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

Manipulation and Analysis01:21

Manipulation and Analysis

27
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
27
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
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...
6.6K
Review and Preview01:13

Review and Preview

9.0K
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...
9.0K
Visual System01:26

Visual System

589
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
589
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Interpreting R Charts01:22

Interpreting R Charts

67
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...
67

You might also read

Related Articles

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

Sort by
Same author

The Effects of Belief Elicitation in Visual Data Analysis: A Longitudinal Classroom Study.

IEEE transactions on visualization and computer graphics·2025
Same author

A Case Report in Using a Laboratory-Based Decision Support Alert for Research Enrollment and Randomization.

Applied clinical informatics·2025
Same author

More Like Vis, Less Like Vis: Comparing Interactions for Integrating User Preferences Into Partial Specification Recommenders.

IEEE transactions on visualization and computer graphics·2025
Same author

A Typology of Decision-Making Tasks for Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Utilizing Provenance as an Attribute for Visual Data Analysis: A Design Probe With ProvenanceLens.

IEEE transactions on visualization and computer graphics·2025
Same author

Dashboard Vision: Using Eye-Tracking to Understand and Predict Dashboard Viewing Behaviors.

IEEE transactions on visualization and computer graphics·2025
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: Jul 10, 2025

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

10.2K

Preliminary Guidelines for Combining Data Integration and Visual Data Analysis.

Adam Coscia, Ashley Suh, Remco Chang

    IEEE Transactions on Visualization and Computer Graphics
    |November 20, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Integrating data during visual analysis, whether manually (ex-situ) or automatically (in-situ), showed similar task completion times. However, in-situ integration allowed more time for actual analysis and hypothesis tracking.

    More Related Videos

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.3K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    Related Experiment Videos

    Last Updated: Jul 10, 2025

    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

    10.2K
    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    6.3K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K

    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Science

    Background:

    • Data integration is crucial for consolidating disparate data sources in visual data analysis.
    • Current practices often separate data integration from core visual analytics operations (e.g., encoding, filtering).
    • This separation may hinder the analytical workflow and insight generation.

    Purpose of the Study:

    • To investigate the impact of integrating data integration directly into the visual analytics process.
    • To compare user performance and behavior between manual, ex-situ integration and automatic, in-situ integration.
    • To derive design guidelines for future visual analytics interfaces.

    Main Methods:

    • A preliminary user study comparing two interface alternatives: manual file-based ex-situ integration and automatic UI-based in-situ integration.
    • Participants completed specific and free-form tasks involving pattern discovery, insight generation, and relationship summarization across multiple files.
    • Analysis of interaction data and user feedback to evaluate task completion, time spent, and integration strategies.

    Main Results:

    • Task completion time and total interactions were comparable across both ex-situ and in-situ integration interfaces.
    • In-situ integration allowed users to dedicate more time to analysis tasks compared to ex-situ integration.
    • Distinct integration strategies and analytical behaviors emerged, influenced by the interface, affecting hypothesis generation and tracking.

    Conclusions:

    • While overall task efficiency may be similar, in-situ data integration supports deeper engagement with analysis.
    • Interface design significantly influences user strategies for hypothesis management and insight development.
    • Preliminary guidelines suggest incorporating attribute integration seamlessly within the active analysis process for enhanced visual analytics systems.