Flexible Specification of Data Models for Neuroscience Databases

Scientific progress depends increasingly on collaborative efforts that involve exchange of data and re-analysis of previously recorded data. With increasing complexity of the data it becomes more difficult to access both data and metadata for application of specific analysis methods, for exchange with collaborators, or for further analysis some time after the initial study was completed. This effort causes a major barrier to fully exploit the scientific potential of experimental data. In order to make data analysis, re-analysis, and data sharing efficient, data together with metadata should be managed and accessed in a unified and reproducible way, so that the researcher can focus on the scientific questions rather than on problems of data management. We present a met model for formal specification of data models using concepts of entity relationship diagrams and database system catalogs. We are currently applying this metamodel to a data management system which is based on relational database technology, together with mechanisms to account for the heterogeneity of electrophysiological data(www.g-node.org/data). This approach provides (automatically generated) interfaces to analysis tools and programming languages that are commonly used in neurophysiology. It thus will enable researchers to seamlessly integrate data access into their daily laboratory workflow and efficiently perform management and selection of data in a systematic and largely automatized fashion for data sharing and analysis.