Search (6 results, page 1 of 1)

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Abstract

This article reviews theoretical and empirical studies on information credibility, with particular questions as to how scholars have conceptualized credibility, which is known as a multifaceted concept with underlying dimensions; how credibility has been operationalized and measured in empirical studies, especially in the web context; what are the important user characteristics that contribute to the variability of web credibility assessment; and how the process of web credibility assessment has been theorized. An agenda for future research on information credibility is also discussed.

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Abstract

This article proposes a method that allows a value-based assessment of metadata quality and construction of a baseline quality model. The method is illustrated on a large-scale, aggregated collection of simple Dublin core metadata records. An analysis of the collection suggests that metadata providers and end users may have different value structures for the same metadata. To promote better use of the metadata collection, value models for metadata in the collection should be made transparent to end users and end users should be allowed to participate in content creation and quality control processes.

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Abstract

One cannot manage information quality (IQ) without first being able to measure it meaningfully and establishing a causal connection between the source of IQ change, the IQ problem types, the types of activities affected, and their implications. In this article we propose a general IQ assessment framework. In contrast to context-specific IQ assessment models, which usually focus on a few variables determined by local needs, our framework consists of comprehensive typologies of IQ problems, related activities, and a taxonomy of IQ dimensions organized in a systematic way based on sound theories and practices. The framework can be used as a knowledge resource and as a guide for developing IQ measurement models for many different settings. The framework was validated and refined by developing specific IQ measurement models for two large-scale collections of two large classes of information objects: Simple Dublin Core records and online encyclopedia articles.

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Abstract

The rapid accumulation of genome annotations, as well as their widespread reuse in clinical and scientific practice, poses new challenges to management of the quality of scientific data. This study contributes towards better understanding of scientists' perceptions of and priorities for data quality and data quality assurance skills needed in genome annotation. This study was guided by a previously developed general framework for assessment of data quality and by a taxonomy of data-quality (DQ) skills, and intended to define context-sensitive models of criteria for data quality and skills for genome annotation. Analysis of the results revealed that genomics scientists recognize specific sets of criteria for quality in the genome-annotation context. Seventeen data quality dimensions were reduced to 5-factor constructs, and 17 relevant skills were grouped into 4-factor constructs. The constructs defined by this study advance the understanding of data quality relationships and are an important contribution to data and information quality research. In addition, the resulting models can serve as valuable resources to genome data curators and administrators for developing data-curation policies and designing DQ-assurance strategies, processes, procedures, and infrastructure. The study's findings may also inform educators in developing data quality assurance curricula and training courses.

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Date

22. 1.2011 13:19:42

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Date

22. 1.2023 18:43:02