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Related Concept Videos

Bar Graph01:07

Bar Graph

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...
Time-Series Graph00:54

Time-Series Graph

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...
Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
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:
pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
Multiple Bar Graph01:07

Multiple Bar Graph

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...

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Using Generative Art to Convey Past and Future Climate Transitions
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Consistent visualizations of changing knowledge.

Hannah J Tipney1, Ronald P Schuyler, Lawrence Hunter

  • 1University of Colorado, Pharmacology, Aurora, CO.

Summit on Translational Bioinformatics
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

Biological networks evolve with new data. Retaining user customizations and visualizing provenance aids network exploration and discovery, supported by the ReOrient system.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological networks are crucial for representing complex biological data.
  • Evolving biological knowledge necessitates dynamic network representations.
  • Customization of networks by researchers presents challenges in knowledge management.

Purpose of the Study:

  • To address the challenge of evolving biological knowledge in network representations.
  • To enhance network exploration and analysis in the face of changing data.
  • To present the ReOrient system for managing knowledge change in biological networks.

Main Methods:

  • Implementing retention of user customizations in network analysis.
  • Collecting and visualizing knowledge provenance.
  • Extending the Hanalyzer system to create ReOrient.

Main Results:

  • User customizations and provenance visualization facilitate effective network exploration.
  • The ReOrient system supports network analysis amidst evolving knowledge.
  • Improved insight generation and hypothesis formulation through dynamic network exploration.

Conclusions:

  • Effective management of evolving biological knowledge is key for discovery.
  • Systematic tracking of network changes and user interactions enhances biological insights.
  • The ReOrient system offers a robust solution for dynamic biological network analysis.