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

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

17.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
17.3K
Levels of Use of a GIS01:29

Levels of Use of a GIS

456
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
456
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
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
Bar Graph01:07

Bar Graph

23.8K
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...
23.8K
Interpreting Run Charts01:25

Interpreting Run Charts

4.2K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Refining the Definition for "Low Risk" in Pulmonary Arterial Hypertension: Time to Reduce Morbidity and Mortality.

JACC. Heart failure·2026
Same author

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2026
Same author

Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation.

IEEE transactions on visualization and computer graphics·2026
Same author

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2025
Same author

Use and Usefulness of Risk Prediction Tools in Urologic Surgery: Current State and Path Forward.

Urology practice·2025
Same author

Reflections on interactive visualization of electronic health records: past, present, future.

Journal of the American Medical Informatics Association : JAMIA·2024
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: Apr 4, 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

10.6K

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics.

Charles D Stolper, Adam Perer, David Gotz

    IEEE Transactions on Visualization and Computer Graphics
    |September 11, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Progressive visual analytics allows analysts to view partial results and guide computations in real-time. This interactive approach enhances the analysis of complex datasets, improving efficiency for tasks involving large data volumes.

    More Related Videos

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.7K
    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
    10:43

    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

    Published on: June 10, 2021

    5.9K

    Related Experiment Videos

    Last Updated: Apr 4, 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

    10.6K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.7K
    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
    10:43

    Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

    Published on: June 10, 2021

    5.9K

    Area of Science:

    • Computer Science
    • Data Science
    • Human-Computer Interaction

    Background:

    • Traditional analytical workflows are inefficient for large datasets and complex algorithms.
    • Analysts often face lengthy computation times, hindering iterative analysis and parameter tuning.

    Purpose of the Study:

    • Introduce and describe the progressive visual analytics paradigm.
    • Define design goals for algorithms and visualizations in progressive visual analytics.
    • Present and evaluate a system, Progressive Insights, for analyzing event sequence data.

    Main Methods:

    • Developed a novel workflow: progressive visual analytics.
    • Adapted analytical algorithms for meaningful partial results and interactive intervention.
    • Enhanced information visualization techniques for dynamic result refinement and analyst control.
    • Built the Progressive Insights system for pattern analysis in event sequences.

    Main Results:

    • Demonstrated that progressive visual analytics enables real-time inspection of partial results.
    • Showcased interactive control for directing analytical computations towards subspaces of interest.
    • Evaluated the system with clinical researchers analyzing electronic medical records, confirming its utility.

    Conclusions:

    • Progressive visual analytics offers a viable and efficient alternative to traditional workflows for complex data analysis.
    • The paradigm effectively supports iterative analysis by allowing real-time interaction and guidance.
    • The Progressive Insights system provides a practical implementation for analyzing event sequence data, particularly in domains like healthcare.