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

11.1K
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:
11.1K
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

505
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
505
Arrhenius Plots02:34

Arrhenius Plots

46.8K
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...
46.8K
Residual Plots01:07

Residual Plots

6.5K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
6.5K
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

1.2K
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
1.2K
Bode Plots01:26

Bode Plots

1.3K
Bode plots are graphical tools that use logarithmic scales for frequency on the x-axis and gain in decibels on the y-axis. This logarithmic method allows a wide range of frequencies to be compactly displayed, enabling the analysis of component effects on circuit behavior across a broad frequency spectrum.
A network function represents the ratio of a system's output to its input, with the magnitude and phase angle derived from the complex network function. The decibel logarithmic gain is...
1.3K

You might also read

Related Articles

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

Sort by
Same author

LoGCC: Local-to-Global Correlation Clustering for Scalar Field Ensembles.

IEEE transactions on visualization and computer graphics·2025
Same author

Risk of genitourinary late effects after radiotherapy for prostate cancer associated with early changes in bladder shape.

Physics and imaging in radiation oncology·2025
Same author

Paper-based radial anatomy puzzles as educational tools: A pilot study at a dental school.

Medical teacher·2025
Same author

Playful Learning in Computer Graphics.

IEEE computer graphics and applications·2025
Same author

TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance.

IEEE transactions on visualization and computer graphics·2025
Same author

Squishicalization: Exploring Elastic Volume Physicalization.

IEEE transactions on visualization and computer graphics·2025
Same journal

Two-phase Impulse Fluid on Particle Flow Map.

IEEE transactions on visualization and computer graphics·2026
Same journal

FGO-SLAM++: Real-time Geometry-Aware Gaussian SLAM with Continuous Opacity Field.

IEEE transactions on visualization and computer graphics·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

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

Related Experiment Video

Updated: Jan 27, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

10.0K

Relaxing Dense Scatter Plots with Pixel-Based Mappings.

Renata G Raidou, M Eduard Groller, Martin Eisemann

    IEEE Transactions on Visualization and Computer Graphics
    |March 21, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Pixel-Relaxed Scatter Plots enhance dense data visualization by using pixel-based mappings to avoid overplotting and improve space coverage. This novel technique offers better insight into data motifs and clusters compared to traditional scatter plots.

    More Related Videos

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
    11:57

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

    Published on: May 20, 2013

    13.9K
    Mechanical Control of Relaxation Using Intact Cardiac Trabeculae
    07:51

    Mechanical Control of Relaxation Using Intact Cardiac Trabeculae

    Published on: February 17, 2023

    1.5K

    Related Experiment Videos

    Last Updated: Jan 27, 2026

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
    14:58

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

    Published on: June 2, 2010

    10.0K
    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
    11:57

    Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

    Published on: May 20, 2013

    13.9K
    Mechanical Control of Relaxation Using Intact Cardiac Trabeculae
    07:51

    Mechanical Control of Relaxation Using Intact Cardiac Trabeculae

    Published on: February 17, 2023

    1.5K

    Area of Science:

    • Data Visualization
    • Computer Graphics
    • Information Visualization

    Background:

    • Scatter plots are standard for bivariate data visualization but suffer from overplotting in dense datasets.
    • Existing solutions like density plots may still have data overlap or empty regions.
    • Pixel-based techniques can improve data density representation and spatial coverage.

    Purpose of the Study:

    • To introduce Pixel-Relaxed Scatter Plots, a novel variant for improved dense scatter plot visualization.
    • To address overplotting and optimize the use of the plotting canvas for better data insight.
    • To enhance the display of data motifs and clusters in large datasets.

    Main Methods:

    • Developed a new scatter plot variant using pixel-based, space-filling mappings.
    • Employed various methods for mapping scatter plot points to pixels.
    • Visually presented the pixel-based mapping for enhanced data representation.
    • Evaluated the technique on synthetic and realistic datasets.

    Main Results:

    • Pixel-Relaxed Scatter Plots effectively mitigate overplotting in dense datasets.
    • The technique optimizes space coverage, providing better insight into data motif size and presence.
    • User evaluations indicate improved visualization compared to traditional scatter plots.
    • Demonstrated suitability for various data visualization tasks.

    Conclusions:

    • Pixel-Relaxed Scatter Plots offer a valuable enhancement for visualizing dense bivariate data.
    • The method improves upon traditional scatter plots by optimizing canvas utilization and reducing clutter.
    • This technique provides clearer insights into data distribution, clusters, and outliers.