MRI: Acquisition of a Regional Resource for Long-Term Archiving of Large Scale Research Data Collections

MRI：获取区域资源以长期存档大规模研究数据集

• 批准号: 1828567
• 负责人: Henry Neeman
• 金额: $ 96.78万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828567&HistoricalAwards=false
• 关键词: MRI; Acquisition; Regional; Resource; Long

The project will support University of Oklahoma (OU)'s acquisition, deployment and maintenance of a large-scale storage instrument -- the OU & Regional Research Store (OURRstore). This instrument will enable faculty, staff, postdocs, graduate students and undergraduates to pursue data-intensive research (by building large and growing data collections), and to share and publish these datasets (which they can make discoverable and searchable). Science, Technology, Engineering and Mathematics (STEM) research is increasingly data-intensive, with massive growth of research data collections. Yet a substantial fraction of universities and colleges have been underprepared not only for the volume, velocity and variety of data, but especially for long term stewardship of rapidly growing data collections. In addition, many STEM research projects require substantial storage during their experiments, so much so that holding it all on disk is too expensive to be practical. This challenge is quickly becoming more acute, because disk prices are now improving much more slowly than in the past.OURRstore is a large-scale storage instrument, consisting of a substantial tape library, as well as crucial support subsystems such as servers, disk, network components and software. OURRstore allows the massive growth of storage capacity needed to address the requirements of the large and broad research community that it serves. Via an innovative, low cost business model, OURRstore offers a cost-effective data storage and access and solution. The research topics supported by the instrument include: weather forecasting, weather radar and weather data mining, including for severe storms such as tornadoes and hurricanes; earthquake triggers and seismology; data and modeling for agriculture, forestry, water and earth ecosystems; coastal simulation; molecular systems that control growth and development of vegetation; microbial contamination of infrastructure; effects of repeated stress on organisms; RNA processing in mitochondria; nanotechnology; visual neuroscience; physiological adaptation to extreme environments; astrophysical objects and stellar atmospheres; malware cybersecurity; influence of microblogs on social media. OURRstore maximizes its impact across the US in the following ways. First, it serves as a national model for affordable, large scale, long term, multi-institutional storage. Second, the OURRstore community includes a substantial number of people from underrepresented populations: over 200 research team members are one or more of the following: women, African Americans, Hispanics, Native Americans, disabled, and US veterans. Third, the instrument will enhance OU's current "Supercomputing in Plain English" webinar series, a popular outreach program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将支持俄克拉荷马州大学（俄克拉何马州）购买、部署和维护一个大型存储仪器-俄克拉何马州区域研究商店（OURRstore）。该工具将使教职员工、博士后、研究生和本科生能够进行数据密集型研究（通过建立大型且不断增长的数据集），并共享和发布这些数据集（他们可以使这些数据集可发现和可搜索）。科学、技术、工程和数学（STEM）研究的数据密集度越来越高，研究数据收集量大幅增长。然而，相当一部分大学和学院不仅在数据的数量、速度和种类方面准备不足，而且特别是在对快速增长的数据收集的长期管理方面。此外，许多STEM研究项目在实验过程中需要大量的存储空间，以至于将其全部保存在磁盘上过于昂贵而不实用。这一挑战正迅速变得更加严峻，因为磁盘价格的提高速度比过去慢得多。OURRstore是一种大型存储工具，由大量磁带库以及服务器、磁盘、网络组件和软件等关键支持子系统组成。OURRstore允许存储容量的大规模增长，以满足其所服务的庞大而广泛的研究社区的需求。通过创新的低成本商业模式，OURRstore提供了一个经济高效的数据存储和访问解决方案。该仪器支持的研究课题包括：天气预报、天气雷达和天气数据挖掘，包括龙卷风和飓风等强风暴;地震触发因素和地震学;农业、林业、水和地球生态系统的数据和建模;沿海模拟;控制植被生长和发育的分子系统;基础设施的微生物污染;反复压力对生物体的影响;线粒体中的RNA处理;纳米技术;视觉神经科学;对极端环境的生理适应;天体物理对象和恒星大气;恶意软件网络安全;微博对社交媒体的影响。OURRstore通过以下方式最大化其在美国的影响力。首先，它是负担得起的，大规模的，长期的，多机构存储的国家模式。其次，OURRstore社区包括大量来自代表性不足的人群：超过200名研究团队成员是以下一种或多种：女性，非洲裔美国人，西班牙裔美国人，美洲原住民，残疾人和美国退伍军人。第三，该仪器将增强NSF目前的“超级计算简明英语”网络研讨会系列，这是一个受欢迎的推广计划。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。