BD Spokes: SPOKE: SOUTH: Collaborative: Using Big Data for Environmental Sustainability: Big Data + AI Technology = Accessible, Usable, Useful Knowledge!

BD 发言：发言：南方：协作：利用大数据促进环境可持续发展：大数据人工智能技术 = 可获取、可用、有用的知识！

• 批准号: 1636848
• 负责人: Ashok Goel
• 金额: $ 75.46万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636848&HistoricalAwards=false
• 关键词: BD; Spokes; SPOKE; SOUTH; Collaborative

Protecting the environment is among the biggest challenges facing our society. As the effects of environmental degradation, global warming and climate change continue to grow, there is an increasingly urgent and critical need for research and education in biological diversity, ecological modeling and environmental sustainability. On one hand, professional and citizen scientists need ready access to large-scale biological, ecological and environmental data for modeling, simulation and analysis. On the other, college teachers and students in biology and ecology need to access large-scale data in a form meaningful to them. The various audiences will engage with big data in different ways and so a variety of knowledge-building tools are needed. This project brings together two dozen scientists from a dozen institutions in academia, government and industry to address the problem of translating big data into meaningful knowledge in support of research and education in environmental sustainability. Encyclopedia of Life (EOL) is the world's largest database of biological species and other biodiversity information. EOL also works closely with scores of other biodiversity datasets such as BISON, GBIF, and OBIS. This project seeks to make EOL and related biodiversity data sources accessible, usable, and useful, by integrating extant artificial intelligence tools for information extraction, modeling and simulation, and question answering. The focus of this project will be on the data engineering required for this integration and construction of a resulting EOL+ system. The project team will provide access to EOL+ such that users can build their own tools and services on top of EOL+. The team will work with the NSF South Big Data Hub to organize yearly workshops for building and supporting a community of users of EOL+. Professional and citizen scientists, and teachers and students alike, will be able to access EOL+ through NSF's South Big Data Hub webportal, and use it for modeling and analysis, explanation and prediction, as well as education and workforce development in biological diversity, ecological modeling and environmental sustainability.

保护环境是我们社会面临的最大挑战之一。随着环境降解，全球变暖和气候变化的影响，对生物多样性，生态建模和环境可持续性的研究和教育的越来越紧迫和迫切需要。一方面，专业和公民科学家需要准备好访问大型生物学，生态和环境数据，以进行建模，模拟和分析。另一方面，生物学和生态学领域的大学教师和学生需要以对他们有意义的形式访问大规模数据。各种观众将以不同的方式参与大数据，因此需要各种知识建设工具。该项目汇集了来自学术界，政府和行业的十二个机构的十二个科学家，以解决将大数据转化为有意义的知识，以支持环境可持续性的研究和教育。 生命百科全书（EOL）是世界上最大的生物物种和其他生物多样性信息的数据库。 EOL还与许多其他生物多样性数据集（例如野牛，GBIF和OBIS）紧密合作。该项目旨在通过整合现有的人工智能工具来访问，可用和有用的相关生物多样性数据来源，以进行信息提取，建模和仿真以及问题答案。该项目的重点将放在此集成和构建所得EOL+系统所需的数据工程上。项目团队将提供对EOL+的访问权限，以便用户可以在EOL+之上构建自己的工具和服务。该团队将与NSF South Big Data Hub合作，组织年度研讨会，以建立和支持EOL+用户社区。专业和公民科学家以及教师和学生都将能够通过NSF的South Big Data Hub Webportal访问EOL+，并将其用于建模和分析，解释和预测以及生物多样性，生态建模和环境可持续性的教育和劳动力发展。

项目成果

Guiding Parameter Estimation of Agent-Based Modeling Through Knowledge-based Function Approximation

通过基于知识的函数逼近指导基于代理的建模参数估计

• 发表时间: 2021
• 期刊: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021
• 作者: Broniec, W.

Scientific Modeling Using Large Scale Knowledge

• DOI: 10.1007/978-3-030-52240-7_4
• 发表时间: 2020-06-10
• 期刊: Artificial Intelligence in Education
• 作者: An S;Bates R;Hammock J;Rugaber S;Weigel E;Goel A
• 通讯作者: Goel A

VERA: Popularizing Science Through AI

• DOI: 10.1007/978-3-319-93846-2_6
• 发表时间: 2018-01-01
• 期刊: ARTIFICIAL INTELLIGENCE IN EDUCATION, PT II
• 作者: An, Sungeun;Bates, Robert;Goel, Ashok
• 通讯作者: Goel, Ashok

Effects of Guidance on Learning about Ill-defined Problems

指导对学习不明确问题的影响

• DOI: 10.1007/978-3-031-09680-8_28
• 发表时间: 2022
• 期刊: Proceedings of the 18th International Conference on Intelligent Tutoring Systems.
• 作者: An, S.

Cognitive Strategies for Parameter Estimation in Model Exploration

模型探索中参数估计的认知策略

• 发表时间: 2021
• 期刊: Proceedings of the 43rd Annual Conference of the Cognitive Science Society
• 作者: An, S.