![]() For such systems, it is therefore desirable to build a software layer that minimizes the possibility of incorrect metadata, in other words, a metadata management system (MMS). ![]() If, by accident, a metadata element is defined incorrectly, subtle or major malfunctions manifest in the system's operation for example, errors may creep into the data, as discussed shortly. In highly functional metadata-driven systems, the interrelationships within the metadata become complex, and metadata maintenance becomes challenging. While harder to create, metadata-driven software ultimately requires fewer modifications as the domain evolves. At runtime, software uses this information to perform various tasks such as data validation, interface generation, and customization. One way to meet such requirements is to use a generic (“entity-attribute-value” or EAV) data model 1, 2 and an architecture driven by metadata, which is loosely defined as “data that describe data.” 3, 4 Basically, one captures information such as domain-specific descriptions, application conditions, parameters, and methods in a repository. Database applications for the management of scientific and clinical data should be easily modifiable and adapt to scientific advances.
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