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.

该项目建立了路易斯维尔大学的红衣主教学术研究数据存储（卡）基础设施。卡可以管理和共享PETABYTE规模的机构数据，消除现有的存储性能瓶颈，并支持各个优先领域的研究，包括人工智能和机器学习，数字病理学，空间观察，癌症增长模拟和材料建模。此外，卡片通过将大型数据集以及统计学，工程和计算机科学教师与域科学家联系起来，以增强数据分析和知识发现，从而促进教师之间的校内合作。卡片中积累的信息提供了丰富的资源，可以为研究生创建跨学科项目，并将卡片集成到本地REU网站程序中，缩放全国本科生可用的产品。卡对于通过允许用于分布式数据存储平台的新动态缓存和层级技术的测试和开发来实现最先进的科学和工程进步至关重要。卡中的数据有助于创建独特的人工智能模型，以增强导航，避免障碍物和自主系统中的路径计划，并改进了数字病理学的图像分析。卡促进了用于冠状质量射血事件的新机器学习检测机制的生产，以及使用计算建模来评估阻塞的冠状动脉的非侵入性方法。卡可以生成用于癌症化学疗法和肿瘤生长的计算模型，以及用于下一代应用的新型高性能材料，用于纳米电子学，神经形态计算和储能。这是由高级网络基础设施办公室授予的，该奖项是由既定的decor anst decor anst de de de the de the epsfors the de the epscor的既定计划的支持。通过使用基金会的智力优点和更广泛影响的评论标准进行评估。