XML (eXtensible Markup Language) is standard for sorting information that has the flexibility needed to handle complex data. It is similar to HTML (HyperText Markup Language) in that it uses tags to denote information. It is a simple text file which means that you can use a text editor to view the raw data. Although this may not be the most efficient way to look at an XML file, this allows you to peek at the information in any XML file without any special software. There are three areas that show how useful XML is for Statistics:
1. MathML. MathML is a joint standard for displaying mathematical equations. It allows you to import equations written in this format to your own mathematics program where you can plug in values for the variables or otherwise manipulate the data.
2. Scalable Vector Graphics. Scalable Vector Graphs is another standard for displaying graphs and images. With the right software, you can manipulate images that others have produced.
3. mageML. MageML (Microarray Analysis of Gene Expression Markup Language) is a standard for storing information from a microarray experiment. It stores all the complex information from a microarray experiment in a single self-documenting package.
Resources:
- Adobe SVG Zone
- Affymetrix - Manual - Probe Set Data in MAGE-ML Format
- Cover Pages- MicroArray and Gene Expression Markup Language
- Genome Biology | Design and implementation of microarray gene expression markup language
- A Gentle Introduction to MathML
- Introduction to Scalable Vector Graphics
- MathML Central - A Wolfram Web Resource
- MathML - What’s in it for us?
- Major MAGE-ML Packages
- MicroArray and Gene Expression - MAGE
- MicroArray and Gene Expression Markup Language (MAGE-ML)
- Scalable Vector Graphics (SVG)
- SchemaSoft MathML to SVG Converter
- The State of MathML - Mathematically Speaking (and Stuttering)
- Welcome to the Maplesoft MathML Home Page
- XML.com- An Introduction to Scalable Vector Graphics
You can find an earlier version of this page on my original website.