A Semantic Cross-Species Derived Data Management Application

TL;DR

This paper presents a semantic data management system designed to handle complex, multi-species, multi-disciplinary research data, improving data integration and usability for diverse scientific teams.

Contribution

It introduces a semantic web-based application using NIDM to support cross-species, multi-technology data management in neuroscience research.

Findings

Enables low-cost, easy-to-maintain data management

Facilitates semantic understanding across diverse datasets

Supports complex multi-species research workflows

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.