Another face of Lorenz-Mie scattering: monodisperse distributions of spheres produce Lissajous-like patterns

The complete scattering matrix S of spheres was measured with a flow cytometer. The experimental equipment allows simultaneous detection of two scattering-matrix elements for every sphere in the distribution. Two-parameter scatterplots withx andy coordinates determined by the Sll + Sij and S11 - Sij values are measured. Samples of spheres with very narrow size distributions (< 1%) were analyzed with a FlowCytometer, and they produced unexpected two-parameter scatterplots. Instead of compact distributions we observed Lissajous-like loops. Simulation of the scatterplots, using Lorenz-Mie theory, shows that these loops are due not to experimental errors but to true Lorenz-Mie scattering. It is shown that the loops originate from the sensitivity of the scattered field on the radius of the spheres. This paper demonstrates that the interpretation of rare events and hidden features in flow cytometry needs reconsideration

A randomized trial in a massive online open course shows people don't know what a statistically significant relationship looks like, but they can learn

Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI: 45.1%-49.7%) of statistically significant relationships, and 74.6% (95% CI: 72.5%-76.6%) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/.Comment: 7 pages, including 2 figures and 1 tabl

Optical mammography combined with fluorescence imaging: lesion detection using scatterplots

Using scatterplots of 2 or 3 parameters, diffuse optical tomography and fluorescence imaging are combined to improve detectability of breast lesions. Small or low contrast phantom-lesions that were missed in the optical and fluorescence images were detected in the scatterplots. In patient measurements, all tumors were visible and easily differentiated from artifacts and areolas in the scatterplots. The different rate of intake and wash out of the fluorescent contrast agent in the healthy versus malignant tissues was also observed in the scatterplot: this information can be used to discriminate malignant lesion from normal structures

Human Perception of Outliers in Correlated Scatterplots

Despite ample research analyzing how people recognize differences in data, one aspect that has largely gone unmeasured is how outliers affects these comparisons. This paper aims to provide a better understanding how people recognize differences in data by having participants decide which correlation is stronger (forced choice) when comparing scatterplots at different correlations with outliers. With 67 participants, we calculated a just noticeable difference (JND) at different correlation values. The results indicate that at all levels of correlation (e.g., .4 or .8) tested, people were less able to detect the stronger correlation for the scatterplots with outliers compared to scatterplots without outliers

Exploring and Explaining 3D Data Representations

Many people interact with scientific data by means of 2D or 3D representations such as scatterplots. In this thesis we study mechanisms for simplifying this interaction process. First, we propose a method that allows users to easily rotate, with a high degree of control, complex 3D shapes to inspect them from specific viewpoints. Secondly, we propose methods that explain 2D and 3D scatterplots of high-dimensional data so that a wide range of users can understand what the visual structures in these plots actually represent. Finally, we compare the effectiveness of our explanations for 2D and 3D scatterplots to provide recommendations for cases when one, or the other type, of plot is best used in practice

Measuring Categorical Perception in Color-Coded Scatterplots

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how the number of categories and choice of color encodings used in multiclass scatterplots influences the viewers' abilities to analyze data across classes. Participants estimated relative means in a series of scatterplots with 2 to 10 categories encoded using ten color palettes drawn from popular design tools. Our results show that the number of categories and color discriminability within a color palette notably impact people's perception of categorical data in scatterplots and that the judgments become harder as the number of categories grows. We examine existing palette design heuristics in light of our results to help designers make robust color choices informed by the parameters of their data.Comment: The paper has been accepted to the ACM CHI 2023. 14 pages, 7 figure