CC* Data Storage: Remote Instrumentation Science Environment for Intelligent Image Analytics

CC* 数据存储：用于智能图像分析的远程仪器科学环境

• 批准号: 2322063
• 负责人: Prasad Calyam
• 金额: $ 50万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322063&HistoricalAwards=false
• 关键词: CC; Data; Storage; Remote; Instrumentation

This NSF CC* project acquires and manages an on-premises data storage system supporting multiple scientific application storage needs for material science, biomedical science and other research activities on campus. The platform also provides an experimental testbed for developing innovations in intelligent image analytics. Project activities serve scientific experiments on university campuses that frequently involve control of specialized instruments such as scanning electron microscopes, and image data collection from those instruments. The significance of the project activities is in the automated handling of remote instrumentation and image analytics to overcome the manual processes in current practice that requires notable effort and time, and could lead to inconsistencies or errors. As a next-generation storage environment, the Remote Instrumentation Science Environment (RISE) developed in this project is a shared resource at the intra-campus level located at the campus datacenter and at the inter-campus level via a federated data sharing fabric. RISE ensures adherence to FAIR (findable, accessible, interoperable, and reusable) principles and equitable access. The RISE data storage system configurations are driven by scientific use cases of image analytics pipelines in areas such as material biomedical science, plant science, and biochemistry. The RISE storage system development features flexible web services to perform automated image data collection/analysis guided by an intelligent agent and involves collaborations with campus information technology group and the Open Science Grid. Usability studies in this project advance knowledge in terms of understanding challenges in orchestration and maintenance of large on-premise storage systems for diverse image analytics applications.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NSF CC*项目将获取并管理一个本地数据存储系统，该数据存储系统支持材料科学，生物医学科学和其他研究活动的多个科学应用存储需求。该平台还为开发智能图像分析的创新提供了实验测试床。项目活动在大学校园内提供科学实验，这些实验经常涉及控制诸如扫描电子显微镜之类的专业仪器以及这些乐器的图像数据收集。项目活动的重要性在于对远程仪器和图像分析的自动处理来克服当前实践中需要明显努力和时间的手动过程，并可能导致不一致或错误。作为下一代存储环境，该项目中开发的远程仪器科学环境（RISE）是校园内部数据中心校内和校园内通过联合数据共享结构的共享资源。上升确保遵守公平（可发现，可访问，可互操作和可重复使用的）原则和公平访问权限。 RISE数据存储系统配置是由材料生物医学，植物科学和生物化学等领域的图像分析管道的科学用例驱动的。 RISE存储系统开发具有灵活的Web服务，以执行以智能代理为指导的自动图像数据收集/分析，并涉及与校园信息技术小组和开放科学网格的合作。该项目的可用性研究在理解大型本地存储系统方面的挑战方面提高了知识，以用于多种图像分析应用程序。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。