Data and visual displays in the Journal of Ecology 1996–2016. (January 2021)

• Record Type: Journal Article
• Authors: Friedman, Alon
• Is Part Of: Information visualization. Volume 20:Number 1(2021)
• Page Start: 35
• Page End: 46
• Publication Date: 2021-01
• Subjects: Visual display -- Journal of Ecology -- ecology domain -- data types -- visualization types -- visual best practices; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871620980121
• Languages: English
• ISSNs: 1473-8716

Abstract

Scholars in scientific disciplines face unique challenges in the creation of visualizations, especially in publications that require insights derived from analyses to be visually displayed. The literature on visualizations describes different techniques and best practices for the creation of graphs. However, these techniques have not been used to evaluate the impact of visualizations in academic publications. In the field of ecology, as in other scientific fields, graphs are an essential part of journal articles. Little is known about the connections between the kind of data presented and domain in which the researchers conducted their study that together produces the visual graphics. This study focused on articles published in the Journal of Ecology between 1996 and 2016 to explore possible connections between data type, domain, and visualization type. Specifically, this study asked three questions: How many of the graphics published between 1996 and 2016 follow a particular set of recommendations for best practices? What can Pearson correlations reveal about the relationships between type of data, domain of study, and visual displays? Can the findings be examined through an inter-reliability test lens? Out of the 20, 080 visualizations assessed, 54% included unnecessary graphical elements in the early part of the study (1996–2010). The most common type of data was univariate (35%) and it was often displayed using line graphs. Twenty-one percent of the articles in the period studied could be categorized under the domain type "single species." Pearson correlation analysis showed that data type and domain type was positively correlated ( r = 0.08; p ≤ 0.05). Cohen's kappa for the reliability test was 0.86, suggesting good agreement between the two categories. This study provides evidence that data type and domain types are equally important in determining the type of visualizations found in scientific journals.