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.
FoxPHendlerJ. Changing the equation on scientific data visualization. Science2011; 331: 705–708.
2.
DasguptaAPocoJWeiY, et al. Bridging theory with practice: an exploratory study of visualization use and design for climate model comparison. IEEE Trans Vis Comput Graph2015; 21(9): 996–1014.
3.
BackALArnoldRMBaileWF, et al. Faculty development to change the paradigm of communication skills teaching in oncology. J Clin Oncol2009; 27(7): 1137–1141.
4.
ReinaGChildsHMatkovic’K, et al. The moving target of visualization software for an increasingly complex world. Comput Graph2020; 87: 12–29.
5.
QueenJPQuinnGPKeoughMJ. Experimental design and data analysis for biologists. Cambridge: Cambridge University Press, 2002.
6.
HeerJBostockMOgievetskyV. A tour through the visualization zoo. Commun ACM2010; 53(6): 59–67.
7.
WainerH. Graphic discovery: a trout in the milk and other visual adventures. Princeton, NJ: Princeton University Press, 2005.
8.
GreenacreM. Data reporting and visualization in ecology. Polar Biol2016; 39(11): 2189–2205.
9.
EllisonAM. Exploratory data analysis and graphic display. In: SchneiderSMGurevitchJ (eds) Design and analysis of ecological experiments. Oxford: Oxford University Press, 1993, pp.14–45.
10.
TukeyJW. Exploratory data analysis. New York, NY: Addison-Wesley, 1977.
11.
TufteER. The visual display of quantitative information. Cheshire, CT: Graphics Press, 1983.
HinterbergerH. Data visualization. In: LiuLÖzsuMT (eds) Encyclopedia of database systems. Boston, MA: Springer, 2009, pp.652–657.
14.
IsenbergPElmqvistNScholtzJ, et al. Collaborative visualization: definition, challenges, and research agenda. Inf Vis2011; 10(4): 310–326.
15.
RajeRRBoylesMFangS. CEV: collaborative environment for visualization using Java RMI. Concurr Pract Exp1998; 10(11–13): 1079–1085.
16.
NielsenGHagenHMüllerH. Scientific visualization. Institute of Electrical and Electronics Engineers, Los Alamitos, CA, January1997.
17.
SaraiyaPNorthCDucaK. Visualizing biological pathways: requirements analysis, systems evaluation and research agenda. Inf Vis2005; 4(3): 191–205.
18.
ChenC. Information visualization. Wiley Interdiscip Rev Comput Stat2010; 2(4): 387–403.
19.
TverskyB. Visualizing thought. In: HuangW (ed.) Handbook of human centric visualization. New York, NY: Springer, 2014, pp.3–40.
20.
LarkinJHSimonHA. Why a diagram is (sometimes) worth ten thousand words. Cogn Sci1987; 11(1): 65–100.
21.
PinkerS. A theory of graph comprehension. In: FreedleR (ed.) Artificial intelligence and the future of testing. Hillsdale, NJ: Lawrence Erlbaum Associates, 1990, pp.73–126.
FrielSNCurcioFRBrightGW. Making sense of graphs: critical factors influencing comprehension and instructional implications. J Res Math Educ2001; 32(2): 124–158.
25.
BellAJanvierC. The interpretation of graphs representing situations. For the Learning of Mathematics1981; 2(1): 34–42.
26.
CarpenterPAShahP. A model of the perceptual and conceptual processes in graph comprehension. J Exp Psychol Appl1998; 4(2): 75.
27.
CairoA. How charts lie: getting smarter about visual information. New York, NY: WW Norton and Company, 2019.
28.
HoweBPo-ShenLGrechkinM, et al. Deep mapping of the visual literature. In: Proceedings of the 26th international conference on world wide web companion, April2017, pp.1273–1277. Perth: ACM. https://dl.acm.org/doi/proceedings/10.5555/3041021.
29.
GelmanAUnwinA. Infovis and statistical graphics: different goals, different looks. J Comput Graph Stat2013; 22(1): 2–28.
30.
WeissgerberTLMilicNMWinhamSJ, et al. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol2015; 13: e1002128.
31.
ZacksJLevyETverskyB, et al. Graphs in print. In: AndersonMMeyerBOlivierP (eds) Diagrammatic representation and reasoning. London: Springer-Verlag, 2002, p.212.
32.
MagnetA. Diachronic Aaalysis of the visuals in the research paper: a corpus-based study of the strategies and semiotics of visual representation in nutrition biochemistry. LSP Prof Commun (2001-2008)2001; 1(1).
33.
FriedmanA. The relationship between research method and visual display: a study of conference proceedings in the field of knowledge organization. Inf Res2014; 19(4): 134–153.
34.
MunznerT. Process and pitfalls in writing information visualization research papers. In: KerrenAStaskoJTFeketeJD, et al. (eds) Information visualization. Berlin/Heidelberg: Springer, 2008, pp.134–153.
35.
EckelmanMJLaboyMM. LCAart: communicating industrial ecology at a human scale. J Ind Ecol2020; 24(4): 736–747.
36.
RutterLCookD. BigPint: a bioconductor visualization package that makes big data pint-sized. PLoS Comput Biol2020; 16(6): e1007912.
37.
AndersonMJWillisTJ. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology2003; 84(2): 511–525.
38.
MarcumJW. Beyond visual culture: the challenge of visual ecology. Portal2002; 2(2): 189–206.
39.
DunbarK. Concept discovery in a scientific domain. Cogn Sci1993; 17(3): 397–434.
40.
OkadaTSimonHA. Collaborative discovery in a scientific domain. Cogn Sci1997; 21(2): 109–146.
41.
ChenC. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol2006; 57(3): 359–377.
42.
BörnerKChenCBoyackKW. Knowledge domain visualization. In: ChenC (ed.) Information visualization: beyond the horizon. London: Springer, 2006 pp.143–171.
43.
YuanXChenCZhangX, et al. Effects of domain knowledge on user performance and perception in a knowledge domain visualization system. In: International conference of design, user experience, and usability, July2013, pp.601–610. Berlin/Heidelberg: Springer.
44.
ClevelandWS. Graphs in scientific publications. Am Stat1984; 38(4): 261–269.
45.
HilbornRMangelM. The ecological detective: confronting models with data. Vol. 28. Princeton, NJ: Princeton University Press, 1997.
46.
ZuurAFLenoENSmithGM. Analysing ecological data. New York: Springer, 2007.
47.
ShahPHoeffnerJ. Review of graph comprehension research: implications for instruction. Educ Psychol Rev2002; 14(1): 47–69.
48.
ClevelandWSMcGillR. Graphical perception and graphical methods for analyzing scientific data. Science1985; 229(4716): 828–833.
49.
ScheinerSMWilligMR. A general theory of ecology. Theor Ecol2008; 1(1): 21–28.
50.
PalmerMA. Ecological science and sustainability for a crowded planet: 21st century vision and action plan for the Ecological Society of America. Report, http://esa.Org/ecovisions/ (2004)
51.
CherretM. Ecological concepts-the results of the survey of the British Ecological Society member’s views. Ecol Bull1988; 69: 41–42.
52.
ChristensenNLBartuskaAMBrownJH, et al. The report of the Ecological Society of America committee on the scientific basis for ecosystem management. Ecol Appl1996; 6(3): 665–691.
53.
CarpenterSRFolkeC. Ecology for transformation. Trends Ecol Evol2006; 21(6): 309–315.
54.
MonathTP. The Arboviruses: epidemiology and ecology. Vol. 1. Boca Raton, FL: CRC Press, 2020.
55.
CarmelYKentRBar-MassadaA, et al. Trends in ecological research during the last three decades–a systematic review. PLoS One2013; 8(4): e59813.
56.
KelleherCWagenerT. Ten guidelines for effective data visualization in scientific publications. Environ Model Softw2011; 26(6): 822–827.
57.
WongsuphasawatKMoritzDAnandA, et al. Towards a general-purpose query language for visualization recommendation. In: Proceedings of the workshop on Human-In-the-Loop Data Analytics (HILDA ’16), June2016, pp.1–6. New York, NY: ACM.
RoseG. Visual methodologies: an introduction to researching with visual materials. London: Sage, 2016.
60.
LockIAraujoT. Visualizing the triple bottom line: a large-scale automated visual content analysis of European corporations’ website and social media images. Corp Soc Responsib Environ Manag2020; 27(6): 2631–2641.
61.
LeeNBuchananKYuM. Each post matters: a content analysis of mental health images on Instagram. J Vis Commun Med2020; 43(3): 128–138.
62.
RoyerPDCobbNSCliffordMJ, et al. Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: primary and secondary ecological implications. J Ecol2011; 99(3): 714–723.
63.
KozlovMVLantaVZverevV, et al. Background losses of woody plant foliage to insects show variable relationships with plant functional traits across the globe. J Ecol2015; 103(6): 1519–1528.
BealMJGhahramaniZRasmussenCE. The infinite hidden Markov model. In: DietterichTGBeckerSGhahramaniZ (eds) Advances in neural information processing systems. Cambridge, MA: The MIT Press, 2002, pp.577–584.
66.
BakemanRGottmanJM. Observing interaction: an introduction to sequential analysis. New York, NY: Cambridge Uuniversity Press, 1997.
67.
BakemanRQueraV. Sequential analysis and observational methods for the behavioral sciences. New York, NY: Cambridge University Press, 2011.
68.
MeyerDZeileisAHornikK, et al. Package “vcd”. Visualizing Categorical Data. R package version 1.4-8, 21September2020.
69.
WilkinsonL. The grammar of graphics. New York: Springer-Verlag, 2005, p.37.
