BD Hubs: MIDWEST: SEEDCorn: Sustainable Enabling Environment for Data Collaboration
BD Hubs: MIDWEST: SEEDCorn: Sustainable Enabling Environment for Data Collaboration
批准号:
1550320
负责人:
William Gropp
金额:
$125.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-10-01 至 2019-08-31
中文摘要
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英文摘要
Catalyzed by the NSF Big Data Hub program, the Universities of Illinois, Indiana, Michigan, North Dakota, and Iowa State University have created a flexible regional Midwest Big Data Hub (MBDH), with a network of diverse and committed regional supporting partners (including colleges, universities, and libraries; non-profit organizations; industry; city, state and federal government organizations who bring data projects from multiple private, public, and government sources and funding agencies). The NSF-funded SEEDCorn project will be the foundational project to energize the activities of MBDH, leveraging partner activities and resources, coordinating existing projects, initiating 20-30 new public-private partnerships, sharing best practices and data policies, starting pilots, and helping to acquire funding. The result of SEEDCorn will be a sustainable hub of Big Data activities across the region and across the nation that enable research communities to better tackle complex science, engineering, and societal challenges, that support competitiveness of US industry, and that enable decision makers to make more informed decisions on topics ranging from public policy to economic development.The MBDH is focusing on specific strengths and themes of importance to the Midwest across three sectors: Society (including smart cities and communities, network science, business analytics), Natural & Built World (including food, energy, water, digital agriculture, transportation, advanced manufacturing), and Healthcare and Biomedical Research (which spans patient care to genomics). Integrative "rings" connect all spokes and will be organized around themes of specific MBDH strengths, including (a) Data Science, where computational and statistical approaches can be developed and integrated with domain knowledge and societal considerations that support the underlying needs of "data to knowledge," (b) services, infrastructure, and tools needed to collect, store, link, serve, and analyze complex data collections, to support pilot projects, and ultimately provide production-level data services across the hub, and (c) educational activities needed to advance the knowledge base and train a new generation of data science-enabled specialists and a more general workforce in the practice and use of data science and services. Further information on the project can be found at http://midwestbigdatahub.org.
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