CC* Data Storage: Cardinal Academic Research Data Storage (CARDS)

CC* 数据存储：Cardinal 学术研究数据存储 (CARDS)

• 批准号: 2322248
• 负责人: Nihat Altiparmak
• 金额: $ 50万
• 依托单位: University of Louisville Research Foundation Inc
• 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: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322248&HistoricalAwards=false
• 关键词: CC; Data; Storage; Cardinal; Academic

This project establishes the Cardinal Academic Research Data Storage (CARDS) infrastructure at the University of Louisville. CARDS allows the management and sharing of petabyte-scale institutional data, eliminates existing storage performance bottlenecks, and supports research across various priority areas including artificial intelligence and machine learning, digital pathology, space observation, cancer growth simulation, and materials modeling. In addition, CARDS promotes intra-campus collaborations among faculty by bringing domain scientists with large datasets together with statistics, engineering, and computer science faculty for enhanced data analysis and knowledge discovery. The information accumulated in CARDS provides a rich resource to create cross-disciplinary projects for graduate students, and integrating CARDS into local REU site programs scales the offerings available to undergraduate students nationwide. CARDS is critical to enable scientific and engineering advances in the state-of-the-art by allowing the testing and development of new dynamic caching and tiering techniques for distributed data storage platforms. The data in CARDS helps create unique artificial intelligence models for enhanced navigation, obstacle avoidance, and path planning in autonomous systems, as well as improved image analysis for digital pathology. CARDS facilitates the production of new machine learning based detection mechanisms for coronal mass ejection events, and non-invasive methods to assess blocked coronary arteries using computational modeling. CARDS enables the generation of computational models for cancer chemotherapy and tumor growth, and novel, high-performance materials for next-generation applications in nano-electronics, neuromorphic computing, and energy storage.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Established Program to Stimulate Competitive Research (EPSCoR).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.

该项目在路易斯维尔大学建立了红衣主教学术研究数据存储（Cardinal Academic Research Data Storage，简称RDR）基础设施。它允许管理和共享PB级机构数据，消除现有的存储性能瓶颈，并支持各种优先领域的研究，包括人工智能和机器学习，数字病理学，空间观察，癌症生长模拟和材料建模。此外，通过将拥有大型数据集的领域科学家与统计学，工程学和计算机科学教师一起促进教师之间的校园内合作，以增强数据分析和知识发现。积累的信息在EMBA提供了丰富的资源，为研究生创建跨学科的项目，并整合EMBA到当地REU网站程序规模提供给全国本科生。通过允许测试和开发用于分布式数据存储平台的新的动态缓存和分层技术，可扩展性对于实现最先进的科学和工程进步至关重要。这些数据有助于创建独特的人工智能模型，用于增强自主系统中的导航、避障和路径规划，以及改进数字病理学的图像分析。该研究促进了基于机器学习的新冠状物质喷射事件检测机制的产生，以及使用计算建模评估阻塞冠状动脉的非侵入性方法。可用于生成癌症化疗和肿瘤生长的计算模型，以及用于纳米电子学，神经形态计算，高级网络基础设施办公室的这一奖项得到了刺激竞争研究的既定计划（EPSCoR）的联合支持。该奖项反映了NSF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。