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

Bar Graph01:07

Bar Graph

20.0K
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...
20.0K
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
Time-Series Graph00:54

Time-Series Graph

4.6K
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...
4.6K
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
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
Scatter Plot01:15

Scatter Plot

9.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
9.8K

You might also read

Related Articles

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

Sort by
Same author

Transmission electron microscopy characterisation of Spirulina bioplastics.

Journal of microscopy·2026
Same author

From innovation to implementation: Artificial intelligence in cognitive behaviour therapy training and supervision.

Behaviour research and therapy·2026
Same author

Mechanism of quiescent nanoplastic formation from semicrystalline polymers.

Nature communications·2025
Same author

Can AI have common sense? Finding out will be key to achieving machine intelligence.

Nature·2024
Same author

A noise audit of human-labeled benchmarks for machine commonsense reasoning.

Scientific reports·2024
Same author

Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.

ACS polymers Au·2024
Same journal

Establishment of comparative transcriptome dataset related to nitrogen use efficiency in melon.

Scientific data·2026
Same journal

A chromosome-level reference genome assembly of the King Ratsnake (Elaphe carinata).

Scientific data·2026
Same journal

A six-week longitudinal dataset of wearable and self-reported stress measurements in working adults.

Scientific data·2026
Same journal

A Multi-Regional Single-nucleus Atlas of the Huntington's Disease Brain.

Scientific data·2026
Same journal

A multimodal speech-production dataset with time-aligned articulography, EEG, audio, and vocal-tract anatomy.

Scientific data·2026
Same journal

A Wearable Motion Capture Dataset for Gait Analysis Using IMUs and Shank-Mounted Egocentric Cameras.

Scientific data·2026
See all related articles

Related Experiment Video

Updated: Sep 21, 2025

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

599

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph.

Michael E Deagen1, Jamie P McCusker2, Tolulomo Fateye3

  • 1Department of Mechanical Engineering, University of Vermont, Burlington, VT, USA. mdeagen@mit.edu.

Scientific Data
|May 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces interactive data graphics for exploring knowledge graphs. By combining SPARQL and Vega-Lite, it enables FAIR data visualization in materials science.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
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

Related Experiment Videos

Last Updated: Sep 21, 2025

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

599
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K
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

Area of Science:

  • Materials Science
  • Computer Science
  • Data Visualization

Background:

  • Graph databases integrate diverse data for knowledge representation.
  • Visual exploration of complex knowledge graphs remains a challenge.

Purpose of the Study:

  • To develop an approach for interactive visualization of knowledge graphs.
  • To enable FAIR (findable, accessible, interoperable, reusable) data visualization.

Main Methods:

  • Modeling charts using SPARQL for semantic context and Vega-Lite for visual context.
  • Synchronizing web-based interactive graphics with knowledge graphs.
  • Utilizing Uniform Resource Identifiers (URIs) for enhanced information content.

Main Results:

  • Bespoke, interactive data graphics (bar charts, scatter plots) for knowledge graph exploration.
  • A browsable gallery of published charts.
  • Demonstration in polymer nanocomposite materials science.

Conclusions:

  • The pairing of SPARQL and Vega-Lite offers an extensible framework for scientific data visualization.
  • This approach enhances the discoverability and usability of scientific data within knowledge graphs.