The translation of bedside experience to pedagogical content presents a unique challenge for the field of bioethics. The contributions are multidisciplinary, the practices are heterogeneous, and the work product is characteristically nuanced. While academic bioethics education programs have proliferated, developing content and pedagogy sufficient to teach clinical ethics effectively remains a longstanding challenge. The authors identify three reasons why progress towards this goal has been slow. First, there is a lack of robust, empirical knowledge for education focused on praxis. Second, the methods employed in academic education tend to focus on traditional didactic approaches rather than engendering competency through interaction and practice—the principle means by which clinical ethicists work. Third, the data practitioners have captured has not been presented in a medium educators and students can most meaningfully interact with.

In this paper, the authors describe a novel pedagogical tool: the Armstrong Clinical Ethics Coding System (ACECS) and interactive visual analytics dashboard. Together, these components comprise an educational platform that utilizes the empirical data collected by the institution’s ethics service. The tool offers four advantages. First, it aids with the identification of ethical issues that present during a consultation at that specific institution or medical unit by making use of a lingua franca comprehensible to both ethicists and non-ethicists. Second, content is centered on issue frequency, type, and relation to other issues. Iterating through cases, requestors, or hospital units allows one to understand cases typologically and through metanarratives that reveal relationships and subtle patterns. Third, the use of interactive data visualizations and data storytelling aids comprehension and retention. Fourth, the process of using the system necessitates understanding the manifold ways each case can be understood, accommodating a wide range of perspectives and ethical lenses, enhancing case analysis and self-reflection conducive to life-long learning.

Multisector stakeholders, including, community-based organizations, health systems, researchers, policymakers, and commerce, increasingly seek to address health inequities that persist due to structural racism. They require accessible tools to visualize and quantify the prevalence of social drivers of health (SDOH) and correlate them with health to facilitate dialog and action. We developed and deployed a web-based data visualization platform to make health and SDOH data available to the community. We conducted interviews and focus groups among end users of the platform to establish needs and desired platform functionality. The platform displays curated SDOH and de-identified and aggregated local electronic health record data. The resulting Social, Environmental, and Equity Drivers (SEED) Health Atlas integrates SDOH data across multiple constructs, including socioeconomic status, environmental pollution, and built environment. Aggregated health prevalence data on multiple conditions can be visualized in interactive maps. Data can be visualized and downloaded without coding knowledge. Visualizations facilitate an understanding of community health priorities and local health inequities. SEED could facilitate future discussions on improving community health and health equity. SEED provides a promising tool that members of the community and researchers may use in their efforts to improve health equity.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.

Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed or graded. There is a need to better understand, and potentially quantify, design progress. Natural Language Processing (NLP) is one way of doing so. With the advancement in NLP research, some of its models are adopted into the field of design to quantify a design class performance. To quantify and visualize design progress, the NLP models are often deployed to analyze written documentation collected from the class participants at fixed time intervals through the span of a course. This paper will explore several ways of using NLP in assessing design progress, analyze its advantages and shortcomings, and present a case study to demonstrate its application. The paper concludes with some guidelines and recommendations for future development.

The measurement and communication of the effect size of an independent variable on a dependent variable is critical to effective statistical analysis in the Social Sciences. We develop ideas about how to extend traditional methods of evaluating relationships in multivariate models to explain and illustrate the statistical power of a focal independent variable. Even with a growing acceptance of the need to report effect sizes, scholars in the management community have few well-established protocols or guidelines for reporting effect sizes. In this editorial essay, we: (1) review the necessity of reporting effect sizes; (2) discuss commonly used measures of effect size and accepted cut-offs for large, medium, and small effect sizes; (3) recommend standards for reporting effect sizes via verbal descriptions and graphical presentations; and (4) present best practice examples of reporting and discussing effect size. In summary, we provide guidance for authors on how to report and interpret effect sizes, advocating for rigor and completeness in statistical analysis.

How do economists use graphs, and do they use them well? Using Amazon’s Mechanical Turk, I provide evidence to these questions by exploring more than 2600 graphs published in the first issue of the American Economic Review from 1911 to 2017. I find that economists use a lot of line charts – more than 80% of the total sample are line charts. I also find that the share of graphs that use data (as opposed to diagrams) fell over the first half of the century and then increased from about the early 1980s to today, correlated with perceived graph quality.

Computational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogical inspiration in a user-driven manner. To achieve this, we perform hierarchical Nonnegative Matrix Factorization to generate a clustered structure of patent data and employ D3.js to visualize the patent structure in a node-link network, in which user interaction capabilities are enabled for data exploration. In this study, we also analyze the effect of data size (ranging from 100 to 3000 patents) on two performance aspects of VISION – the clustering quality of topic modeling results and the frame rate of interactive data visualization. The findings show that the tool exhibits more randomized and inconsistent topic modeling results when the database size is too small. But, increasing the database size lowers the frame rate to the point that it could diminish designers’ ability to retrieve and recall information. The scope of the work here is to present the creation of the DbA visualization tool called VISION and to evaluate its data scale limitations in order to provide a basis for developing a visual interaction tool for the analogical retrieval process during DbA.

Information about the global climate, the carbon cycle, changes in solar activity, and a number of other atmospheric processes are preserved in the carbon-14 and the beryllium-10 records. However, these isotope datasets are large and cumbersome to work with. We have designed a self-contained, easy-to-use application that allows for more efficient analysis of different periods and patterns of interest. For several applications in atmospheric modelling, a pre-processing stage is applied to the isotope datasets in order to interpolate the data and mitigate their low temporal resolution. In CHRONOscope, we included linear and non-linear methods of interpolation with interactive parameter optimization. The resultant interpolated data can be extracted for further use. The main functionalities of CHRONOscope include the importation and superimposition of external data, quick navigation through the data with the use of markers, expression of the carbon-14 results in both Δ14C and yr BP form, separation of the data by source, and the visualization of associated error bars. We make this free software available in standalone applications for both Windows and Mac operating systems.

This article introduces the digital humanities and dance studies project Mapping Touring, and employs it in an analysis of Denishawn's touring prior to and immediately following the company's 1925–26 tour to Asia. Situated within the archival turn in dance, Mapping Touring emphasizes the possibilities of spatial and comparative analysis for touring dance artists, with information about their location and repertory drawn from archival sources, gathered and stored in a database, and plotted on interactive maps. With Denishawn as a case study, I contend that digital mapping can make visible some of the implications of travel and touring for the circulation and spread of theories of embodiment contained in dance repertory.

As one of the largest marathons worldwide, the Bank of America Chicago Marathon (BACCM; Chicago, Illinois USA) accumulates high volumes of data. Race organizers and engaged agencies need the ability to access specific data in real-time. This report details a data visualization system designed for the Chicago Marathon and establishes key principles for event management data visualization. The data visualization system allows for efficient data communication among the organizing agencies of Chicago endurance events. Agencies can observe the progress of the race throughout the day and obtain needed information, such as the number and location of runners on the course and current weather conditions. Implementation of the system can reduce time-consuming, face-to-face interactions between involved agencies by having key data streams in one location, streamlining communications with the purpose of improving race logistics, as well as medical preparedness and response.

HankenT, YoungS, SmilowitzK, ChiampasG, WaskowskiD. Developing a Data Visualization System for the Bank of America Chicago Marathon (Chicago, Illinois USA). Prehosp Disaster Med. 2016;31(5):572–577.

Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.