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Examining data visualization pitfalls in scientific publications
Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556474/ https://www.ncbi.nlm.nih.gov/pubmed/34714412 http://dx.doi.org/10.1186/s42492-021-00092-y |
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author | Nguyen, Vinh T Jung, Kwanghee Gupta, Vibhuti |
author_facet | Nguyen, Vinh T Jung, Kwanghee Gupta, Vibhuti |
author_sort | Nguyen, Vinh T |
collection | PubMed |
description | Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation. |
format | Online Article Text |
id | pubmed-8556474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-85564742021-11-15 Examining data visualization pitfalls in scientific publications Nguyen, Vinh T Jung, Kwanghee Gupta, Vibhuti Vis Comput Ind Biomed Art Original Article Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation. Springer Singapore 2021-10-29 /pmc/articles/PMC8556474/ /pubmed/34714412 http://dx.doi.org/10.1186/s42492-021-00092-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Nguyen, Vinh T Jung, Kwanghee Gupta, Vibhuti Examining data visualization pitfalls in scientific publications |
title | Examining data visualization pitfalls in scientific publications |
title_full | Examining data visualization pitfalls in scientific publications |
title_fullStr | Examining data visualization pitfalls in scientific publications |
title_full_unstemmed | Examining data visualization pitfalls in scientific publications |
title_short | Examining data visualization pitfalls in scientific publications |
title_sort | examining data visualization pitfalls in scientific publications |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8556474/ https://www.ncbi.nlm.nih.gov/pubmed/34714412 http://dx.doi.org/10.1186/s42492-021-00092-y |
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