Common data model for neuroscience data and data model exchange

Objective: Generalizing the data models underlying two prototype neurophysi ology databases, the authors describe and propose the Common Data Model (CD M) as a framework for federating a broad spectrum of disparate neuroscience information resources. Design: Each component of the CDM derives from one of five superclasses-dat a, site, method, model, and reference-or from relations defined between the m. A hierarchic attribute-value scheme for metadata enables interoperabilit y with variable tree depth to serve specific intra- or broad interdomain qu eries. To mediate data exchange between disparate systems, the authors prop ose a set of XML-derived schema for describing not only data sets but data models. These include biophysical description markup language (BDML), which mediates interoperability between data resources by providing a meta-descr iption for the CDM. Results: The set of superclasses potentially spans data needs of contempora ry neuroscience. Data elements abstracted from neurophysiology time series and histogram data represent data sets that differ in dimension and concord ance. Site elements transcend neurons to describe subcellular compartments, circuits, regions, or slices; non-neuroanatomic sites include sequences to patients. Methods and models are highly domain-dependent. Conclusions: True federation of data resources requires explicit public des cription, in a metalanguage, of the contents, query methods, data formats, and data models of each data resource. Any data model that can be derived f rom the defined superclasses is potentially conformant and interoperability can be enabled by recognition of BDML-described compatibilities. Such meta -descriptions can buffer technologic changes.