Data Management and Biostatistics Core

数据管理和生物统计学核心 总结 数据管理和生物统计核心的主要目标是建立一个集中的 数据管理系统，用于实验室和现场三项研究产生的数据 ICEMR的项目。该核心还将提供数据分析支持和培训， ICEMR联合会成员，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进行研究和控制。我们将实施既定的数据处理 所有项目领导人和主要负责人都应遵守标准化框架， 研究者可以随时了解相互关联和协同作用的研究状况， 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结果。电子数据采集（EDC）框架 将主要部署在线基于Web的数据输入应用程序和/或离线移动的 用于确保研究结果的数据安全性、质量和一致性的设备 肯尼亚和埃塞俄比亚的实验室以及美国的实验室。数据管理系统（DMS） 将在肯尼亚、埃塞俄比亚和UCI建立服务器，并通过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项目将收集多层次数据，包括遥感、生态和 地理空间特征、深度测序数据和临床信息， 符合NIH要求的公共储存库，例如，VectorBase和PlasmoDB。 有了ICEMR中这些极其庞大且多样化的多学科数据集，我们将使用 最先进的大数据技术和基于云的分布式计算， 源Apache Hadoop框架，以支持复杂的分析和演示， 在单一的计算服务器中是不可能的。我们还将提供统计支持， 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， 并在肯尼亚和埃塞俄比亚为疟疾研究建立基础设施。核心将被引导 由UCI的Ming-Chieh Lee博士撰写，并得到肯尼亚生物统计学家和数据管理人员的支持 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.