Proto-OKN Theme 1 CollabNext: A Person-Focused Metafabric for Open Knowledge Networks

该项目旨在创建一个原型知识图，集中在科学领域中经常被低估的贡献者，例如个人，研究领域，以及隶属于历史上的黑人学院和大学（HBCUs）和非卡内基R1机构的组织。该项目的主要目标是阐明美国广阔的研究前景，促进各种合作，并为萌芽的研究伙伴关系提供一个开放的平台。这个名为CollabNext的知识图谱利用开放的数据源和基础设施，集成了人工智能和机器学习进行数据处理，同时强调人在循环输入，以确保数据的准确性和可持续性。CollabNext是佐治亚理工学院、莫尔豪斯学院和克拉克亚特兰大大学的合作项目，旨在通过有效的数据利用来促进研究合作。该项目涉及多所大学的合作，并利用了来自各个领域的计算思维技能、数据和机器学习。它的重点是使数据民主化，以实现更广泛的社会效益和知识图谱的实际应用。合作者包括菲斯克大学和莫尔豪斯学院等学术机构，以及文艺复兴计算研究所和TDP数据系统等行业和研究合作伙伴。CollabNext的有效性在于它整合了不同的知识图谱实体，并致力于建立一个包容性和标准化的数据基础设施。通过优先考虑来自代表性不足的研究人员的反馈，该工具将提高新兴研究机构的知名度，促进研究合作，并培养更多样化的研究队伍。其综合数据库将完全集成到开放知识网络中，从而为其他用例建立强大的数据基础设施。该奖项反映了美国国家科学基金会的法定使命，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。

This project aims to create a prototype knowledge graph concentrating on often-undervalued contributors within the science realm, such as individuals, research areas, and organizations affiliated with Historically Black Colleges and Universities (HBCUs) and non-Carnegie R1 institutions. The primary objective of this project is to illuminate the expansive research landscape of the US, promote varied collaborations, and provide an open platform for budding research partnerships. The knowledge graph, named CollabNext, utilizes open data sources and infrastructure and integrates AI and machine learning for data processing, while emphasizing human-in-the-loop input to ensure data accuracy and sustainability. A collaborative effort between Georgia Tech, Morehouse, and Clark Atlanta University, CollabNext aims to boost research collaborations through effective data utilization.The project involves a multi-university collaboration and draws upon computational thinking skills, data, and machine learning from various fields. Its focus is on democratizing data for broader societal benefit and the practical application of knowledge graphs. Collaborators range from academic institutions such as Fisk University and Morehouse College to industry and research partners like the Renaissance Computing Institute and TDP Data Systems. CollabNext's effectiveness lies in its integration of diverse knowledge graph entities and its commitment to an inclusive and standardized data infrastructure. By prioritizing feedback from underrepresented researchers, the tool is poised to increase visibility for emerging research institutions, boost research collaborations, and foster a more diverse research workforce. Its comprehensive database will be fully integrated into the Open Knowledge Network, thus establishing a robust data infrastructure for other use cases.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.