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

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、生物医学科学和其他校园研究活动的多种科学应用存储需求。该平台还为开发智能图像分析的创新提供了一个实验测试平台。项目活动是为大学校园里的科学实验服务的，这些实验经常涉及到对扫描电子显微镜等专门仪器的控制，以及从这些仪器中收集图像数据。项目活动的意义在于远程仪器和图像分析的自动化处理，以克服当前实践中需要大量精力和时间的手动过程，并且可能导致不一致或错误。作为下一代存储环境，该项目开发的远程仪器科学环境（RISE）是位于校园数据中心的校内级共享资源，并通过联邦数据共享结构在校际级共享资源。RISE确保遵守公平（可查找、可访问、可互操作和可重用）原则和公平访问。RISE数据存储系统配置是由诸如材料生物医学科学、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的。RISE存储系统的开发特点是灵活的网络服务，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。这个项目中的可用性研究在理解各种图像分析应用程序的大型内部部署存储系统的编排和维护方面的挑战方面提高了知识。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.