Effective Visual Communication for the Quantitative Scientist

Vandemeulebroecke, Marc; Baillie, Mark; Margolskee, Alison; Magnusson, Baldur

doi:10.1002/psp4.12455

Cited by 13 publications

(13 citation statements)

References 51 publications

(97 reference statements)

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“…In a second step, changing perspective from authors to publishers, the community needs more advanced tools than static PDF or printed papers (Vandemeulebroecke et al, 2019). If articles are more and more published in HTML format, interactive visualizations could allow for multiple perspectives (e.g., zoom, selection, layering).…”

Section: Avoid Color -Learning From Black and White Visualizationsmentioning

confidence: 99%

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Stoelzle¹,

Stein²

2021

Preprint

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Abstract. Nowadays color in scientific visualizations is standard and extensively used to group, highlight or delineate different parts of data in visualizations. The rainbow color map (also known as jet color map) is famous for its appealing use of the full visual spectrum with impressive changes in chroma and luminance. Beside attracting attention, science has for decades criticized the rainbow color map for its non-linear and erratic change of hue and luminance along the data variation. The missed uniformity causes a misrepresentation of data values and flaws in science communication. The rainbow color map is scientifically incorrect and hardly decodable for a considerable number of people due to color-vision deficiency (CVD) or other vision impairments. Here we aim to raise awareness how widely used the rainbow color maps still is in hydrology. To this end we perform a paper survey scanning for color issues in around 1000 scientific publications in three different journals including papers published between 2005 and 2020. In this survey, depending on the journal, 16–24 % of the publications have a rainbow color map and around the same ratio of papers (18–29 %) use red-green elements often in a way that color is the only possibility to decode the visualized groups of data. Given these shares, there is a 99.6 % chance to pick at least one visual problematic publication in 10 randomly chosen papers from our survey. To overcome the use of the rainbow color maps in science, we propose some tools and techniques focusing on improvement of typical visualization types in hydrological science. Consequently, color should be used with more care to highlight most important aspects of a visualization and the identification of correct data types such as categorical or sequential data is essential to pick appropriate color maps. We give guidance how to avoid, improve and trust and color in a proper and scientific way. Finally, we sketch a way to improve the communication of rainbow flaws between different status groups in science, publishers, and the media.

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Section: Avoid Color -Learning From Black and White Visualizationsmentioning

confidence: 99%

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Stoelzle¹,

Stein²

2021

Preprint

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“…Communication through visualizing data or analysis results is an important step in a quantitative workflow and is one of the most effective ways to summarize and communicate complex results 12,3 or results from large data sets 4 . Visual data presentations make up a considerable amount of the results section in scientific publications.…”

Section: Background and Previous Workmentioning

confidence: 99%

“…In network meta‐analysis, and especially in simulation studies for network meta‐analysis, the complexity of the data structure poses a challenge in creating visual summaries, complicating communication and interpretation of results. A recent publication 1 defines the three main ingredients of a graphical representation as having a clear purpose, providing a clear visualization of the data and making the message transported by the visualization obvious. The purpose of a radar graph in network meta‐analysis is (a) to compare treatments (or treatment comparisons) to each other, for example, for a particular outcome or statistic, and (b) to compare a set of quantities estimated in a collection of network meta‐analysis with a pre‐specified value, for example, coverage of 95% confidence intervals in a simulation study.…”

Section: Using Radar Graphs In Network Meta‐analysismentioning

confidence: 99%

“…At best, they help to access, understand, and interpret the main message of a research paper quickly. 5 Visualization, as every form of communication, therefore needs careful evaluation of the message that should be illustrated, 1,3 careful adaption to the specific setting and audience, as well as careful composing of the graph itself, maximizing the data-to-ink ratio. 6 Meta-analysis is a technique to summarize results from various studies commonly applied in the medical context quantitatively.…”

Section: Background and Previous Workmentioning

confidence: 99%

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Utilizing radar graphs in the visualization of simulation and estimation results in network meta‐analysis

Seide

Jensen

Kieser

2020

Research Synthesis Methods

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Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The complexity increases further in situations where all potential contrasts in the network are compared to a predefined fixed value of interest, such as the 95% coverage evaluated against the nominal value of 95% in simulation studies. We propose utilizing radar graphs to illustrate results from network meta-analysis in cases where the interest lies in the comparison of estimated results (after fitting a network meta-analysis in a specific data set) or a performance measure (simulation study) to a pre-defined fixed reference value. Accounting for the complex high-dimensional data structure, the general picture of the full network is captured at once without increasing the space needed for visualization. Especially in large simulation studies, where multiple scenarios need to be visually combined to gain an overview on different scenarios, this type of illustration facilitates the discussion of results. Further properties, such as the expected variation due to the Monte-Carlo error or the differentiation between directly and indirectly estimated treatment contrasts in simulation studies, as well as the indication of well-connected and sparsely connected treatments in an applied network meta-analysis, can additionally be included in the visualization. While we used the radar-graph mainly for a simulation study, other applications are suitable whenever relative contributions of treatment (contrasts) are of interest.

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“…DMC reports should be have a clear structure and ideally be a single document that includes a table of content. The graphical and interactive visualization of data may ease the exploration of the data and enhance the readers' understanding of the data [ [33] , [34] , [35] ]. DMC reports are no exception to this.…”

Section: Statistical Aspects Of Monitoring Clinical Trials In Covid-1mentioning

confidence: 99%

Data monitoring committees for clinical trials evaluating treatments of COVID-19

Mütze

Friede

2020

Contemporary Clinical Trials

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The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.

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Effective Visual Communication for the Quantitative Scientist

Cited by 13 publications

References 51 publications

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Utilizing radar graphs in the visualization of simulation and estimation results in network meta‐analysis

Data monitoring committees for clinical trials evaluating treatments of COVID-19

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