CC* Regional Computing: Taylor Geospatial Institute Regional AI Learning System

泰勒地理空间研究所区域人工智能学习系统 (TGI RAILS) 项目提供高性能计算和数据分析系统，供以圣路易斯地区为中心并延伸到整个中西部各州的区域学术机构联盟使用。该联盟的统一焦点是泰勒地理空间研究所 (TGI)，这是一个综合性、整体性的地理空间研究团体，最初由中西部中部地区的八个杰出研究机构组成。 TGI 与行业、政府机构以及研究和教育实体建立合作伙伴关系，以推进包括人工智能、数据分析和地理空间科学在内的多个学科，其目标是在粮食系统、健康和社会平等、智能和弹性社区、劳动力发展、国家安全和经济发展等领域产生重大社会效益。TGI RAILS 提供了该联盟的研究和教育工作所缺乏的关键、共享计算基础设施，大大缩短了富有挑战性的研究调查的洞察时间，并为工作人员、研究人员和学生提供了体验拥有最新的人工智能和数据分析技术。 TGI RAILS 的经验支持研究人员获得国家计算资源（例如 ACCESS 系统）的分配。TGI RAILS 解决了关键资源不足机构的巨大计算资源缺口，特别是那些为少数族裔或非传统学生和研究人员服务的机构。它为学生提供数据科学、计算机科学、统计学、研究和科学沟通技能，以设计实验和交流他们的发现。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Taylor Geospatial Institute Regional AI Learning System (TGI RAILS) project provides a high-performance computing and data analysis system for use by the regional consortium of academic institutions that is centered around the St. Louis area and extends to states throughout the Midwest. The unifying focus of this consortium is the Taylor Geospatial Institute (TGI), a comprehensive and holistic geospatial research community of initially eight outstanding research institutions in the Midwest central region. TGI comprises partnerships with industry, government agencies, and research and educational entities to advance multiple disciplines including AI, data analytics and geospatial science with the targeted goals of producing significant societal benefits in the areas of food systems, health and social equality, smart and resilient communities, workforce development, national security, and economic development.TGI RAILS provides critical, shared computing infrastructure that is otherwise lacking for the research and educational work of the consortium, dramatically reducing time to insight for challenging research investigations and providing staff, researchers, and students experience with the latest AI and data analytics technologies. TGI RAILS experience supports researchers in obtaining allocations on national computing resources such as ACCESS systems.TGI RAILS addresses significant computational resource gaps at key under-resourced institutions, particularly those serving minoritized or non-traditional students and investigators. It prepares students with data science, computer science, statistics, research, and scientific communication skills to design experiments and communicate their findings.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.