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

保护环境是我们社会面临的最大挑战之一。随着环境退化、全球变暖和气候变化的影响不断加剧，对生物多样性、生态建模和环境可持续性的研究和教育的需求日益迫切和迫切。一方面，专业和公民科学家需要随时获得大规模的生物、生态和环境数据，以便进行建模、模拟和分析。另一方面，生物学和生态学的大学师生需要以对他们有意义的形式访问大规模数据。不同的受众将以不同的方式接触大数据，因此需要各种知识构建工具。该项目汇集了来自学术界、政府和工业界十几个机构的二十几位科学家，共同解决将大数据转化为有意义的知识的问题，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数据库。EOL还与其他生物多样性数据集密切合作，如BISON、GBIF和OBIS。该项目旨在通过集成现有的人工智能工具进行信息提取、建模和仿真以及问题回答，使EOL和相关生物多样性数据源可访问、可用和有用。这个项目的重点将放在集成和构建EOL+系统所需的数据工程上。项目团队将提供对EOL+的访问，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南方大数据中心合作，组织年度研讨会，以建立和支持EOL+的用户社区。专业和公民科学家、教师和学生都可以通过NSF的南方大数据中心门户网站访问EOL+，并将其用于生物多样性、生态建模和环境可持续性方面的建模和分析、解释和预测，以及教育和劳动力发展。

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.