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Abstract

Nursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.

Keywords

Big Data visualization data analysis data science multidimensional data data integration

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References

Abramson

C. M.

Dohan

(2015). Beyond text: Using arrays to represent and analyze ethnographic data. Sociological Methodology, 45, 272-319.

Cameron

Jones

I. G.

(1983). John Snow, the broad street pump and modern epidemiology. International Journal of Epidemiology, 12, 393-396.

Card

S. K.

Mackinlay

J. D.

Shneiderman

(Eds.). (1999). Readings in information visualization: Using vision to think. San Francisco, CA: Morgan Kaufman.

Chen

Hou

Liu

(2012). An illuminated path: The impact of the work of Jim Thomas. In Dill

Earnshaw

(Eds.), Explanding the frontiers of visual analytics and visualization (pp. 9-20). New York, NY: Springer.

Clark

(2013, August). Four things you need to know in the Big Data era. Forbes. Retrieved from http://www.forbes.com/sites/dorieclark/2013/08/08/four-things-you-need-to-know-in-the-big-data-era/#6d712bc12e86

Cleveland

W. S.

(1985). The elements of graphing data. Summit, NJ: Hobart Press.

Cohen

(1984). Florence nightingale. Scientific American, 250, 128-137.

Few

(Ed.). (2009a). Data visualization for human perception. Aarhus, Denmark. Retrieved from https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception

Few

(2009b). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press.

10.

Fox

Hendler

(2011). Changing the equation on scientific data visualization. Science, 331, 705-708.

11.

Friendly

(2006). A brief history of data visualization. In Chen

Hardle

Unwin

(Eds.), Handbook of computational statistics: Data visualization (pp. 16-48). Heidelberg, Germany: Springer-Verlag.

12.

Jin

Wah

B. W.

Cheng

Wang

(2015). Significance and challenges of Big Data research. Big Data Research, 2(2), 59-64.

13.

Johnson

Feinglos

Pereira

Hassell

Blascovich

Nicollerat

. . . Vorderstrasse

(2014). Feasibility and preliminary effects of a virtual environment for adults with type 2 diabetes: Pilot study. JMIR Research Protocols, 3(2), e23. doi:10.2196/resprot.3045

14.

Kehrer

Hauser

(2013). Visualization and visual analysis of multifaceted scientific data: A survey. IEEE Transactions on Visualization and Computer Graphics, 19, 495-513.

15.

Keim

D. A.

Andrienko

Fekete

J. D.

Gorg

Kohlhammer

Melancon

(2008). Visual analytics: Definition, process, and challenges. In Kerren

(Ed.), Information visualization (pp. 154-175). Berlin, Germany: Springer-Verlag.

16.

Keim

D. A.

Mansmann

Schneidewind

Ziegler

(2006). Challenges in visual data analysis. In IV ’06 Proceedings of the Tenth International Conference on Information Visualization (pp. 9-16). Washington, DC: IEEE Computer Society.

17.

Kerren

Stasko

J. T.

Dykes

(2008). Teaching information visualization. In Kerren

Stasko

J. T.

Fekete

J. D.

North

(Eds.), Information visualization (pp. 65-90). New York, NY: Springer.

18.

Kitchin

McArdle

(2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 1-10. doi:10.1177/2053951716631130

19.

Knigge

Cope

(2006). Grounded visualization: Integrating the analysis of qualitative and quantitative data through grounded theory and visualization. Environment and Planning, 38, 2021-2037.

20.

Lam

Bertini

Isenberg

Plaisant

Carpendale

(2012). Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 18, 1520-1536.

21.

Leventhal

(2014, July 14). Survey: Variety, not volume, the biggest challenge to analyzing Big Data [Web log post]. Retrieved from http://www.healthcare-informatics.com/news-item/survey-variety-not-volume-biggest-challenge-analyzing-big-data

22.

Lim

A. K.

Thuemmler

(2015, April). Opportunities and challenges of internet-based health interventions in the future internet. Paper presented at New Generations (ITNG), 2015 12th International Conference on Information Technology, Las Vegas, NV.

23.

Lynch

(1985). Discipline and the material form of images: An analysis of scientific visibility. Social Studies of Science, 15, 37-66.

24.

Margolis

Derr

Dunn

Huerta

Larkin

Sheehan

. . . Green

E. D.

(2014). The National Institutes of Health’s Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical Big Data. Journal of the American Medical Informatics Association, 21, 957-958.

25.

North

(2006). Toward measuring visualization insight. IEEE Computer Graphics and Applications, 26(3), 6-9.

26.

Onwuegbuzie

A. J.

Dickinson

W. B.

(2008). Mixed methods analysis and information visualization: Graphical display for effective communication of research results. The Qualitative Report, 13, 204-225.

27.

Shamir

Stolpnik

(2012). Multivariate graphs: Exploration using interactive visual queries. Computers & Graphics, 36, 257-264.

28.

Sharts-Hopko

N. C.

(2013). Tackling complex problems, building evidence for practice, and educating doctoral nursing students to manage the tension. Nursing Outlook, 61, 102-108.

29.

Shaw

(2014, March-April). Why “Big Data” is a big deal. Harvard Magazine. Retrieved from http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal

30.

Tominski

Abello

Schumann

(2009). CGV—An interactive graph visualization system. Computers & Graphics, 33, 660-678.

31.

Tufte

E. R.

(1983). The visual display of quantitative information. Cheshire, CT: Graphics Press.

32.

Tufte

E. R.

(1990). Envisioning information. Cheshire, CT: Graphics Press.

33.

Tufte

E. R.

(1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.

34.

Tufte

E. R.

(2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.

35.

Tufte

E. R.

(2006). Beautiful evidence. Cheshire, CT: Graphics Press.

36.

Tukey

J. W.

(1977). Exploratory data analysis. New York, NY: Pearson.

37.

Vorderstrasse

Melkus

Lewinski

Johnson

C. M.

(2015). Diabetes learning in virtual environments: Testing the efficacy of self-management training and support in virtual environments (Randomized controlled trial protocol). Nursing Research, 64, 485-494.

38.

Weber

G. H.

Hauser

(2014). Interactive visual exploration and analysis. Retrieved from https://www.semanticscholar.org/paper/Interactive-Visual-Exploration-and-Analysis-Weber-Hauser/17b65ea9dc2cbf9844df55572ef66ef332c18d23

39.

Wong

P. C.

Bergeron

R. D.

(1994). 30 years of multidimensional multivariate visualization. In Proceedings in scientific visualization, overviews, methodologies, and techniques (pp. 3-33). Washington, DC: IEEE Computer Society.

40.

L. H.

Hsu

P. Y.

(2013). An interactive and flexible information visualization method. Information Sciences, 221, 306-305.

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Visualization of Multidimensional Data in Nursing Science

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