  The extraction of information form high-throughput experiments is a key
aspect of modern biology. Early in the development of microarray technology,
researchers recognized that the size of the datasets and the limitations of
both computational and visualization techniques restricted their ability to
find the biological meaning hidden in the data. In addition, most researchers
wanted to make their datasets accessible to others. This resulted in the
development of new and advanced data storage, analysis, and visualization tools
enabling the cross-platform validation of the experiments and the
identification of previously undetected patterns. In order to reap the benefits
of this microarray data, researchers have needed to implement database
management systems providing integration of different experiments and data
types. Moreover, it was necessary to standardize the basic data structure and
experimental techniques for the standardization of microarray platforms. In
this chapter, we introduce the reader to the major concepts related to the use
of controlled vocabularies (ontologies), the definition of Minimum Information
About a Microarray Experiment (MIAME) and provide an overview of different
microarray data management strategies in use today. We summarize the main
characteristics of microarray data storage and sharing strategies including
warehouses, datamarts, and federations. The fundamental challenges involved in
the distribution, and retrieval of microarray data are presented, along with an
overview of some emerging technologies.
