Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transformations of Functions III01:20

Transformations of Functions III

131
Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
131
Multiple Bar Graph01:07

Multiple Bar Graph

8.9K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.9K
Bar Graph01:07

Bar Graph

21.3K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.3K
Interpreting R Charts01:22

Interpreting R Charts

301
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
301
Pie Chart01:04

Pie Chart

15.7K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.7K
Modified Boxplots00:57

Modified Boxplots

10.8K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
10.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance of large language models as an information resource on functional hypothalamic amenorrhea for patients and healthcare professionals.

Frontiers in artificial intelligence·2026
Same author

Differences in overall survival for invasive epithelial ovarian cancer by race and ethnicity: results from the Ovarian Cancer Association Consortium.

British journal of cancer·2026
Same author

Sex, Not Age, Predicts Weight Loss Outcomes With Tirzepatide: A Retrospective Analysis.

Obesity (Silver Spring, Md.)·2026
Same author

United States Menopausal Hormone Therapy Usage Trends: An Observational Study.

Mayo Clinic proceedings·2026
Same author

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.

NPJ digital medicine·2026
Same author

Multi‑scale immune and spatial profiling of sclerosing adenosis and surrounding breast tissue identifies an immune‑cold field phenotype associated with breast cancer risk.

Breast cancer research : BCR·2026

Related Experiment Video

Updated: Jan 5, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K

Reveal, Don't Conceal: Transforming Data Visualization to Improve Transparency.

Tracey L Weissgerber1,2, Stacey J Winham3, Ethan P Heinzen3

  • 1Division of Nephrology and Hypertension (T.L.W., O.G.V., V.D.G., N.M.M.), Mayo Clinic, Rochester, MN.

Circulation
|October 29, 2019
PubMed
Summary

Many journals discourage bar graphs for continuous data, yet nearly half of studies still use them. This guide offers better data visualization techniques for clearer scientific communication.

Keywords:
bar graphsbasic sciencecontinuous datadata visualization

More Related Videos

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.4K
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

2.7K

Related Experiment Videos

Last Updated: Jan 5, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K
Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.4K
Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

2.7K

Area of Science:

  • Medical Visualization
  • Scientific Communication
  • Data Presentation

Background:

  • Journals increasingly recommend against bar graphs for continuous data due to visualization issues.
  • Existing policies offer limited guidance on effective data display alternatives.
  • Suboptimal data visualization practices remain prevalent in scientific literature.

Purpose of the Study:

  • To systematically review data visualization practices in peripheral vascular disease journals.
  • To assess the prevalence of bar graph use for continuous data.
  • To provide guidance on selecting and creating effective data graphics.

Main Methods:

  • Systematic review of figures in top peripheral vascular disease journals.
  • Analysis of figure types used for presenting continuous data.
  • Identification of common data visualization problems.

Main Results:

  • Bar graphs were used to present continuous data in 47.7% of papers with data figures.
  • Prevalence of suboptimal data visualization practices was assessed.
  • Common issues beyond bar graph use were identified.

Conclusions:

  • There is a need for improved guidance on data visualization in scientific publishing.
  • Effective alternatives to bar graphs, such as dot plots, box plots, and violin plots, should be promoted.
  • Authors require resources and strategies to create more informative and accurate scientific figures.