Ed Nagelhout

ed nagelTitle: Professor

University: University of Nevada, Las Vegas

Email: ed.nagelhout@unlv.edu

Twitter: @EdNagelhout1

Website: https://faculty.unlv.edu/nagelhout/home/

Description of Work:

My current work includes three major, long-term projects: 1) Comprehensive Study of Scientific Figures; 2) Graduate Writing at UNLV; 3) Critical Digital Program Development. These three projects are distinct, yet connected in a variety of practical and theoretical ways.

The Scientific Figures project, as you all will see, seeks a comprehensive understanding of the visual presentation (and visual nature) of scientific figures for both producers and consumers. This project is divided into three phases: Phase 1 seeks to understand the production of scientific figures; Phase 2 will create a comprehensive and searchable corpus of scientific figures; Phase 3 will examine the translation of scientific findings in figures to the public. Ultimately we want to offer insights for both producers and consumers for creating more effective complex figures, as well as strategies for reading and understanding these figures.

The Graduate Writing project is a local study that describes and makes visible writing practices and the culture of writing in various graduate curricula at UNLV. To date, we’ve distributed surveys to both graduate students (1,047 responses) and graduate faculty (365 responses) and conducted focus group interviews. Currently we are working on a report to the university and planning long-term initiatives, short-term activities, and a series of workshops that seek to collaborate with as many units as possible on campus.

The Program Development project catalogs our approach for developing a new Minor in Professional Writing at UNLV: an approach that creates seamless connections among courses, promotes broader community engagement in learning networks, and correlates competencies, digital assets, student activities, and program assessments. Our primary goal is to create and maintain flexible curricula and relevant assets within a networked learning environment that encourages more collaborative approaches, privileges informal and situated learning, and promotes ubiquitous and lifelong learning, thereby increasing learner control, learner choice, and learner independence.

 Symposium Submission:

Scientific Figure Complexity and the Dissemination of Health Information to the Public

More than 80 million unique visitors—with 221 million page views—have accessed MedlinePlus, an online health information resource covering more than 900 diseases and conditions. The site depends on multiple sources for the latest in health news, including biomedical journal press releases; therefore, research may now be disseminated to a new, larger readership—likely comprised of scientists as well as laypersons. This far-reaching dissemination will require researchers to consider all of the ways their findings will be read and understood.

One common strategy for disseminating research findings in biomedical journals is through data visualization aids, such as the figure, which ideally present information in a precise, transportable, and easily-digestible form. The data within figures, whether graphs, images, or diagrams, may ultimately become what informs the public. And yet, in the last 10 years, the creation and composition of figures has changed, becoming more complex both in substance and format. While scientists have the capability to create robust visuals of different data types and data sets, easily generating figures with multiple panels, panels within panels, and multiple panels, such figure complexity—the sheer detail contained within these visuals—may complicate review for both scientists and the public.

Because data from consumer studies suggest that the complexity of visuals is a barrier to consumer understanding of health information, a systematic investigation of the visual complexity of scientific figures is needed to ensure that new health-related findings are communicated effectively to the public. But to analyze scientific figures for complexity, we must understand the standards established by biomedical journals. In addition, a systematic assessment of original figures is needed to inform whether the biomedical journal standards are consistent with what the public understands. These assessments are critical first steps for ensuring that new health-related findings are communicated effectively to the public.

The importance of disseminating clear, meaningful, and comprehensible biomedical findings to the public, including patients, marks the visual analysis of figures as a critical area of inquiry. Visual analysis methods are accepted tools for both quantitative and qualitative research and are increasingly used in a wide range of disciplines. And while a number of visual rhetoric researchers have examined scientific communication or described different analytical frames for better understanding scientific figures, most have been relatively small in scope and focused, primarily, on expert producers creating visuals for expert readers. More importantly, they have not systematically examined the complexity of visuals used in biomedical journals and the ways these same visuals “translate” for public use. Therefore, a collaborative, interdisciplinary research team composed of rhetoric and health investigators began to study the broad dissemination of health information to the public through scientific figures.

For this paper, we will describe findings from the first phase of our research analyzing the complexity of scientific figures. Our discussion will focus on three areas: (1) publishing standards for scientific figures; (2) a comparison of standards with consumer perceptions; and (3) our development of a Scientific Figure Complexity Scale.

We will begin by establishing the disciplinary publishing standards for scientific figures in biomedical journals. To do this, we randomized journals in the biomedical field of skeletal muscle regeneration and randomly selected 100 journals for review. This biomedical field was selected because of (1) investigator expertise; (2) widespread journal outlets; and (3) limited, routine dissemination of its findings to the public. Next we downloaded and organized the Author Guidelines from each selected journal’s website, categorizing all headings, subheadings, and statements pertaining to publishing figures. We then performed a discourse analysis of the categorized content for lexicon, style, rhetoric, and meaning.

Next, we will describe our comparison of standards for the publication of figures with consumers’ documented understanding of and preferences for visualization aids in health information. Much current research focuses on how individuals—mainly patients or other individuals without a medical background—perceive or understand visualization aids and the personal factors that may affect interpretation. Our goal for comparison is to determine if there are similarities, differences, or gaps between the expectations of expert readers (editors of biomedical journals) and those of the public (patients and their families).

Finally, we will describe our development of a Scientific Figure Complexity Scale, which is a tool for quantitative analysis of figures. This tool assesses complexity by incorporating common figure elements (i.e., size, panel number, quantity of bars/lines, caption length, and notations) and common visual markers (i.e., contrast, repetition, alignment, proximity, symmetry, color, grid layouts, and typography) with the expected standards for the publication of figures in biomedical journals. This later phase of our research will establish both its validity and reliability by collecting data, analyzing and assessing select figures, and generating findings about the complexity of figures used in skeletal muscle regeneration research.

Our future research plans include (1) assessing the complexity of figures from high impact, biomedical journals, such as the New England Journal of Medicine, and the complexity of genomic-related figures; and (2) constructing a comprehensive and searchable corpus of scientific figures.

As an emerging area of inquiry, we envision that our research will broaden the scope of the visual analysis of scientific figures, lead to the development of evidence-based strategies for understanding scientific figures, and alter ways that scientists produce figures and visuals within the scientific community. We believe that scientists must communicate data effectively in both scientific and lay publications so that patients, their families, and the public can be presented the latest health information visually in the most effective and accessible ways. In addressing concerns of comprehension and clarity, the scientific community may expand the accessibility of its product to a larger readership. Such a shift will not only ensure the public’s grasp of taxpayer-funded research, but may also encourage greater appreciation, consumption, and support for scientific studies.