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

Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Three-Compartment Open Model01:06

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

You might also read

Related Articles

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

Sort by
Same author

Visualization Tasks for Unlabeled Graphs.

IEEE transactions on visualization and computer graphics·2026
Same author

A Design Space for Multiscale Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration.

IEEE transactions on visualization and computer graphics·2025
Same author

The Census-Stub Graph Invariant Descriptor.

IEEE transactions on visualization and computer graphics·2025
Same author

Iceberg Sensemaking: A Process Model for Critical Data Analysis.

IEEE transactions on visualization and computer graphics·2024
Same author

DeLVE into Earth's Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts.

IEEE transactions on visualization and computer graphics·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: Jun 19, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

A nested model for visualization design and validation.

Tamara Munzner1

  • 1University of British Columbia. tmm@cs.ubc.ca

IEEE Transactions on Visualization and Computer Graphics
|October 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a four-layer nested model for visualization design, emphasizing how errors cascade through the process. It offers guidance on evaluation and highlights often-overlooked design and evaluation steps.

More Related Videos

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Related Experiment Videos

Last Updated: Jun 19, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Computer Science
  • Information Visualization

Background:

  • The visualization design process is complex and often lacks a structured approach.
  • Existing models may not adequately address the interdependencies between design stages.

Purpose of the Study:

  • To present a nested model for the visualization design process.
  • To provide guidance on evaluation approaches and identify critical design steps.

Main Methods:

  • A four-layer nested model is proposed: problem domain characterization, data type abstraction, visual encoding/interaction design, and algorithm creation.
  • The model illustrates error cascading from upstream to downstream levels.

Main Results:

  • The model highlights the impact of upstream errors on subsequent design and evaluation stages.
  • It identifies specific threats to validity unique to each design level.
  • Recommendations are provided for authors and visualization venues.

Conclusions:

  • The nested model offers a structured framework for visualization design and evaluation.
  • It encourages clearer articulation of contributions and assumptions in research.
  • Increased focus on domain characterization in visualization research is recommended.