Data Interpretation: Methods and Tools

Lies, Damned Lies and Visualizations – will Metadata and Paradata be a Solution or a Curse?

by Martin J. Turner

Visualizations have the immense power to convince and illustrate and at times enable users to gain a higher level of insight and inspiration. Based upon the massive amount of brain power within the human visual system, constituting about one third of the total brain size, visualizations have been shown to be one of the best and sometimes the only way of conveying a huge amount of data in a short period of time. Their use has been proved on countless examples, but they can also confuse, deceive and even at times lie. These deceptions can be both accidental and at times throughout history possibly deliberate. This paper introduces scientific visualisation in relation to neurology and psychology of vision; using the example of optical illusion, basic processes of human visual perception, such as assimilation and contrast, are explained.

It is said that a picture describes a thousand words but, as W. Terry Hewitt observed, a good visualization requires a thousand words to describe it. When teaching good scientific visualization techniques a common tool used is to describe a seminal publication by Al Globus and Eric Raible that teaches the opposite: ‘14 Ways to Say Nothing with Scientific Visualization’.1

The tension between these two contrasting approaches to presentation of information and their effect on intellectual transparency of visualisation, are discussed, in the context of three philosophies that have emerged in recent decades: the role of e-science allowing for the creation of tools for metadata to be connected with both outputs and source data; the development of the ideas of the Semantic Web as Tim Berners-Lee’s vision; and the construction of ontology description including ideas for visualizations.

1. Al Globus and Eric Raible, ‘14 Ways to Say Nothing with Scientific Visualization’, IEEE Computer, 27/7 (1994): 86-88.

Different versions of visualizations for the same data flow

Plate 12. 1 Fourteen different versions of visualizations for the same data flow field (Source: Mary J. McDerby, Introduction to Scientific Visualization, Training notes (Manchester: University of Manchester Research Computing Services, 2007). Reproduced with kind permission.

Two Kanizsa triangle illusions

Figure 12.1 Two Kanizsa triangle illusions © Martin Turner based on an idea derived from: Peter K. Kaiser, The Joy of Visual Perception: A Web Book, 2006-2009, see Chapter 3: Fun things in vision.


1 Response to 12

  1. Pingback: Paradata and Transparency in Virtual Heritage | PARADATA

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