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

Scatter Plot01:15

Scatter Plot

12.6K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
12.6K
Time-Series Graph00:54

Time-Series Graph

5.6K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.6K
Multiple Bar Graph01:07

Multiple Bar Graph

10.5K
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...
10.5K
Modified Boxplots00:57

Modified Boxplots

11.7K
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...
11.7K
Interpreting R Charts01:22

Interpreting R Charts

420
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...
420
Boxplot01:12

Boxplot

14.5K
Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
14.5K

You might also read

Related Articles

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

Sort by
Same author

Business Data Visualization, Beyond the Boring.

IEEE computer graphics and applications·2024
Same author

Average Estimates in Line Graphs Are Biased Toward Areas of Higher Variability.

IEEE transactions on visualization and computer graphics·2023
Same author

Notebooks for Data Analysis and Visualization: Moving Beyond the Data.

IEEE computer graphics and applications·2023
Same author

Difficulty limits of visual mental imagery.

Cognition·2023
Same author

Symmetry and spatial ability enhance change detection in visuospatial structures.

Memory & cognition·2022
Same author

More Than Meets the Eye: A Closer Look at Encodings in Visualization.

IEEE computer graphics and applications·2022
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

The Connected Scatterplot for Presenting Paired Time Series.

Steve Haroz, Robert Kosara, Steven L Franconeri

    IEEE Transactions on Visualization and Computer Graphics
    |November 25, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Connected scatterplots effectively visualize paired time series data, with viewers preferring them over traditional formats. However, some misinterpretations occur, necessitating further research for clarity.

    More Related Videos

    Rapid Analysis and Exploration of Fluorescence Microscopy Images
    11:41

    Rapid Analysis and Exploration of Fluorescence Microscopy Images

    Published on: March 19, 2014

    12.8K
    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    1.3K

    Related Experiment Videos

    Last Updated: Mar 29, 2026

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    2.0K
    Rapid Analysis and Exploration of Fluorescence Microscopy Images
    11:41

    Rapid Analysis and Exploration of Fluorescence Microscopy Images

    Published on: March 19, 2014

    12.8K
    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    1.3K

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Information Design

    Background:

    • Connected scatterplots are increasingly used in news media to present paired time series data.
    • The intuition is that this visualization format is both understandable and engaging for the public.

    Purpose of the Study:

    • To describe how paired time series relationships appear in connected scatterplots.
    • To evaluate audience understanding and misinterpretations of trends shown in this format.
    • To compare viewer preference for connected scatterplots versus traditional graph formats.

    Main Methods:

    • Descriptive analysis of connected scatterplot representations.
    • Qualitative evaluation of trend comprehension.
    • Quantitative measurement of misinterpretation types and frequency.
    • Empirical assessment of viewer preference.

    Main Results:

    • Low-complexity connected scatterplots are generally understood with minimal explanation.
    • Viewers show a bias towards inspecting connected scatterplots over traditional formats.
    • Specific types of misinterpretations were identified and documented.

    Conclusions:

    • Connected scatterplots can be an effective visualization tool when complexity is managed.
    • Viewer preference suggests potential for wider adoption, but understanding challenges remain.
    • Further research is needed to mitigate misinterpretations for unfamiliar audiences.