Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis. Chapter uri icon


  • The National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program (SRP) aims to support university-based multidisciplinary research on human health and environmental issues related to hazardous substances and pollutants. The Texas A&M Superfund Research Program comprehensively evaluates the complexities of hazardous chemical mixtures and their potential adverse health impacts due to exposure through a number of multi-disciplinary projects and cores. One of the essential components of the Texas A&M Superfund Research Center is the Data Science Core, which serves as the basis for translating the data produced by the multi-disciplinary research projects into useful knowledge for the community via data collection, quality control, analysis, and model generation. In this work, we demonstrate the Texas A&M Superfund Research Program computational platform, which houses and integrates large-scale, diverse datasets generated across the Center, provides basic visualization service to facilitate interpretation, monitors data quality, and finally implements a variety of state-of-the-art statistical analysis for model/tool development. The platform is aimed to facilitate effective integration and collaboration across the Center and acts as an enabler for the dissemination of comprehensive ad-hoc tools and models developed to address the environmental and health effects of chemical mixture exposure during environmental emergency-related contamination events.

author list (cited authors)

  • Mukherjee, R., Onel, M., Beykal, B., Szafran, A. T., Stossi, F., Mancini, M. A., ... Pistikopoulos, E. N.

citation count

  • 3

complete list of authors

  • Mukherjee, Rajib||Onel, Melis||Beykal, Burcu||Szafran, Adam T||Stossi, Fabio||Mancini, Michael A||Zhou, Lan||Wright, Fred A||Pistikopoulos, Efstratios N

Book Title

  • 29th European Symposium on Computer Aided Process Engineering

publication date

  • January 2019