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Understanding and comparing distributions of data (e.g., regarding their modes, shapes, or outliers) is a common challenge in many scientific disciplines. Typically, this challenge is addressed using side-by-side comparisons of histograms or density plots. However, comparing multiple density plots is mentally demanding. Uniform histograms often represent distributions imprecisely since missing values, outliers, or modes are hidden by a grouping of equal size. In this paper, a novel type of overview visualization for the comparison of univariate data distributions is presented: AccuStripes (i.e., accumulated stripes) is a new visual metaphor encoding accumulations of data distributions according to adaptive binning using color coded stripes of irregular width. We provide detailed insights about challenges of binning. Specifically, we explore different adaptive binning concepts such as Bayesian Blocks binning and Jenks Natural Breaks binning for the computation of binning boundaries, in terms of their capabilities to represent the datasets as accurately as possible. In addition, we discuss issues arising with the representation of designs for the comparative visualization of distributions: To allow for a comparison of many distributions, their accumulated representations are plotted below each other in a stacked mode. Based on our findings, we propose three different layouts for comparative visualization of multiple distributions. The usefulness of AccuStripes is investigated using a statistical evaluation of the binning methods. Using a similarity metric from cluster analysis, it is shown, which binning method statistically yields the best grouping results. Through a user study we evaluate, which binning strategy visually represents the distribution in the most intuitive form and investigate, which layout allows the user the comparison of many distributions in the most effortless way., Comment: 11 pages, 6 figures

Comparative analysis of multivariate datasets, e.g. of advanced materials regarding the characteristics of internal structures (fibers, pores, etc.), is of crucial importance in various scientific disciplines. Currently domain experts in materials science mostly rely on sequential comparison of data using juxtaposition. Our work assists domain experts to perform detailed comparative analyses of large ensemble data in materials science applications. For this purpose, we developed a comparative visualization framework, that includes a tabular overview and three detailed visualization techniques to provide a holistic view on the similarities in the ensemble. We demonstrate the applicability of our framework on two specific usage scenarios and verify its techniques using a qualitative user study with 12 material experts. The insights gained from our work represent a significant advancement in the field of comparative material analysis of high-dimensional data. Our framework provides experts with a novel perspective on the data and eliminates the need for time-consuming sequential exploration of numerical data., Vision, Modeling, and Visualization, Visual Parameter Space Analysis, 117, 124, Anja Heim, Eduard Gröller, and Christoph Heinzl, CCS Concepts: Human-centered computing --> Visual analytics; Applied computing --> Physical sciences and engineering

In sicherheitskritischen Bereichen wie der Aeronautik, aber auch in anderen Sparten wie der Freizeitindustrie, werden die Weiterentwicklungen entsprechender Produkte massgeblich durch die Verbesserung der eingesetzten Werkstoffe und Materialien geleitet. Um die Zieleigenschaften dieser neuen Materialien zu analysieren, werden durch bildgebende Verfahren wie Röntgencomputertomographie (XCT) Abbildungen der inneren Strukturen angefertigt, die anschließend durch Segmentations- und Quantifizierungsalgorithmen detailiert aufgeschlüsselt werden. Der genaue Aufbau der inneren Strukturen ist für MaterialwissenschaftlerInnen hierbei ausschlaggebend für die Charakterisierung von Werkstoffen und ein Vergleich mehrerer Materialkandidaten anhand ihrer Charakteristika somit unumgänglich für die Untersuchung verschiedener Herstellungs-, Optimierungsverfahren oder Eigenschaftsverhalten. Derzeit sind MaterialwissenschaftlerInnen bei dem Vergleich von mehreren Materialien auf sequentielle Vergleiche angewiesen. Die Verteilungen der einzelnen Attribute verschiedener Materialsysteme müssen verglichen werden, weshalb diese Aufgabe typischerweise geistig aufwendig, zeitintensiv und dadurch fehlerbehaftet ist. Diese Arbeit zielt darauf ab, Fachexperten bei ihren täglichen Aufgaben in der Analyse großer Materialensembledatensätze zu unterstützen. Wir haben für diesen Zweck ein komparatives Visualisierungsrahmenwerk entwickelt, das durch eine Übersichtsvisualisierung und drei Detailvisualisierungstechniken ein ganzheitliches Bild über Ähnlichkeiten bzw. Unähnlichkeiten in den Daten liefert. Anhand der Verwendung der Dimensionsreduktionsmethode Multidimensionale Skalierung werden die individuellen Strukturen zusammengefasst und in einer tabellen-basierten Visualisierungtechnik, namens Histogramm-Tabelle, dargestellt. Die Informationen, in welchen Attributen sich die Strukturen am ähnlichsten sind und ihre genauen Ausprägungen, werden mittels statistischer Berechnungen evaluiert, deren Ergebnisse in einem Säulendiagramm und Kastengrafik visualisiert werden. Schlussendlich können auch die linearen Korrelationen zwischen den individuellen Charakteristiken genauer in einer Korrelationskarte exploriert werden. Wir präsentieren die Nutzbarkeit dieses Visualisierungssystems anhand von drei konkreten Anwendungsszenarien und überprüfen ihre Anwendbarkeit mittels einer qualitativen Studie mit 12 Materialexperten. Die aus unserer Arbeit gewonnenen Erkenntnisse repräsentieren einen signifikanten Schritt im Bereich der komparativen Materialanalyse von hochdimensionalen Daten und unterstützen Fachexperten dabei, ihre Arbeit einfacher und effizienter zu gestalten., In safety-critical areas such as aeronautics, but also in other sectors such as the leisure industry, the advancement of respective products is largely driven by the improvement of the materials used. In order to analyze the targeted properties of these new materials, data of the internal structures is generated, using imaging techniques such as X-ray computed tomography (XCT), which is then analyzed in detail using segmentation and quantification algorithms. For materials scientists, the exact design of the internal structures is crucial for the characterization of materials and a comparison of several material candidates based on their characteristics is therefore indispensable for the investigation of different manufacturing and optimization processes or property behavior. Currently, material scientists are dependent on sequential comparisons when analyzing several material candidates. Distributions of the individual attributes across the material systems need to be compared, which is why this task is typically cognitively demanding, time consuming, and thus error-prone. This work aims to support domain experts in their daily tasks of analysing large ensembles of material data. For this purpose we developed a comparative visualization framework that provides a holistic picture of similarities and dissimilarities in the data by means of an overview visualization and three detailed visualization techniques. Using the dimension reduction method Multidimensional Scaling, the individual structures are summarized and rendered in a table-based visualization technique called Histogram-Table. Information, describing in which attributes the structures are most similar as well as their exact characteristics, is evaluated by statistical calculations, the results of which are visualized in a bar chart and box plot. Finally, the linear correlations between the individual characteristics can be explored in a correlation map. We present the usability of this visualization system by means of three concrete usage scenarios and verify its applicability by means of a qualitative study with 12 material experts. The knowledge gained from our work represents a significant step in the field of comparative material analysis of high-dimensional data and supports experts in making their work easier and more efficient.