MRI: Track 2 Development of a Platform for Accessible Data-Intensive Science and Engineering

低成本、高性能的传感器正在各个领域产生爆炸性的数据，从宇宙的奥秘到最微小的粒子。尽管有如此丰富的数据，但缺乏强大的基础设施和工具阻碍了有效分析和利用这些数据的能力。开发无障碍数据密集型科学与工程平台(DISE)将是一个数据管理、共享和分析系统，允许自动收集和整理数据，即时访问以进行计算分析，并共享研究结果以实现可重复性。该系统将为数据驱动的科学和科学成果方面的新研究铺平道路。Dise解决的一个关键问题是数据从存储移动到分析站点的复杂性和成本，尤其是在基于云的场景中。DISE的目标是建立一个更有凝聚力的系统，集成数据存储和分析。通过让公众更容易获得研究成果，研究所将为研究界和整个社会提供更高的研究投资回报。至关重要的是，DISE将在数据密集型研究方面培训下一代科学家，确保不同学习者的可获得性，并促进公平的科学界。DISE的目标是促进数据密集型科学和工程，使研究更容易获得和可重复，促进所有社会部门的公平结果。DISE将推出一个创新平台，促进来自科学仪器的自动数据管理、详细的元数据查询、智能分层以实现计算工作负载的即时数据访问、交互式访问图形处理器(GPU)加速的资源、托管和共享集装箱化的研究产品以及部署用于实时数据分析的自我修复微服务。该平台将配备2PB的科学数据管理系统、500TB的全闪存高性能并行文件系统和GPU加速计算。这些功能不仅承诺简化数据管理，还将促进每秒输入/输出操作(IOPS)密集型工作流，包括数据挖掘、数据驱动的研究、人工智能基础、数据密集型计算以及可靠和值得信赖的人工智能。DISE致力于通过利用社区数据库来帮助探索新的研究领域，同时促进下一代熟练从事数据密集型研究的科学家的成长。DISE还将改善对研究数据和计算的获取，从而增加先前研究投资的回报。DISE的自动管理功能将彻底改变研究数据的组织和共享方式，使其能够在原始研究人员之外访问和互操作。此外，Dise致力于提供高性能的GPU资源，以促进数据密集型科学的发展。该平台将与不同学科的研究人员联系，并打算通过从共享用户设施自动进行数据管理来进一步扩大其覆盖范围。此外，通过易于访问的JupyterHub使用GPU加速的计算资源将有助于教育和培训下一代研究人员。最后，DISE致力于促进该领域的多样性，提供公平的资源获取机会，并与摩根州立大学等机构合作，培养新一代多样化的数据密集型科学家和工程师。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Low-cost, high-performance sensors are producing an explosion of data in every field, from the mysteries of the cosmos to the tiniest particles. Despite this abundance of data, the absence of robust infrastructure and tools impedes the ability to effectively analyze and utilize it. The Development of a Platform for Accessible Data-Intensive Science and Engineering (DISE) will be a system for data management, sharing, and analysis that allows automatic data collection and curation, instant access for computational analysis, and research result sharing for reproducibility. The system will pave the way for new research in data-driven science – and scientific outcomes. A key issue that DISE addresses is the complexity and cost of data movement from storage to analysis sites, especially in cloud-based scenarios. DISE aims for a more cohesive system integrating data storage and analysis. By making research findings more accessible to the public, DISE will provide increased return on research investments to the research community and society at large. Critically, DISE will serve to train the next generation of scientists in data-intensive research, ensuring accessibility for diverse learners, and contributing to an equitable scientific community.DISE aims to advance data-intensive science and engineering and make research more accessible and reproducible, fostering equitable outcomes across all societal sectors. DISE will introduce an innovative platform that facilitates automatic data curation from scientific instruments, detailed metadata querying, intelligent tiering for instant data accessibility for computational workloads, interactive access to Graphics Processing Unit (GPU)-accelerated resources, hosting and sharing of containerized research products and deployment of self-healing micro-services for real-time data analysis. The platform will be equipped with a 2-petabyte scientific data management system, a 500-terabyte all-flash high-performance parallel file system, and GPU-accelerated computation. These features promise not only to streamline data management but also to facilitate input/output operations per second (IOPS) intensive workflows, including data mining, data-driven research, foundations in AI, data-intensive computation for good, and reliable and trustworthy AI. DISE is dedicated to aiding the exploration of new areas of study by utilizing community data repositories while simultaneously fostering the growth of the upcoming generation of scientists skilled in data-intensive research. DISE will also improve access to research data and computation, thus increasing the return on prior research investments. The automated curation feature of DISE is set to revolutionize how research data is organized and shared, making it accessible and interoperable beyond the originating researchers. Additionally, DISE is committed to providing high-performance GPU resources to foster data-intensive science. The platform will connect with researchers across various disciplines and intends to further expand its reach by automating data curation from shared user facilities. In addition, the use of GPU-accelerated computing resources via an easily accessible JupyterHub will aid in educating and training the next generation of researchers. Finally, DISE is committed to promoting diversity in the field by providing equitable access to resources and partnering with institutions such as Morgan State University to train a new generation of diverse data-intensive scientists and engineers.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.