Data Management and Biostatistics Core

数据管理和生物统计核心 摘要 数据管理和生物统计核心的主要目标是建立一个集中的 三项研究产生的实验室和现场数据的数据管理系统 这座ICEMR的项目。该核心还将提供数据分析支持和培训，以 ICEMR联盟成员，并加强与疟疾有关的生物统计能力 在肯尼亚和埃塞俄比亚的研究和控制。我们将实施既定的数据处理 程序通过标准化框架，在该框架中所有项目负责人和关键人员 研究人员可以随时了解相互关联和协同研究的状况 项目，可以访问经过验证的现场和实验室数据，并与 生物统计学家发表研究成果。电子数据捕获(EDC)框架 将主要通过基于网络的在线数据输入应用程序和/或离线移动设备进行部署 确保研究成果数据安全、质量和一致性的设备 肯尼亚和埃塞俄比亚的地点以及美国的实验室。数据管理系统(DMS) 将在肯尼亚、埃塞俄比亚和UCI建立服务器，并由基于云的服务器进行镜像 通过基于互联网的按需计算服务提供商。我们的多学科 ICEMR项目将收集多层数据，包括遥感、生态和 地理空间要素、深度测序数据和临床信息将与 符合NIH要求的公共存储库，例如VectorBase和PlamoDB。 利用来自ICEMR的这些极其庞大和多样化的多学科数据集，我们将使用 一流的大数据技术和基于云的分布式计算 提供Apache Hadoop框架源代码，以支持复杂的分析和演示 在单个计算服务器中是不可能的。我们还将提供统计支持和 在地理信息系统、数据分析、生态建模、种群遗传学和生物信息学方面的培训， 并为肯尼亚和埃塞俄比亚的疟疾研究建设基础设施。核心将被指导 由UCI的李明杰博士撰写，并得到肯尼亚生物统计学家和数据经理的支持 和埃塞俄比亚。

Data Management and Biostatistics Core SUMMARY 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.