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* 项目获取并管理一个本地数据存储系统，支持材料科学、生物医学科学和校园内其他研究活动的多种科学应用存储需求。该平台还提供了一个用于开发智能图像分析创新的实验平台。项目活动服务于大学校园的科学实验，这些实验经常涉及扫描电子显微镜等专用仪器的控制以及从这些仪器收集图像数据。该项目活动的意义在于自动处理远程仪器和图像分析，以克服当前实践中需要大量精力和时间并可能导致不一致或错误的手动流程。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）是位于校园数据中心的校园内级别的共享资源，以及通过联合数据共享结构在校园间级别的共享资源。 RISE 确保遵守 FAIR（可查找、可访问、可互操作和可重用）原则和公平访问。 RISE 数据存储系统配置由材料生物医学科学、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。 RISE 存储系统开发具有灵活的 Web 服务，可在智能代理的指导下执行自动图像数据收集/分析，并涉及与校园信息技术小组和开放科学网格的合作。该项目中的可用性研究增进了人们对各种图像分析应用程序的大型本地存储系统的编排和维护挑战的了解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和技术进行评估，被认为值得支持。更广泛的影响审查标准。