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Current Opinion in Biotechnology, 2000
Data visualization has developed in several directions: theoretical; methodological; and in new application areas. Advances include the development of a grammar of graphics, deeper understanding of human perception and implications for graphical layout, and better approaches to visualizing multi-dimensional data and large data sets.
R D, Meyer, D, Cook
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Data visualization has developed in several directions: theoretical; methodological; and in new application areas. Advances include the development of a grammar of graphics, deeper understanding of human perception and implications for graphical layout, and better approaches to visualizing multi-dimensional data and large data sets.
R D, Meyer, D, Cook
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Abstract This chapter emphasizes the crucial importance of data visualization in improving the comprehension of social media data. By blending design aesthetics with cognitive psychology, data visualization transforms complex datasets into intuitive visual representations, making insights more accessible and actionable.
Stephen M. Croucher +2 more
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Stephen M. Croucher +2 more
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American Journal of Ophthalmology, 1996
We develop a prediction model for classifying an eye according to glaucoma status based on its visual field. We develop measures of both diffuse and localized defects in the visual field as potential predictors of glaucoma. To identify predictors of abnormal fields, we must describe the variability in the fields of normal eyes, hence we first model the
S, Hilton, J, Katz, S, Zeger
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We develop a prediction model for classifying an eye according to glaucoma status based on its visual field. We develop measures of both diffuse and localized defects in the visual field as potential predictors of glaucoma. To identify predictors of abnormal fields, we must describe the variability in the fields of normal eyes, hence we first model the
S, Hilton, J, Katz, S, Zeger
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Statistics in Medicine, 2003
AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
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AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
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2012
When thinking about what constitutes “data” in research, interview transcripts, observational checklists, or field notes are often the first to come to mind. However, those in social science and educational fields still manage to overlook the rich potential of visual data sources.
Faith Agostinone-Wilson, Sheri R. Klein
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When thinking about what constitutes “data” in research, interview transcripts, observational checklists, or field notes are often the first to come to mind. However, those in social science and educational fields still manage to overlook the rich potential of visual data sources.
Faith Agostinone-Wilson, Sheri R. Klein
openaire +1 more source