Search (364 results, page 19 of 19)

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Date

16.11.2017 13:30:22

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Date

11. 3.2001 17:10:22

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Abstract

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have "scheme harmonization" (compatibility and interoperability with related schemes) as an objective; schemes with the objective "abstraction" (a conceptual model exists separate from the technical implementation) also have the objective "sufficiency" (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective "data publication" do not have the objective "element refinement." The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes.

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Abstract

To Classify is (according to Webster's) "to distribute into classes; to arrange according to a system; to arrange in sets according to some method founded on common properties or characters." A model of classification is a type or category or (excuse the recursive definition) a class of classification "system" as mentioned in Webster's definition. One employs a classification model to implement a specific classification system. (E.g. we employ the hierarchical classification model to implement the Dewey Decimal System) An effective classification model must represent both the commonality (Webster's "common properties"), and also the differences among the items being classified. The commonality of each category or class defines a test to determine which items belong to the set that class represents. The relationships among the classes define the variability among the sets that the classification model can represent. Therefore, a classification model is more than an enumeration or other simple listing of the names of its classes. Our purpose in employing classification models is to build metadata systems that represent and manage knowledge, so that users of these systems we build can: quickly and accurately define (the commonality of) what knowledge they require, allowing the user great flexibility in how that desire is described; be presented existing information assets that best match the stated requirements; distinguish (the variability) among the candidates to determine their best choice(s), without actually having to examine the individual items themselves; retrieve the knowledge they need The MetaData model we present is Contextual Classification. It is a synthesis of several traditional metadata models, including controlled keyword indices, hierarchical classification, attribute value systems, Faceted Classification, and Evolutionary Faceted Classification. Research into building on line library systems of software and software documentation (Frakes and Pole, 19921 and Pole 19962) has shown the need and viability of combining the strengths, and minimizing the weaknesses of multiple metadata models in the development of information systems. The MetaData Object Manager (M.O.M.), a MetaData Warehouse (MDW) and editorial work flow system developed for the Thomson Financial Publishing Group, builds on this earlier research. From controlled keyword systems we borrow the idea of representing commonalties by defining formally defined subject areas or categories of information, which sets are represented by these categories names. From hierarchical classification, we borrow the concept of relating these categories and classes to each other to represent the variability in a collection of information sources. From attribute value we borrow the concept that each information source can be described in different ways, each in respect to the attribute of the information being described. From Faceted Classification we borrow the concept of relating the classes themselves into sets of classes, which a faceted classification system would describe as facets of terms. In this paper we will define the Contextual Classification model, comparing it to the traditional metadata models from which it has evolved. Using the MOM as an example, we will then discuss both the use of Contextual Classification is developing this system, and the organizational, performance and reliability