as a pristine reflection of ‘reality’ but as the residue from a confused field where electronic noise, detector defects, ambient radiation, and cloudy skies mingle indiscriminately with the signal from a source object. The processed image is often considered the more accurate and ‘natural’ rendering” (“Lab” 70).37 The impetus for “cleaning up” data, then, derives from larger habits of scientific visualization, and also contributes to further encouragement of interaction.
To filter data composing the image, STM researchers can use multiple techniques that further structure users’ engagement with the data. For example, the data might reflect “drift” as the sample shifts over the time it takes for the raster to scan the surface: researchers may correct for drift, and then need to crop the picture.38 Indeed, filtering is so important that scientists Sutter et al. have presented a way to filter the image at the level of data recording, through using a semiconductor STM tip that limits electrons in certain energy ranges even before getting to the image form (166101). In this case, scientists further incorporate the imaging software within the experimental apparatus, and blend data gathering and image processing. Alternatively, a researcher might use filters to enhance the contrast between light and dark, or to smooth out the contrast between different sections of the sample to see details. As one scientist explains,
[I]n terms of daily usage, we generally . . . use other image manipulation techniques like taking the derivative of the surface. So then if you have a step [a point at which two uneven planes on the surface join like a stair step or terrace], if you take the derivative of the step, it just shows as a spike where the step is. So essentially, the contrast associated with having two different terraces at different heights goes away and so now those terraces appear like they’re at the same height.39
Filtering techniques allow researchers to sort through data in order to begin interpreting the data: the researcher quoted above continues, stating, “these are actually the terraces, the same terraces we saw before, but here they’re all a uniform gray color now. And now you see that these patches, which is actually what we’re studying, you can clearly make out what actually turns out to be the atomic resolution in the patches.”40 Other filtering techniques include using Fourier transforms that show the frequency range of the data, helping to make the data measurable. Another researcher explains, “So if you look at a silver atom lattice [the structure the atoms create], I can get the periodicity of that [by taking the Fourier transform], take the inverse of that, and it gets me back to real space, and it will tell me that my lattice spacing is five Ångstroms.”41 These and other techniques allow researchers to highlight what information they consider important or to focus on more specific details such as the size of phenomena, for example. As researchers engage in highlighting data, they change how the image looks, and yet do not alter the data set. STM users may even process images further for cover slides for presentations, journal cover images, or for other scientific imaging contests beyond scientific papers.42
During the imaging process, the researcher also draws on other judgments and experiences so that the images created are, as Stoll comments, “aesthetically pleasing and informative and convincing” (76). Deciding to use color demonstrates some of the dynamics involved. False color has become a component of quite a few STM images, especially those appearing on journal covers and web sites (see Chapter 3 for more on color; also see Hennig, “Changes”). Many researchers apply color to highlight differences among pixel values. For example, researchers can highlight the three-dimensional appearance of the surface through assigning color- or gray-scale values to the various heights; researchers can also simulate illumination to introduce shadows or shading (Stoll 72). About the color choices in one image, one scientist explains,
It [color] certainly can help to clarify the presentation because you can accentuate contrast in regions . . . which contain the point you’re trying to make rather than have the reader be distracted by contrast related to things that you aren’t worried about right now . . . . [In this image, for example,] you see all these lines here for atomic steps on the surface. So, if you look at that in black and white, your colors, your gray-scale has to be stretched to accommodate all those steps, and these steps are actually larger than any of the usual features on the surface. So some kind of stretching of the scale of the image has to be done in order to see the fine features that generally you’re interested in. . . . So, if you look at the image just in gray-scale, it doesn’t appear so clear because all the contrast is taken out by the steps rather than by the little bumps on the surface that you want to study.43
Adding color to STM images helps viewers grasp slight variations in value; Russ explains that human eyes can only pick out about twenty to forty shades of gray in an image, but can differentiate between hundreds of colors (35). Russ also observes that colors help humans verbally refer to parts of an image through different colors, as opposed to different shades of gray (35). Color allows viewers to not only distinguish differences in value, but also to import the value differences into language—another medium.
While added color helps viewers verbally and visually distinguish an image’s particular characteristics, and so eases the entry of the presented value into scientific discourse, the researcher cannot rely on a color correspondence or order while choosing colors to use. Edward Tufte explains, “Despite our experiences with the spectrum in science books and rainbows, the mind’s eye does not readily give a visual ordering to colors, except possibly for red to reflect higher levels than other colors” (154). Therefore, adding color to an image is dependent on the user’s or group’s decisions and previous associations with color. In his study of the digital image production of astronomers, Lynch explains that the dependence on individual or group associations to assign meanings to colors can lead to the user’s reinforcement of her or his expectations of the sample through color choice: “Color becomes iconic when used for color enhancement or when signifying intensity or red shift. That is, the code is selected intuitively to suggest properties the object should have” (“Lab” 71).
In images of the nanoscale, the question of whether color correspondences exist is unsettled. Science studies scholars Arie Rip and Martin Ruivenkamp observe that color choices are not fully determined in the nano-researcher community: Some researchers see some colors as commonly used for certain features or attributes and cite, for example, the default colors in imaging software (29). Figuring out color schemes involves the researcher’s preferences. One scientist I interviewed commented, “[Y]eah, so there’s some playing that goes on with false coloring and just looking at what highlights the features that you want to show. Those [colors] aren’t—of course, those aren’t real.”44 Another commented that he would try out different colors and would make a file of those images, images he would later return to, and choose a version he liked best.45 Using color, then, engages STM image-creators in another set of interactions to determine how to best present the data to users. Like other filtering techniques, choosing color extends the process of interacting through the GUI; choosing color engages the user in practices that also include scientific and extra-scientific cultural elements and conventions. By changing the appearance of the data and the image, chosen colors can also affect how data is read.46 As color choices are not based on a predetermined order whose meaning viewers will immediately understand, but instead are based on previous (and most likely unexamined) color associations as well as the experience of color in the image, color choices affect the viewer’s response to the image and his or her perceptions of what the image depicts.
Habituated Interactions: Coordinated Dynamic Effects
The operating dynamics of electron tunneling, movement in x, y, and z directions, and GUI use are separately identifiable in STM operations as well as in the dynamics of other visualization technologies. In the STM, however, the coordinated contributions of electron tunneling, movement in x, y, and z directions, and GUI use allow an intensification of the kinds of multi-directional interactivity and manipulability afforded individually by each dynamic. In the process of operating, these three dynamics constitute an instrument that encourages manipulability and interaction—interaction with and through images and data (as one might expect with GUI) , but also with atomic phenomena. As part of the coordinated dynamic of interaction, the user’s
