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数据系统与技术的整合提升了一个超级基金研究中心的研究与协作水平。

Integration of data systems and technology improves research and collaboration for a superfund research center.

作者信息

Hobbie Kevin A, Peterson Elena S, Barton Michael L, Waters Katrina M, Anderson Kim A

机构信息

Department of Environmental & Molecular Toxicology, Oregon State University, Corvallis, OR, USA.

出版信息

J Lab Autom. 2012 Aug;17(4):275-83. doi: 10.1177/2211068212448428. Epub 2012 May 31.

DOI:10.1177/2211068212448428
PMID:22651935
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3460553/
Abstract

Large collaborative centers are a common model for accomplishing integrated environmental health research. These centers often include various types of scientific domains (e.g., chemistry, biology, bioinformatics) that are integrated to solve some of the nation's key economic or public health concerns. The Superfund Research Center (SRP) at Oregon State University (OSU) is one such center established in 2008 to study the emerging health risks of polycyclic aromatic hydrocarbons while using new technologies both in the field and laboratory. With outside collaboration at remote institutions, success for the center as a whole depends on the ability to effectively integrate data across all research projects and support cores. Therefore, the OSU SRP center developed a system that integrates environmental monitoring data with analytical chemistry data and downstream bioinformatics and statistics to enable complete "source-to-outcome" data modeling and information management. This article describes the development of this integrated information management system that includes commercial software for operational laboratory management and sample management in addition to open-source custom-built software for bioinformatics and experimental data management.

摘要

大型合作中心是开展综合环境健康研究的常见模式。这些中心通常包括各种科学领域(如化学、生物学、生物信息学),它们相互整合以解决国家的一些关键经济或公共卫生问题。俄勒冈州立大学(OSU)的超级基金研究中心(SRP)就是这样一个于2008年成立的中心,旨在利用现场和实验室的新技术研究多环芳烃新出现的健康风险。通过与偏远机构的外部合作,整个中心的成功取决于能否有效地整合所有研究项目和支持核心的数据。因此,OSU SRP中心开发了一个系统,将环境监测数据与分析化学数据以及下游生物信息学和统计学相结合,以实现完整的“从源到结果”数据建模和信息管理。本文描述了这个综合信息管理系统的开发,该系统除了包括用于生物信息学和实验数据管理的开源定制软件外,还包括用于操作实验室管理和样品管理的商业软件。

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