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A Framework for Metadata Creation Tools.

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Title: A Framework for Metadata Creation Tools.
Authors: Malaxa, Valentina1 malaxa@acm.org, Douglas, Ian2 idouglas@lsi.fsu.edu
Source: Interdisciplinary Journal of Knowledge & Learning Objects. 2005, Vol. 1, p151-162. 12p. 2 Color Photographs, 4 Diagrams, 1 Chart.
Subject Terms: *Metadata, Metadatabases, Industrial productivity, Data recovery, Prototypes, Production standards
Abstract: Metadata is an increasingly important aspect of resource discovery. Good metadata has the potential to increase discovery and reuse and to facilitate interoperability of digital assets. Using the domain of learning objects, issues associated with the application of metadata standards and the challenges in metadata creation are examined. A framework for customizable metadata authoring that addresses the issues and challenges is described. The framework consists of flexible metadata schema, metadata schema views, templates, collaborative metadata editing, contextual help, andan effective interface component selection. A prototype implementation of the framework, CLOMAT (Customizable Learning Objects Metadata Authoring Tool), is used to illustrate the framework in operation. An initial evaluation of this prototype indicates substantial productivity gains over conventional metadata creation tools. [ABSTRACT FROM AUTHOR]
Copyright of Interdisciplinary Journal of Knowledge & Learning Objects is the property of Informing Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: A Framework for Metadata Creation Tools.
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  Data: <searchLink fieldCode="AR" term="%22Malaxa%2C+Valentina%22">Malaxa, Valentina</searchLink><relatesTo>1</relatesTo><i> malaxa@acm.org</i><br /><searchLink fieldCode="AR" term="%22Douglas%2C+Ian%22">Douglas, Ian</searchLink><relatesTo>2</relatesTo><i> idouglas@lsi.fsu.edu</i>
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  Data: *<searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Metadatabases%22">Metadatabases</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+productivity%22">Industrial productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Data+recovery%22">Data recovery</searchLink><br /><searchLink fieldCode="DE" term="%22Prototypes%22">Prototypes</searchLink><br /><searchLink fieldCode="DE" term="%22Production+standards%22">Production standards</searchLink>
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  Data: Metadata is an increasingly important aspect of resource discovery. Good metadata has the potential to increase discovery and reuse and to facilitate interoperability of digital assets. Using the domain of learning objects, issues associated with the application of metadata standards and the challenges in metadata creation are examined. A framework for customizable metadata authoring that addresses the issues and challenges is described. The framework consists of flexible metadata schema, metadata schema views, templates, collaborative metadata editing, contextual help, andan effective interface component selection. A prototype implementation of the framework, CLOMAT (Customizable Learning Objects Metadata Authoring Tool), is used to illustrate the framework in operation. An initial evaluation of this prototype indicates substantial productivity gains over conventional metadata creation tools. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Interdisciplinary Journal of Knowledge & Learning Objects is the property of Informing Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Data recovery
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      – SubjectFull: Production standards
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