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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

14.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
14.2K
Time-Series Graph00:54

Time-Series Graph

4.5K
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...
4.5K
Multiple Bar Graph01:07

Multiple Bar Graph

8.1K
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...
8.1K
Review and Preview01:13

Review and Preview

9.5K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
9.5K
Bar Graph01:07

Bar Graph

19.9K
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...
19.9K
Ogive Graph01:07

Ogive Graph

5.9K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
5.9K

You might also read

Related Articles

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

Sort by
Same author

Treatment of Patients With Femoroacetabular Impingement Syndrome Using a Pelvic Tilt-Focused Exercise Program: A Prospective Cohort.

The American journal of sports medicine·2026
Same author

Comparing the Efficacy and Safety of Intra-articular Injection Treatments for Hip Osteoarthritis: A Systematic Review and Network Meta-analysis.

Orthopaedic journal of sports medicine·2026
Same author

Exploring MLLMs Perception of Network Visualization Principles.

IEEE transactions on visualization and computer graphics·2026
Same author

What Are Orthopaedic Sports Medicine Knee Surgeons Watching? Exploring Trends in Online Surgical Technique Videos on the VuMedi Education Platform.

Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews·2026
Same author

Consideration of Human Values in Extended Reality: A Systematic Review.

IEEE transactions on visualization and computer graphics·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·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
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: Sep 19, 2025

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

GraphTrials: Visual Proofs of Graph Properties.

Henry Forster, Felix Klesen, Tim Dwyer

    IEEE Transactions on Visualization and Computer Graphics
    |June 6, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces visual proofs for graph properties, using specialized visualizations called "visual certificates." These certificates leverage human perception to verify AI-generated assertions about graph data, enhancing trustworthiness.

    More Related Videos

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    595
    Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
    13:04

    Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

    Published on: January 18, 2022

    4.2K

    Related Experiment Videos

    Last Updated: Sep 19, 2025

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.3K
    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    595
    Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
    13:04

    Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

    Published on: January 18, 2022

    4.2K

    Area of Science:

    • Computer Science
    • Data Visualization
    • Graph Theory

    Background:

    • Graph and network visualization is crucial for analyzing relational data across diverse domains.
    • The rise of AI necessitates trustworthy and explainable methods for validating AI-generated insights from graph data.

    Purpose of the Study:

    • To introduce the concept of visual proofs for graph properties.
    • To establish a framework for defining and creating visual proofs.
    • To explore the role of visualization in verifying AI assertions about graphs.

    Main Methods:

    • Developed a framework defining visual proofs for graph properties.
    • Introduced 'visual certificates'—specialized visualizations designed for perceptual verification.
    • Analyzed the relationship between visual complexity, cognitive load, and complexity theory.
    • Proposed a classification system for visual proof complexity.

    Main Results:

    • Defined visual proofs and visual certificates for graph properties.
    • Demonstrated how visual certificates can leverage pre-attentive processing for efficient verification.
    • Classified visual proofs based on their complexity.
    • Provided examples of visual certificates for various graph problems.

    Conclusions:

    • Visual proofs offer a novel approach to validating graph properties, particularly for AI-generated claims.
    • Visual certificates can enhance the trustworthiness and explainability of graph analysis.
    • Further research is needed to explore limitations and expand the scope of visual proofs.