The major goal of the Data Management and Biostatistics Core is to establish a centralized data management system for laboratory and field data generated from the three research projects of this ICEMR. The core will also provide data analytic support and training to ICEMR consortium members, and enhance biostatistical capacity relevant to malaria research and control in Kenya and Ethiopia. We will implement established data processing procedures through a standardized framework in which all project leaders and key investigators can stay informed of the status of the inter-related and synergistic research projects, have access to validated field and laboratory data, and collaborate with biostatisticians to publish research findings. The electronic data capture (EDC) framework will be deployed primarily with online web-based data entry applications and/or offline mobile devices for to ensure data security, quality and uniformity of the research results from study sites in Kenya and Ethiopia and U.S. laboratories. The Data Management System (DMS) servers will be established in Kenya, Ethiopia and UCI, and mirrored by a cloud-based server through an internet-based on-demand computing service provider. Our multi-disciplinary ICEMR projects will collect multi-layer data that include remote sensing, ecological and geospatial features, deep sequencing data, and clinical information that will be integrated with public repositories in accordance with NIH requirements, e.g., VectorBase and PlasmoDB. With these extremely large and diverse multi-discipline datasets from the ICEMR, we will use state-of-the-art big data technologies and cloud-based distributed computing in an open source Apache Hadoop framework to enable sophisticated analyses and presentations that are not possible in a single computing server. We will also provide statistical support and training in GIS, data analysis, ecological modeling, population genetics and bioinformatics, and build infrastructure for malaria research in Kenya and Ethiopia. The Core will be directed by Dr. Ming-Chieh Lee at UCI and supported by biostatisticians and data managers in Kenya and Ethiopia.
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