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

Interpreting Run Charts01:25

Interpreting Run Charts

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...
Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and columns,...
Review and Preview01:13

Review and Preview

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...
Run Charts01:12

Run Charts

Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For example,...
Overview of Minitab01:11

Overview of Minitab

Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to users...

You might also read

Related Articles

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

Sort by
Same author

Volume-based bias in automated measurements of lateral ventricle and hippocampal volumes of mild traumatic brain injury patients.

Neuroimage. Reports·2026
Same author

Biomimetic Macrophage Cell Membrane-Based Nanoparticles for Effective Treatment of Glioblastoma Through Boron Neutron Capture Therapy Combined With Immunotherapy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Integrated detection of cerebrospinal fluid cfDNA/cfRNA and molecular concordance with glioma characteristics.

Neuro-oncology advances·2026
Same author

Hydrothermal Synthesis and Electronic and Optical Characterization of Ag<sub>2</sub>(NH<sub>4</sub>)AsS<sub>4</sub>.

Inorganic chemistry·2026
Same author

Global policy review to identify links between climate change and antimicrobial resistance.

Public health·2026
Same author

Corrigendum to "The vulnerabilities of chemotherapy resistant pancreatic cancer revealed by organoids of pre- and post- neoadjuvant therapy" [Cancer Lett. (2026), Online ahead of print].

Cancer letters·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: May 21, 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

Analyzing Notebook Histories to Understand Data Visualization Workflows.

David Koop, Colin Brown, Hamed Alhoori

    IEEE Transactions on Visualization and Computer Graphics
    |May 19, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study analyzes notebook version histories to understand visualization design workflows. It reveals how users iterate on data manipulation and visual encodings, comparing different frameworks and the impact of AI.

    More Related Videos

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    Related Experiment Videos

    Last Updated: May 21, 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

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    Using Generative Art to Convey Past and Future Climate Transitions
    06:10

    Using Generative Art to Convey Past and Future Climate Transitions

    Published on: March 31, 2023

    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Visualization design is complex, involving iterative refinement of encodings and data transformations.
    • Code-intensive, lower-level practices in visualization design workflows are underexplored.
    • Exploratory notebook environments facilitate rapid iteration for visualization designers.

    Purpose of the Study:

    • To investigate user practices in code-intensive visualization design within notebook environments.
    • To analyze the interplay between data manipulation and visualization refinement.
    • To compare the impact of different visualization frameworks and the influence of AI on design processes.

    Main Methods:

    • Analysis of publicly available version histories of exploratory notebooks.
    • Classification of changes in visual encodings and data manipulation steps.
    • Comparative analysis of code-oriented libraries versus chart wizards.
    • Examination of temporal trends in notebook interactions, including post-AI availability.

    Main Results:

    • Users build new visualizations from templates and refine existing ones over time.
    • Specific types of changes made during visual encoding updates were classified.
    • Frameworks impact design iteration, with differences observed between code libraries and chart wizards.
    • Notebook interaction patterns have evolved, particularly with the integration of AI tools.

    Conclusions:

    • Understanding code-intensive practices is crucial for improving visualization design tools.
    • Notebooks provide valuable insights into iterative visualization development.
    • Framework choice and AI integration significantly influence visualization design workflows.