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The good news is that it is now possible to both create large databases of information about images and apply existing data standards. The bad news is that each of these databases is protected by proprietary formats that do not communicate with one another, and standards do not yet exist for all image types. Researchers from each of the disciplines under the umbrella called imaging refer to the images in a unique way, using different vocabulary. Wouldn't it be nice if a scientist could simply ask questions without regard to disciplinary boundaries and harness all of the available data about tissue, cells, genes, proteins, and other parts of the body to prove or disprove a hypothesis?

One promise of big data , such as that represented by the large but mutually-exclusive imaging data sets mentioned so far, is that is that data mashups can integrate two or more data sets in a single graphical interface so that doctors, pathologists, radiologists, and laboratory technicians can make connections that improve outcomes for patients. Such mashups require and await technical solutions in the areas of data standards and software development. A significant start to all of these technical solutions are the sub-projects of the National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP).

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