Academic journal article Studies in Art Education

Histochemical Seeing: Scientific Visualization and Art Education

Academic journal article Studies in Art Education

Histochemical Seeing: Scientific Visualization and Art Education

Article excerpt

What are the capacities of visual arts curricula to engage learning within narrow frameworks of overly "scientistic" standards (Lather, 2007)? With growing emphasis in schools under STEM initiatives and evidence- based standards, the possible cross-pollination of effects that art education may have on a science-centric education may be a lifeline to budgets and relevancy. Yet, focal points to ponder these possibilities illuminate not only what art education has to offer, but also, what contradictions in scientific visualization and representation offer to art educators. Seeing in science can be just as much a cultural practice as seeing in art.

Take for instance the laboratory practice of histochemistry and histochemists' relationship to graphic design software. As the science of preparing cell material in order to observe its composition, histochemists use Photoshop in sample preparation to further augment visualizing cellular structures through methods that, for example, utilize qualities intrinsic to digital images such as saturation and hue (Lehr, Van der Loss, Teeling & Gown, 1999). This process has been called an "art form" because histochemists select methods from a variety of approaches to get the best results: manipulations of cellular material that make visible the agential substance or structure that is being sought out (Heidcamp,1 995, para 2). The decision matrix for histochemists in visualizing cell samples indicates that the same tissue sample can be manipulated in different ways to illicit different visibilities related to different diagnostic purposes. For example, in determining liver disease, methods of both analog staining of the tissue and quantification of the area of fibrous tissues are determined within the digital image through what is essentially "pixelcounting" (Matkowskyj, Schonfeld, & Benya, 2000, p. 303). Photoshop is used to illicit visibility and quantification, but these efforts take place in Photoshop and not under the microscope.

Just as James Elkins (2007) surveyed visualization across the university to understand the complexity of the visual studies field, the art form of histochemistry provides insight on pedagogies of visual culture through our mutually complex relationship with digital image manipulation. Many histochemists agree that Photoshop is a useful tool in practices of scientific visualization because of its capacity to objectify image analysis through quantification by automating adjustments so that they can be applied without subjective interjection (Dahab, Kheriza, El-Beltagi, Fouda, & Sharaf El-Din, 2004). Ironically, this very trust in automation makes art educators distrustful in the ways that Photoshop may do too much in producing student art. As Taylor and Carpenter (2007) stated, "We must be wary of the allure of the spectacular and superficial qualities of digital media at the expense of personal, cultural, social, and global content" (p. 89). With a similar suspicion of the spectacular, Mike Rossner and Kenneth Yamada (2004), as the managing editor and editor respectively of The Journal of Cell Biology, stated, "It's so easy with Photoshop"(p. 11). Art educators and histochemists share a distrust in the "allure" of digital media such as Photoshop, but our respective reasons are quite different.

Histochemists question Photoshop's participation in making scientific visualizations as an ethical question of good scientific method. For histochemists, this ease of digital image manipulation has translated into a"temptation"that ultimately "constitute^] inappropriate changes to youroriginal data, and making such changes can be classified as scientific misconduct" (Rossner & Yamada, 2004, p. 11). The editors propose that good science relies on good data, and that manipulation is an intrusion on the objectivity that is a hallmark of good data. For Rossner and Yamada (2004), "Creating a result is worse than making weak data look better" (p. 11). …

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