CC*IIE Engineer: Campus Cyberinfrastructure Engineer for Arizona State University
CC*IIE Engineer: Campus Cyberinfrastructure Engineer for Arizona State University
批准号:
1440600
负责人:
Francis Timmes
金额:
$39.82万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-10-01 至 2016-09-30
中文摘要
该项目支持亚利桑那州立大学(ASU)的大量不同学科的研究人员进行数据密集型研究计划。 示例数据密集型用例,如飞秒纳米微结构,生物质可再生能源,能源效率燃烧,国家EarthScope项目,天体物理学,沙漠水流和基因组学,与亚利桑那州立大学,亚利桑那州,国家和世界各地的合作者共享对更大数据集的低延迟传输的共同需求。 该项目利用ASU在校园级动态网络服务方面的现有投资,更有效地实现更大范围的多样化和分布式研究和教育数据集。 亚利桑那州立大学拥有重要的网络能力和科学DMZ,但研究小组往往需要具体的帮助,以克服最佳使用这种先进的基础设施进行研究的挑战。 这个为期两年的项目支持的校园基础设施工程师通过帮助校园研究人员充分利用先进的网络资源,倡导ASU内研究网络基础设施的不同需求,并参与国家和国际资源和组织,如XSEDE和OSG,来弥合这一差距。 因此,校园基础设施工程师加强了整个大学的网络工程和数据密集型学科的具体研究。
英文摘要
This project supports a large and diverse set of discipline specific researchers at Arizona State University (ASU) with data-intensive research programs. Example data-intensive use cases such as femtosecond nanocrystallography, biomass renewable energy, combustion for energy efficiency, the national EarthScope project, astrophysics, desert water flows, and genomics share a common need for low-latency transfer of ever-larger data sets with collaborators across ASU, Arizona, the Nation, and the world. This project leverages existing ASU investments in campus level dynamic network services to more efficiently enable a larger range of diverse and distributed research and educational data sets. ASU has significant network capability and a Science DMZ, but research groups often need specific assistance overcoming the challenges of optimally using this advanced infrastructure for their research. The campus infrastructure engineer supported by this two-year project bridges this gap by helping campus researchers make the best use of the advanced networking resources, advocating for the differing needs of research cyberinfrastructure within ASU, and engages national and international resources and organizations such as XSEDE and OSG. The campus infrastructure engineer thus strengthens network engineering and data-intensive discipline specific research across the University.
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