Managing dynamic information in large multi-site, multi-species, and multi-discipline consortia is a challenging task for data management applications. Often in academic research studies the goals for informatics teams are to build applications that provide extract-transform-load (ETL) functionality to archive and catalog source data that has been collected by the research teams. In consortia that cross species and methodological or scientific domains, building interfaces which supply data in a usable fashion and make intuitive sense to scientists from dramatically different backgrounds increases the complexity for developers. In this work we have built a multi-species data management system which uses semantic web techniques based on the Neuroimaging Data Model (NIDM ;Figure). We find this approach enables a low-cost, easy to maintain, and semantically meaningful information management system, enabling the diverse research teams to access and use the data.
Citation: Keator, D.B. et al., (2017). A Semantic Cross-Species Derived Data Management Application. Data Science Journal. 16, p.45. DOI: http://doi.org/10.5334/dsj-2017-045