Data Visulations
~What is Data Visualization?~
- Data visualization is the graphical display of information for the purpose of sense-making and communication.
- The information is abstract in that it describes things that are not physical.
- Although data visualization usually features relationships between quantitative values, it can also display relationships that are not quantitative.
[Examples of Data Visualization Tools:]
- Gephi
- NodeXL
- Cytoscape
- yEd
- Prezi
Data visualization programs can be very useful because they give you many different ways to display all different kinds of information. They can even make the information easier to read/understand.
Exmaple 1
Gephi is an open source software for network visualization and analysis. It helps users to reveal patterns and trends, highlight outliers and tells stories with their data
The choices made in the visual representation affect how the data is read by using 3D technology to display large graphs in time and to speed up the exploration. It combines built-in functionalities and flexible architecture to:
- explore
- analyze
- spatialize
- filter
- cluster
- manipulate
- export
any type of network.
For more information/examples visit:
Example 2
NodeXL Basic is a free, open source template for Microsoft Excel 2007, 2010, 2013 and 2016 that makes it easy to explore network graphs. With this technology you can enter a network edge list in a worksheet, click a button and see your graph, all within a Excel window.
The choices made in the data visulaization affect how the data is read by providing easy access to social media network data streams, advanced network metrics, and text and sentiment analysis, and powerful report generation.
NodeXL Pro makes creating insights into social media streams simple.
For more information/examples visit:
Video Example
Example 3
Cytoscape is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
The choices made in the data visual representation affect how the data is read by making it easy to load molecular and genetic data sets in many formats ,project and integrate global datasets and functional annotations, establish powerful visual mappings across these data, and many more.
For more information/examples visit: