Building a biomedical cyberinfrastructure for collaborative research
Schad, P., Mobley, L., & Hamilton, C. (2011). Building a biomedical cyberinfrastructure for collaborative research. American Journal of Preventive Medicine, 40(5, Suppl. 2), S144-S150. https://doi.org/10.1016/j.amepre.2011.01.018
For the potential power of genome-wide association studies (GWAS) and translational medicine to be realized, the biomedical research community must adopt standard measures, vocabularies, and systems to establish an extensible biomedical cyberinfrastructure. Incorporating standard measures will greatly facilitate combining and comparing studies via meta-analysis. Incorporating consensus-based and well-established measures into various studies should reduce the variability across studies due to attributes of measurement, making findings across studies more comparable.
This article describes two well-established consensus-based approaches to identifying standard measures and systems: PhenX (consensus measures for phenotypes and eXposures), and the Open Geospatial Consortium (OGC). NIH support for these efforts has produced the PhenX Toolkit, an assembled catalog of standard measures for use in GWAS and other large-scale genomic research efforts, and the RTI Spatial Impact Factor Database (SIFD), a comprehensive repository of geo-referenced variables and extensive meta-data that conforms to OGC standards. The need for coordinated development of cyberinfrastructure to support measures and systems that enhance collaboration and data interoperability is clear; this paper includes a discussion of standard protocols for ensuring data compatibility and interoperability. Adopting a cyberinfrastructure that includes standard measures and vocabularies, and open-source systems architecture, such as the two well-established systems discussed here, will enhance the potential of future biomedical and translational research. Establishing and maintaining the cyberinfrastructure will require a fundamental change in the way researchers think about study design, collaboration, and data storage and analysis.