The collaborative ‹Big Science’ approach prevalent in physics during the mid- and late-20th century is becoming more common in the life sciences. Often computationally mediated, these collaborations challenge researchers’ trust practices. Focusing on the visualisations that are often at the heart of this form of scientific practice, the paper proposes that the aesthetic aspects of these visualisations are themselves a way of securing trust. Kant’s account of aesthetic judgements in the Third Critique is drawn upon in order to show (...) that the image-building capability of imagination, and the sensus communis, both of which are integral parts of aesthetic experience, play an important role in building and sustaining community in these forms of science. Kant’s theory shows that the aesthetic appeal of scientific visualisations is not isolated from two other dimensions of the visualisations: the cognitive-epistemic, aesthetic-stylistic and interpersonal dimensions, and that in virtue of these inter-relationships, visualisations contribute to building up the intersubjectively shared framework of agreement which is basic for trust. (shrink)

‘Visualisations play an important role in science’, this seems to be an uncontroversial statement today. Scientists not only use visual representations as means to communicate their research results in publications or talks, but also often as surrogates for their objects of interest during the process of research. Thus, we can make a distinction between two contexts of usage here, namely the explanatory and the exploratory context. The focus of this paper is on the latter one. Obviously, using visualisations as surrogates (...) for their objects of depiction presupposes the assumption that the former can tell us something relevant about the latter. Thus, a particular referential relation between object and image has to be assumed that can transfer the relevant information. Furthermore, as science is a collaborative enterprise – a social activity – this information has to be intersubjectively accessible and stable. Nonetheless, philosophers of science still quarrel about the epistemic and ontological status of such visualisations. After all, they are means to visualise theoretical entities, such as the Higgs Boson, i.e. entities that are principally not observable with the unaided eye. Especially the significant reliance on information technology devices to access this world of the unobservable provokes a lot of suspicion with regard to the referential status of such visualisations. In this sense, quite a few philosophers adopt social constructivism as an explanatory hypothesis. A particular image is constructed with the aid of instruments and theoretical assumptions; hence it does not refer to an entity outside this system. In this paper, I will critically analyse this assumption and try to argue for an alternative point of view. (shrink)

Taking the visual appeal of the ‘bell curve’ as an example, this paper discusses in how far the availability of quantitative approaches (here: statistics) that comes along with representational standards immediately affects qualitative concepts of scientific reasoning (here: normality). Within the realm of this paper I shall focus on the relationship between normality, as defined by scientific enterprise, and normativity, that result out of the very processes of standardisation itself. Two hypotheses are guiding this analysis: (1) normality, as (...) it is defined by the natural and the life sciences, must be regarded as an ontological, but epistemological important fiction and (2) standardised, canonical visualisations (such as the ‘bell curve’) impact on scientific thinking and reasoning to a significant degree. I restrict my analysis to the epistemological function of scientific representations of data: This means identifying key strategies of producing graphs and images in scientific practice. As a starting point, it is crucial to evaluate to what degree graphs and images could be seen as guiding scientific reasoning itself, for instance in attributing to them a certain epistemological function within a given field of research. (shrink)

The author of the present paper argues that while trying to explain the institutional success of the science and its broad social impact, it is worth throwing aside the arguments concerning the universal traits of human nature, changes in the human mentality, or transformation of the culture and civilization, such as the development of capitalism or bureaucratic power. In the 16th century no new man emerged, and no mutants with overgrown brains work in modern laboratories. So one must also reject (...) the Great Divide between the cultures of the scientific and pre-scientific and replace it with multiple, uncertain and unexpected ‘not-so-great divides’, which can be described in meticulous anthropological studies. Although the achievements of science are certainly spectacular, and the gap between scientific practice and other areas of activity is so obvious, this does not mean that one must look for the “great” reasons behind this situation. One should rather focus on quite down-to-earth practices and tools used by scientists. A significant part of their activities can be described by referring to the craft of writing, reading and transforming of various types of inscriptions , and broadly understood visualization – their combining, performing, interpreting, confronting, comparing, shifting, shuffling etc. The important role of these tools and methods is especially visible in situations of scientific controversy. It is so because scientific controversies are won by the one able to muster on the spot the largest number of well aligned and faithful allies, and the technology of writing, printing and visualizing play a special role in mobilizing them. These are necessary to ensure that certain factors can be mobile – easy to move from place to place, and yet, immutable – not undergoing deformation as a result of the movement. This way, scientists are able to not only diffuse different types of factors relevant to the dispute and the process of constituting science, but also concentrate them in the centers of calculation, where, through accumulation, one can take actions not available elsewhere. (shrink)

This paper argues that spacetime visualisability is not a necessary condition for the intelligibility of theories in physics. Visualisation can be an important tool for rendering a theory intelligible, but it is by no means a sine qua non. The paper examines the historical transition from classical to quantum physics, and analyses the role of visualisability and its relation to intelligibility. On the basis of this historical analysis, an alternative conception of the intelligibility of scientific theories is proposed, (...) based on Heisenberg 's reinterpretation of the notion of Anschaulichkeit. (shrink)

This book critically reflects upon how images are mobilised within certain knowledge traditions, beyond the established categories of art, scientific visualisations and religious images. Thinking through and with images across ages, the authors seek to expand our understanding of the relationship between the visual and the epistemic.

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...) is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

Without doubt, there is a widespread usage of visualisations in science. However, what exactly the _epistemic status_ of these visual representations in science may be remains an open question. In the following, I will argue that at least some scientific visualisations are indispensible for our cognitive processes. My thesis will be that, with regard to the activity of _learning_, visual representations are of relevance in the sense of contributing to the aim of _scientific_ _understanding_. Taking into account that understanding (...) can be regarded as an epistemic desideratum in its own right, I will argue that, at least in some instances, no understanding can be achieved without the aid of visualisations. Consequently, they are of crucial importance in this process. Moreover, to support this thesis we will make use of some findings in educational psychology. (shrink)

Philosophers have traditionally conceived of discovery in terms of internal cognitive acts. Close consideration of Rosalind Franklin's role in the discovery of the DNA double helix, however, reveals some problems with this traditional conception. This article argues that defining discovery in terms of mental operations entails problematic conclusions and excludes acts that should fall within the domain of discovery. It proposes that discovery be expanded to include external acts of making visible. Doing so allows for a reevaluation of Franklin's role (...) in the discovery of the structure of DNA. (shrink)

The authors consider the relationship between knowledge and image, though multi-faceted, to be one of reciprocal dependence. But how do images carry and convey knowledge? The ambiguities of images means that interpretations do not necessarily follow the intention of the image producers. Through an array of different cases, the chapters critically reflect upon how images are mobilised and used in different knowledge practices, within certain knowledge traditions, in different historical periods. They question what we take for granted, what seems evident, (...) what goes without saying. This approach spans across established categories such as «scientific imaging», «religious images» and «artworks», and considers how images may contribute meaning across such categories. (shrink)

Without doubt, there is a great diversity of scientific images both with regard to their appearances and their functions. Diagrams, photographs, drawings, etc. serve as evidence in publications, as eye-catchers in presentations, as surrogates for the research object in scientific reasoning. This fact has been highlighted by Stephen M. Downes who takes this diversity as a reason to argue against a unifying representation-based account of how visualisations play their epistemic role in science. In the following paper, I will (...) suggest an alternative explanation of the diversity of scientific images. This account refers to processes which are caused by the social setting of science. What exactly is meant by this, I will spell out with the aid of Ludwik Fleck’s theory of the social mechanisms of scientific communication. (shrink)

This study will mainly investigate a semiotic theory of the production and functions of visualisation and image in scientific literature, especially concerningobservational astrophysics, as well as theoretical physics, and will also mention the physiology of movement dealing with chronophotography.

Only a decade ago, the topic of scientific understanding remained one that philosophers of science largely avoided. Earlier discussions by Hempel and others had branded scientific understanding a mere subjective state or feeling, one to be studied by psychologists perhaps, but not an important or fruitful focus for philosophers of science. Even as scientific explanation became a central topic in philosophy of science, little attention was given to understanding. Over the last decade, however, this situation has changed. (...) Analyses of scientific understanding that do not treat it as a subjective state or feeling have been offered and debated, and both the epistemic value and the pitfalls of purported psychological .. (shrink)

The act of understanding is at the heart of all scientific activity; without it any ostensibly scientific activity is as sterile as that of a high school student substituting numbers into a formula. Ordinary language often uses visual metaphors in connection with understanding. When we finally understand what someone is trying to point out to us, we exclaim: “I see!” When someone really understands a subject matter, we say that she has “insight”. There appears to be a link (...) between visualization and understanding, and between visualizability and intelligibility. This applies in science no less than in daily life: visualization is regarded as a useful means of achieving scientific understanding, even in the .. (shrink)

Almost everyone has an intuitive notion of what a random number is. For example, consider these two series of binary digits: 01010101010101010101 01101100110111100010 The first is obviously constructed according to a simple rule; it consists of the number 01 repeated ten times. If one were asked to speculate on how the series might continue, one could predict with considerable confidence that the next two digits would be 0 and 1. Inspection of the second series of digits yields no such comprehensive (...) pattern. There is no obvious rule governing the formation of the number, and there is no rational way to guess the succeeding digits. The arrangement seems haphazard; in other words, the sequence appears to be a random assortment of 0's and 1's. (shrink)

What could be more certain than the fact that 2 plus 2 equals 4? Since the time of the ancient Greeks mathematicians have believed there is little---if anything---as unequivocal as a proved theorem. In fact, mathematical statements that can be proved true have often been regarded as a more solid foundation for a system of thought than any maxim about morals or even physical objects. The 17th-century German mathematician and philosopher Gottfried Wilhelm Leibniz even envisioned a ``calculus'' of reasoning such (...) that all disputes could one day be settled with the words ``Gentlemen, let us compute!'' By the beginning of this century symbolic logic had progressed to such an extent that the German mathematician David Hilbert declared that all mathematical questions are in principle decidable, and he confidently set out to codify once and for all the methods of mathematical reasoning. (shrink)

Human beings are strange animals. Although evolutionary theory has brilliantly accounted for the features we share with other creatures—from the genetic code that directs the construction of our bodies to the details of how our muscles and neurons work—we still stand out in countless ways. Our brains are exceptionally large, we alone have truly grammatical language, and we alone compose symphonies, drive cars, eat spaghetti with a fork and wonder about the origins of the universe.

As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation of computational (...) and informational visualisations and imaging techniques challenges the philosophy of science to re-think its position on three key distinctions: the qualitative/quantitative distinction, the subjective/objective distinction, and the causal/non-causal distinction. (shrink)

Just before the Scientific Revolution, there was a "Mathematical Revolution", heavily based on geometrical and machine diagrams. The "faculty of imagination" (now called scientific visualization) was developed to allow 3D understanding of planetary motion, human anatomy and the workings of machines. 1543 saw the publication of the heavily geometrical work of Copernicus and Vesalius, as well as the first Italian translation of Euclid.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template (...) is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond. Depending on graph type, raw data can be displayed along with standard measures of central tendency and dispersion. The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization. (shrink)

This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like "basic" vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation (...) that take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition. (shrink)

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.