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1. Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

   https://doi.org/10.1002/ecs2.3739  

   Wesner, Jeff S.; Pomeranz, Justin P. F. (September 2021, Ecosphere)

   Abstract Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non‐informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, and sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models that have a link function and potentially dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non‐informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator–prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis. 

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2. A Crowdsourced Study of Visual Strategies for Mitigating Confirmation Bias

   Chuanromanee, R.; Metoyer, R.A. (October 2022, Visual Languages and Human-Centered Computing 2022)

   Confirmation bias is a type of cognitive bias that involves seeking and prioritizing information that conforms to a pre-existing view or hypothesis that can negatively affect the decision-making process. We investigate the manifestation and mitigation of confirmation bias with an emphasis on the use of visualization. In a series of Amazon Mechanical Turk studies, participants selected evidence that supported or refuted a given hypothesis. We demonstrated the presence of confirmation bias and investigated the use of five simple visual representations, using color, positional, and length encodings for mitigating this bias. We found that at worst, visualization had no effect in the amount of confirmation bias present, and at best, it was successful in mitigating the bias. We discuss these results in light of factors that can complicate visual debiasing in non-experts. 

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3. TactViz: A VMD Plugin for Tactile Visualization of Protein Structures

   https://doi.org/10.14448/jsesd.12.0015  

   R. Shaw, Olivia; A. Hadden-Perilla, Jodi (October 2020, The Journal of Science Education for Students with Disabilities)

   Scientific disciplines spanning biology, biochemistry, and biophysics involve the study of proteins and their functions. Visualization of protein structures represents a barrier to education and research in these disciplines for students who are blind or visually impaired. Here, we present a software plugin for readily producing variable-height tactile graphics of proteins using the free biomolecular visualization software Visual Molecular Dynamics (VMD) and protein structure data that is publicly available through the Protein Data Bank. Our method also supports interactive tactile visualization of proteins with VMD on electronic refreshable tactile display devices. Employing our method in an academic laboratory has enabled an undergraduate student who is blind to carry out research alongside her sighted peers. By making the study of protein structures accessible to students who are blind or visually impaired, we aim to promote diversity and inclusion in STEM education and research. 

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4. Spanning the Boundaries of Data Visualization Work: An Exploration of Functional Affordances and Disciplinary Values

   https://doi.org/10.1007/978-3-030-15742-5_6  

   Snyder, J; Shilton, K (April 2019, Lecture notes in computer science)

   Creating data visualizations requires diverse skills including computer programming, statistics, and graphic design. Visualization practitioners, often formally trained in one but not all of these areas, increasingly face the challenge of reconciling, integrating and prioritizing competing disciplinary values, norms and priorities. To inform multidisciplinary visualization pedagogy, we analyze the negotiation of values in the rhetoric and affordances of two common tools for creating visual representations of data: R and Adobe Illustrator. Features of, and discourse around, these standard visualization tools illustrate both a convergence of values and priorities (clear, attractive, and communicative data-driven graphics) side-by-side with a retention of rhetorical divisions between disciplinary communities (statistical analysis in contrast to creative expression). We discuss implications for data-driven work and data science curricula within the current environment where data visualization practice is converging while values in rhetoric remain divided. 

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5. An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

   https://doi.org/10.3390/e20090625  

   Wu, Jieting; Zhu, Feiyu; Liu, Xin; Yu, Hongfeng (September 2018, Entropy)

   Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented. 

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