Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Windhager, Florian; Salisu, Saminu; Mayr, Eva

doi:10.3390/informatics6030029

Cited by 29 publications

(25 citation statements)

References 43 publications

(62 reference statements)

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“…They found that fuzziness, location, value, arrangement, size, and transparency were rated as highly intuitive. The theory of visual semiotics of uncertainty [14] has inspired numerous applications of metaphoric uncertainty visualization from cultural collections [74] to educational reporting systems [75].…”

Section: Visual Semiotics Of Uncertaintymentioning

confidence: 99%

Uncertainty Visualization

Padilla¹,

Kay²,

Hullman³

2020

Preprint

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While uncertainty is present in most data analysis pipelines, reasoning with uncertainty is challenging for novices and experts alike. Fortunately, researchers are making significant advancements in the communication of uncertainty. In this chapter, we detail new visualization methods and emerging cognitive theories that describe how we reason with visual representations of uncertainty. We describe the best practices in uncertainty visualization and the psychology behind how each approach supports viewers' judgments. This chapter begins with a brief overview of conventional and state-of-the-art uncertainty visualization techniques. Then we take an in-depth look at the pros and cons of each technique using cognitive theories that describe why and how the mind processes different types of uncertainty information.

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Section: Visual Semiotics Of Uncertaintymentioning

confidence: 99%

Uncertainty Visualization

Padilla¹,

Kay²,

Hullman³

2020

Preprint

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“…Law [67] reviewed website evaluation models and pointed out that in recent years, website audits have most frequently focused on content quality. On the other hand, Windhager et al [68] demonstrated that the quality of data and the quality of their visualisation were of significance in the presentation of large data sets.…”

Section: Research Into Website Qualitymentioning

confidence: 99%

Aggregated Indices in Website Quality Assessment

Król

Zdonek

2020

Future Internet

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Website users have increasingly high expectations regarding website quality, starting from performance and ending up with the content. This article provides a list and characteristics of selected website quality indices and testing applications that are available free of charge. Aggregated website quality indices were characterised based on a review of various source materials, including the academic literature and Internet materials. Aggregated website quality indices are usually developed with a less specialised user (customer) searching for descriptive information in mind. Their presentation is focused on aesthetic sensations. Most frequently, their values are expressed in points or percent. Many of these indices appear to be of little substantive value, as they present approximate, estimated values. These indices, however, are of great marketing value instead. Specific (“single”) indices are of a specialised nature. They are more difficult to interpret and address the subtle aspects of website and web application functioning. They offer great value to designers and software developers. They indicate critical spots which affect the website quality. Most of them are expressed precisely, often up to two or three decimal places, in specific units. Algorithmic tests for website quality, whose results are presented using indices, enable a reduction in the cost intensiveness of tests as well as an increase in their number and frequency, as the tests are repetitive and their number is not limited. What is more, they allow the results to be compared.

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“…Selection of an appropriate number of uncertainty levels is also of importance since discriminability of different degrees of uncertainty is impeded with the number of uncertainty degrees represented (Erev & Cohen, 1990). Windhager et al (2019) discuss the trade-off between the omission of uncertainty information at different levels leading to the deception of the recipients and visual complexity that overstrains the cognitive capacities of the recipients. In line with this, Correll et al (2018) showed that when using color scales to represent values or uncertainty, performance accuracy in an identification task is better with a discrete than with a continuous scale, indicating a trade-off between quantization error with using discrete scales of inherently continuous variables and perception error with continuous scales.…”

Section: Processing Visual Expressions Of Uncertaintymentioning

confidence: 99%

How Do We Deal with Uncertain Information? Effects of Verbal and Visual Expressions of Uncertainty on Learning

et al. 2022

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Based on the knowledge generation model for visual analytics including uncertainty propagation and human trust building (Sacha et al. 2016), the cognitive theory of multimedia learning (Mayer, 2014), the multimedia principle (Butcher, 2014), and previous studies on the effects of different uncertainty visualization styles, an integrated theoretical approach is proposed to examine the influence of different degrees of information uncertainty and different uncertainty visualization styles on processing pictures of two archeological reconstructions with accompanying audio explanations presented in a multimedia learning environment. A 4 × 3 design with condition (without uncertainty visualization vs. stop light colors vs. geometric contrast vs. both uncertainty visualizations) as the between-subjects factor and uncertainty value (uncertain vs. medium vs. certain) as the within-subject factor was used. The results showed that appearance of certain content, its uncertainty values, and their verbal scientific justifications were remembered better than uncertain ones. Furthermore, stop light colors enhanced the memory of uncertainty values compared to no uncertainty visualization and were better understood, discriminated, and transferred than geometric contrast. Geometric contrast decreased the memory of the appearance of uncertain architectural elements compared to no uncertainty visualization and was better than stop light colors regarding the memory of the appearance of certain architectural elements. The study integrates and extends existing theories by showing that certain contents are processed with higher priority than uncertain contents and that the multimedia effect is also valid for metainformation such as the uncertainties of contents. Finally, recommendations for designing learning material including uncertainty visualizations are given.

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Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Cited by 29 publications

References 43 publications

Uncertainty Visualization

Uncertainty Visualization

Aggregated Indices in Website Quality Assessment

How Do We Deal with Uncertain Information? Effects of Verbal and Visual Expressions of Uncertainty on Learning

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