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）项目提供了一个高性能的计算和数据分析系统，供以圣路易斯地区为中心的区域学术机构联盟使用，并扩展到整个中西部的各州。该联盟的统一重点是泰勒地理空间研究所（TGI），这是一个全面和整体的地理空间研究社区，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、研究和教育实体建立了合作伙伴关系，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，其目标是在食品系统、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。共享计算基础设施，否则缺乏的研究和教育工作的联盟，大大减少时间洞察具有挑战性的研究调查，并提供员工，研究人员和学生的经验与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的分配，如ACCESS系统。TGI RAILS解决了资源不足的关键机构的重大计算资源缺口，特别是那些为少数民族或非传统学生和研究人员服务的机构。该奖项旨在培养学生具备数据科学、计算机科学、统计学、研究和科学沟通技能，以设计实验并交流他们的发现。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。

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.