to collect data regarding metadata schemas. Because the reuse of existing metadata terms is essential to achieving interoperability among metadata element sets, the identification of existing terms becomes a prerequisite step in any new metadata schema development process. Thus the presence of a metadata registry application "promotes the wider adoption, standardization and interoperability of metadata by facilitating its discovery, and reuse, across diverse disciplines and communities of practice" [Dublin Core Metadata Registry].

Metadata registries are expected to "provide the means to identify and refer to established schemas and application profiles, potentially including the means for machine mapping among different schemas. In addition, it is expected that such registries will contain, or link to, important controlled vocabularies from which the values of metadata fields can be selected" [Duval et al., 2002]. The preliminary functions of metadata registries include registering, publishing, and managing schemas and application profiles, as well as making them searchable. A registry also provides services for crosslinking and crosswalking among schemas and application profiles (see Figure 10).

Image showing relationship of registry to schemas

Figure 10. A metadata registry in relation to schemas.

The basic components of a metadata registry may include the identifications of data models, elements, element sets, encoding schemes, application profiles, element usage information, and element crosswalks. Despite these common components, each registry usually has a specific scope. The following examples represent four different registry ranges:

  1. Cross-domain and cross-schema registry. For example, UKOLN (UK Office for Library Networking)'s SCHEMAS Registry, now used in the new CORES project, contains several metadata element sets and related documents. Through a web interface, one can search or browse according to agencies, element sets, elements, encoding schemes, application profiles, and element usages that are included in this registry. Currently the registry consists of 12 element sets from 10 institutions [CORES].
  2. Domain-specific, cross-schema registry. For example, UKLON's MEG (Metadata for Education Group) Registry facilitates schema registration within the educational domain[MEG Registry].
  3. Project-specific registry. The European Library (TEL) metadata registry [TEL] was established for the purpose of recording all metadata activities associated with TEL. The registry contains translations of element names in different languages and declares whether the element is repeatable, searchable, and mandatory [Van Veen and Oldroyd, 2004].
  4. Schema-specific registry, such as Dublin Core Metadata Initiative's (DCMI) Registry or Open Data Registry [Dublin Core Metadata Registry], for recording the valid elements within the DC schema. Currently the registry provides details regarding the elements, element refinements, controlled vocabulary terms (DCMI-Type Voc.), and vocabulary and encoding schemas.

As Duval et al. (2002) have pointed out, the importance of the management and disclosure roles of registries will increase as more metadata and application profile schemas are developed.

5. Other Approaches

In the open, networked environment that encompasses multiple user communities using a multitude of standards for description of digital resources, the need for interoperability among metadata schemas is paramount. To enable federated searches and to facilitate metadata management, much effort has been devoted to achieving or improving interoperability among metadata records. As discussed in this article, efforts to improve interoperability can take place at different levels – schema, record, and repository levels.

So far we have discussed methods used by selected projects to achieve interoperability at the schema level. The second part of this article (published in this same issue) summarizes methodologies used at the record level and the repository level. At the record level, approaches widely applied include converting metadata records and reusing and integrating data. At the repository level, metadata harvesting and federated searches benefit from the Open Archives Initiative (OAI) Protocol. Meanwhile, there also exist repositories that support multiple formats without record conversion. Other interesting processes or ideas related to ensuring interoperability at the repository level include aggregation, crosswalking services, value-based mapping for cross-database searching, and value-based co-occurrence mapping. As mentioned earlier, the models discussed in this article are not always mutually exclusive. Sometimes, within a particular project, more than one method may be used.


The authors express their thanks for the help and support of Dr. Theodora Hodges (Berkeley, CA), Katy Ginger (DLESE), Dr. Athena Salaba (Kent State University), and Samantha Nicholson (Kent State University).

Sources and References


(Metadata schemas, application profiles, and registries mentioned in Part I of the article)

Created by DLESE (Digital Library for Earth System Education).
The current framework used in the DLESE Discovery System. It contains information used by the Earth System Education community for resource discovery in learning environments (e.g., classroom activities, curriculum, virtual field trips, etc.).

AGLS (Australian Government Locator Service) Metadata Standard
Maintained by the National Archives of Australia and published as Australian Standard AS 5044 by Standards Australia in December 2002.
A set of nineteen descriptive elements that government departments and agencies can use to improve the visibility and accessibility of their services and information over the Internet. It has been mandated for use by Commonwealth Government agencies.

AVEL (Australasian Virtual Engineering Library) Metadata Set
Maintained by the Australasian Virtual Engineering Library.
A Metadata Set consists of nineteen elements based on Dublin Core. AVEL is a gateway of quality web resources developed collaboratively by a national team through an Australian Research Council (ARC) Research Infrastructure Grant from 1998-2002.

Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Maintained by the Federal Geographic Data Committee (FGDC).
A user-defined or theme-specific profile of the FGDC Content Standard for Digital Geospatial Metadata for the purpose of increasing its utility for documenting biological resources. This standard serves as the metadata content standard for the National Biological Information Infrastructure (NBII).

Categories for the Description of Works of Art (CDWA)
A product of the Art Information Task Force (AITF) funded by the J. Paul Getty Trust; edited by Murtha Baca and Patricia Harpring.
A metadata element set, including 381 categories and subcategories, for describing works of art, architecture, groups of objects, and visual and textual surrogates.

Content Standards for Digital Geospatial Metadata (CSDGM)
Maintained by the Federal Geographic Data Committee (FGDC).
A common set of terminology and definitions for the documentation of digital geospatial data.

CORES Registry
Maintained by UKOLN (UK Office for Library Networking).
A collection of vocabularies and profiles enabling projects and services to declare their usage of standards in schemas based on a common model. The CORES project provides a forum to encourage sharing of metadata semantics.

DC (Dublin Core) Metadata Element Set
Developed and maintained by Dublin Core Metadata Initiative (DCMI).
A U.S. national standard (NISO Z39.85) and international standard (ISO 15836) for cross-domain information resource description.

DC-Education Application Profile
Prepared by the DCMI-Education Application Profile Drafting Committee, a subset of the DCMI-Education Working Group.
An application profile that clarifies the use of the Dublin Core Metadata Element Set in education and training-related applications and projects.

DC-Library Application Profile (DC-Lib)
Prepared by the DCMI-Libraries Application Profile drafting committee, a subset of the DCMI-Libraries Working Group.
A proposed application profile that clarifies the use of the Dublin Core Metadata Element Set in libraries and library-related applications and projects.

Dublin Core Metadata Registry
Developed and maintained by Dublin Core Metadata Initiative (DCMI).
An authoritative source of information about the Dublin Core element set and related vocabularies. Dublin Core Metadata Terms (2005-06-13).

Encoded Archival Description (EAD)
Maintained in the Network Development and MARC Standards Office of the Library of Congress (LC) in partnership with the Society of American Archivists.
A standard for encoding archival finding aids using Extensible Markup Language (XML).

ETD-MS: an Interoperability Metadata Standard for Electronic Theses and Dissertations.
Developed by the Networked Digital Library of Theses and Dissertations (NDLTD).
A standard set of metadata elements based on the Dublin Core used to describe electronic theses and dissertations.

GEM (The Gateway to Educational Materials) Element Set
Maintained by the GEM Consortium.
A set of metadata elements based on the Dublin Core used by GEM members to organize and improve access to their own educational materials.

IMS (Instructional Management Systems) Learning Resource Meta-data Specification
Developed by The IMS Global Learning Consortium.
A standard to promote the adoption of open technical specifications for interoperable learning technology. The IMS Learning Resource Meta-data Information Model 1.2.1 Final Specification is superseded by IEEE Std 1484.12.1 - 2002, IEEE Standard for Learning Object Metadata (LOM).

Learning Object Metadata (LOM)
Developed by the IEEE Learning Technology Standards Committee (LTSC).
A minimal set of attributes needed to enable and facilitate the management, location, and evaluation of learning objects. Learning Objects are defined here as any entity, digital or non-digital, that can be used, re-used or referenced during technology supported learning.

MARC (MAchine-Readable Cataloging) Formats
Developed and maintained by the Library of Congress Network Development and MARC Standards Office.
A set of standards for encoding cataloging data in online library catalogs. MARC provides the mechanism by which computers exchange, use, and interpret bibliographic information. The LC MARC became known as USMARC in the 1980s, and in 1999, it was merged with CANMARC (Canadian MARC) to become MARC 21.

Developed and maintained by the Library of Congress Network Development and MARC Standards Office.
A subset of the data elements in the complete MARC 21 Format for Bibliographic Data.

Developed and maintained by the Library of Congress Network Development and MARC Standards Office.
A framework for working with MARC data in a XML environment.

MEG Registry (Registry of MEG-related schemas)
Maintained by UKOLN (UK Office for Library Networking).
A schema registration within the educational domain.

OAIS (Reference Model for an Open Archival Information System)
Developed by ISO Consultative Committee for Space Data Systems (CCSDS) Panel 2.
A technical recommendation for use in developing a broader consensus on what is required for an archive to provide permanent, or indefinite long-term, preservation of digital information.

SCHEMAS Registry – Application Profiles
Maintained by UKOLN (UK Office for Library Networking).
A collection of metadata element sets as well as activity reports that describe and comment on various metadata related activities and initiatives.

TEI: The Text Encoding Initiative
Developed and maintained by the Text Encoding Initiative Consortium.
An international standard for representing all kinds of literary and linguistic texts for online research and teaching.

TEL (The European Library) Application Profile for Objects
The URL was <>. No longer accessible Dec. 22, 2005.
Developed by the TEL Metadata Working Group. A subset of terms generated from the TEL Metadata Registry.

VRA (Visual Resources Association) Core Categories, 3.0
Developed and maintained by the Visual Resources Association Data Standards Committee.
A metadata element set for creating records describing works of visual culture as well as the images that document them.


ADN Framework webpage. (2005). Available: <>.

Baca, M. Gill, T., Gilliland, A.J., & Woodley, M.S. (2000). Introduction to metadata: pathway to digital information. Online edition 2.1. Glossary. Available: <>.

Baker, T. (2003). DCMI Usage Board review of application profiles. Available: <>.

CC:DA (ALCTS/CCS/Committee on Cataloging: Description and Access). (2000). Task Force on Metadata: Final report, June 16, 2000. Available: <>.

CEN (European Committee for Standardization). (2003). Dublin Core application profile guidelines. CEN Workshop Agreement, CWA 14855. Available: <>.

Cumming, M. et al., (2001). Government Application Profile. A DCMI working draft. Available: <>.

Duval, E., Hodgins, W., Sutton, S. & Weibel, S.L. (2002). Metadata principles and practicalities. D-Lib Magazine, 8(4). Available: <doi:10.1045/april2002-weibel>.

Harpring, P., Woodley, M., Gilliland-Swetland, A., & Baca, M. (Compile). (2000) Metadata standards crosswalks. In: Baca, M. et al. (2000) Introduction to metadata: pathway to digital information. Available: <>.

Heery, R., & Patel, M. (2000). Application profiles: mixing and matching metadata schemas. Ariadne, Issue 25. Available: <>.

Johnston, P. (2003). Metadata and interoperability in a complex world. Ariadne, 37. Available: <>.

Library of Congress, Development and MARC Standards Office. (2003). Development of the Encoded Archival Description DTD. Available: <>.

NISO (National Information Standards Organization). (2004). Understanding metadata. Bethesda, MD: NISO Press. Available: <>.

OCLC/RLG Working Group on Preservation Metadata. (2002). Preservation Metadata and the OAIS Information Model, A Metadata Framework to Support the Preservation of Digital Objects. Available: <>.

St. Pierre, M. & LaPlant, W.P. Jr. (1998). Issues in crosswalking content metadata standards. Bethesda, MD: NISO Press. Available: <>.

Taylor, A. (2004). The Organization of Information. 2nd ed. Westport, CN: Libraries Unlimited.

Tennant, R. (2001). Different paths to interoperability. Library Journal, 126(3):118-119.

Van Veen, T. & Oldroyd, B. (2004). Search and Retrieval in The European Library, a new approach. D-Lib Magazine, 10(2). Available: <doi:10.1045/february2004-vanveen>.

Yao, B., Zhang, L., Yu, Y., & Miao S. (2004). Rare materials descriptive metadata standard: Its design and implementation. Available: <>.

Zeng, M.L. (2001). Supporting metadata interoperability: trends and issues. In: C.C. Chen (Ed.): Global digital library development in the new millennium. Beijing: Tsinghua University Press. pp. 405-412.

Zeng, M.L., Zhang, F.J., & Zhang, X. (2003). Metadata standards at Internet arena. Journal of Library Science in China, 29(4):10-14.

Zeng, M.L. & Chan, L.M. (2004). Trends and issues in establishing interoperability among knowledge organization systems. Journal of the American Society for Information Science and Technology (JASIST) 55(5): 377 – 395

Zeng, M.L., & Xiao, L. (2001). Mapping metadata elements of different format. E-Libraries 2001, Proceedings, May 15-17, 2001, New York: 91-99. Medford, NJ: Information Today, Inc.

Zhang, X. (2004). Tutorial on Metadata. In: Tutorials, 7th International Conference of Asian Digital Libraries (ICADL). December 13-17, 2004, Shanghai China: 107-136. Printed by Shanghai Jiaotong University Library.

Copyright © 2006 Lois Mai Chan and Marcia Lei Zeng

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