Common Data Model for Neuroscience Data and Data Model Exchange
- Daniel Gardner,
- Kevin H Knuth,
- Michael Abato,
- Steven M Erde,
- Thomas White,
- Robert DeBellis,
- Esther P Gardner
- Affiliations of the authors: Weill Medical College of Cornell University (DG, KHK, MA, SME, TW, RD) and New York University School of Medicine (EPG), New York, New York
- Correspondence and reprints: Daniel Gardner, PhD, Department of Physiology and Biophysics, Weill Medical College of Cornell University, 1300 York Avenue, New York, NY 10021; e-mail: 〈dan{at}aplysia.med.cornell.edu〉
- Received 19 June 2000
- Accepted 15 September 2000
Abstract
Objective Generalizing the data models underlying two prototype neurophysiology databases, the authors describe and propose the Common Data Model (CDM) 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—data, site, method, model, and reference—or from relations defined between them. A hierarchic attribute-value scheme for metadata enables interoperability with variable tree depth to serve specific intra- or broad inter-domain queries. To mediate data exchange between disparate systems, the authors propose 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-description for the CDM.
Results The set of superclasses potentially spans data needs of contemporary neuroscience. Data elements abstracted from neurophysiology time series and histogram data represent data sets that differ in dimension and concordance. 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 description, in a metalanguage, of the contents, query methods, data formats, and data models of each data resource. Any data model that can be derived from the defined superclasses is potentially conformant and interoperability can be enabled by recognition of BDML-described compatibilities. Such metadescriptions can buffer technologic changes.
Footnotes
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This work was supported by the Human Brain Project through grant MH57153 from the National Institute of Mental Health, grant NS36043 from the National Institute of Neurological Diseases and Stroke, and grant BIR/DBI-9506171 from the National Science Foundation
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Preliminary versions of this work were presented, and published only in abstract form related to, the 1998 and 1999 annual meetings of the Society for Neuroscience and the 1999 and 2000 annual meetings of the Biophysical Society.31 35
