Title: A Semantic Cross-Species Derived Data Management Application,
Abstract: 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 that supply data in a usable fashion
and make intuitive sense to scientists from dramatically different backgrounds
increases the complexity for developers. Further, reusing source data from
outside one's scientific domain is fraught with ambiguities in understanding
the data types, analysis methodologies, and how to combine the data with those
from other research teams. We report on the design, implementation, and
performance of a semantic data management application to support the NIMH
funded Conte Center at the University of California, Irvine. The Center is
testing a theory of the consequences of "fragmented" (unpredictable, high
entropy) early-life experiences on adolescent cognitive and emotional outcomes
in both humans and rodents. It employs cross-species neuroimaging, epigenomic,
molecular, and neuroanatomical approaches in humans and rodents to assess the
potential consequences of fragmented unpredictable experience on brain
structure and circuitry. To address this multi-technology, multi-species
approach, the system uses semantic web techniques based on the Neuroimaging
Data Model (NIDM) to facilitate data ETL functionality. 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.