Data management and analysis core

Emory ACE数据管理和分析核心的目标是创建一个最佳的科学和技术环境，用于从多个领域共同注册的密集采样纵向数据集创建社会参与的增长图表，以探索自闭症的风险和弹性机制，这是项目I-V以及整个项目的关键目标。为了实现这一目标，核心将提供共享的资源，设施和专业知识，用于数据收集，处理，分析，存储和检索，以服务于整个计划项目，以及作为一个公共存储库，用于在各个项目之间共享数据并将结果传输到NDAR。核心的具体目标是：（1）数据管理：满足整个计划项目独特的具有挑战性的数据处理，管理，存储和传输需求;（2）数据分析：提供一个通用的方法框架和共享的计算基础设施，用于对项目1 - 5收集的实验措施进行最先进的发展概况分析和统计分析;（3）培训和可靠性：提供跨项目采用的所有新数据分析技术的共同培训，并确保这些技术在跨项目中一致和可靠地部署，使用适当的数据管理程序;以及（4）质量控制：确保项目产生的所有数据都具有统一的和非常高的质量，适合出版，提交给NDAR，并立即供其他科学家使用。关键人员将提供数学分析，生物统计学，软件工程和数据处理方面的专业知识，以实现每个项目的具体目标。将提供共享设施和基础设施，以支持每个项目的数据管理和分析需要，包括一个中央超级计算设施、当地工作站和一套为每个项目所需数据处理量身定制的软件工具。数据管理将集中在一个通用的数据服务器上，使用RexDB，这是一个定制设计的数据库，委托Prometheus Research专门处理大量的临床，行为和实验数据集，并将其传输到NDAR。

The objective of the Emory ACE Data Management & Analysis Core is to create an optimal scientific and technical environment for creating growth charts of social engagement from densely-sampled longitudinal data sets co-registered across multiple domains in order to explore mechanisms of risk and resilience in autism, a key aim of Projects l-V as well as the whole program project. To accomplish this goal, the Core will provide shared resources, facilities and expertise for data collection, processing, analysis, storage and retrieval to serve the entire program project, as well as acting as a common repository for sharing data between individual projects and transmitting results to NDAR. The specific aims of the Core are: (1) Data Management: to meet the uniquely challenging data processing, management, storage and transmission needs of the entire program project; (2) Data Analysis: to provide a common methodological framework and shared computational infrastructure for carrying out state-of-the-art developmental profiling and statistical analysis of experimental measures collected across Projects l-V; (3) Training and Reliability: to provide common training in all of the novel data analysis techniques employed across projects, and to ensure that those techniques are deployed consistently and reliably across projects, using appropriate procedures for data management; and (4) Quality Control: to ensure that all data arising from the program project are of uniformly and exceptionally high quality, suitable for publication, submission to NDAR, and immediate use by other scientists. Key personnel will provide expertise in mathematical analysis, biostatistics, software engineering, and data processing needed to achieve the specific aims of each project. Shared facilities and infrastructure will be made available to support the data management and analysis needs of each project, including a central supercomputing facility, local workstations, and a suite of software tools customized for the data processing required in each project. Data management will be centralized on a common data server using RexDB, a custom-designed database commissioned from Prometheus Research specifically to handle large-volume clinical, behavioral, and experimental data sets, and their transmission to NDAR.