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为生物医学应用提供高效可靠的数据管理

Ivan Pribec¹, Stephan Hachinger¹, Mohamad Hayek¹

¹Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ-BAdW), Munich, Germany.

Methods in molecular biology (Clifton, N.J.)

|September 13, 2023

概括

在生物医学的高性能计算 (HPC) 中,现代研究数据管理 (RDM) 强调了 FAIR 数据原则. 像CompBioMed和LEXIS这样的项目证明了在超大规模平台上实现可访问,弹性数据工作流和紧急计算的实际实施.

科学领域:

生物医学研究的研究.
计算科学是一种计算科学.
数据科学是数据科学.

背景情况:

现代研究需要强大的数据管理,特别是在利用高性能计算 (HPC) 的生物医学中.
公平的数据原则 (可查找,可访问,可互操作,可重复使用) 对于有效的RDM至关重要.
挑战包括数据格式,出版,注释,自动化管理,HPC基础设施和文件传输.

研究的目的:

讨论研究数据管理 (RDM) 在高性能计算 (HPC) 环境中的生物医学应用中的挑战和要求.
突出FAIR数据原则和实际实施的重要性.
展示自动化数据移动,元数据质量和弹性工作流程的工具和方法.

主要方法:

讨论数据格式,出版平台,注释方案和HPC中的自动化数据管理.
在HPC中心内解释数据基础设施,文件传输和分期方法.
探索EUDAT组件,元数据的本体学和工作流编排平台 (例如LEXIS,YORC).

主要成果:

"CompBioMed"项目是实施RDM原则和工具的实际例子.
在LEXIS项目开发了一个HPC-云融合平台大数据应用程序,提高可访问性.
通过检查点,重复运行和数据复制实现了弹性工作流,从而实现了紧急计算.

结论:

在生物医学HPC中有效的RDM需要遵守FAIR原则和用于数据管理和工作流程编排的先进工具.
像CompBioMed和LEXIS这样的项目证明了这些概念的成功整合,改善了可访问性,并使紧急计算成为可能.
在HPC环境中,开发本体学和用户友好的平台是管理复杂的生物医学数据的关键.

关键词:

生物医学是生物医学.超级尺度是一个超级尺度.公平的原则公平的原则高性能计算的高性能计算.研究数据管理研究数据管理弹性分布式工作流程

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