Elements: Software: HDR: A knowledge base of deep time to facilitate automated workflows in studying the co-evolution of the geosphere and biosphere

要素：软件：HDR：促进研究地圈和生物圈共同进化的自动化工作流程的深度时间知识库

• 批准号: 1835717
• 负责人: Xiaogang Ma
• 金额: $ 59.7万
• 依托单位: Regents of the University of Idaho
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835717&HistoricalAwards=false
• 关键词: Elements; Software; HDR; knowledge; base

This project will result in the creation of a software that will support research in the Earth's deep time history. The co-evolution of the geosphere and biosphere is one of the fundamental questions for the 21st century Earth science. The multi-disciplinary characteristics of the research questions on co-evolution are reflected in the various subjects of datasets that need to be integrated. In the past decades, many open data facilities have been built through the support from NSF and other sources. However, the shortage of efficient methods for accessing and synthesizing multi-source datasets hamper the data-intensive co-evolution research. Geologic time is an essential topic in the co-evolving geosphere and biosphere, and can be used as a common reference to connect various parameters among the data silos. This project will improve the machine readability and alignment of various global, local and regional geologic time standards and build a knowledge base of deep time and its service on the Web. All the deliverables will be well-documented and offered under open-access to promote a national cyberinfrastructure ecosystem. The planned tasks and activities will leverage the usage of existing data facilities, facilitate executable and reproducible workflows, generate best practices of cross-disciplinary data science, generate state-of-the-art materials to education programs, and engage the participation of female and underrepresented groups. Shared in the national cyberinfrastructure, the knowledge base built in the project will be able to support a broad range of research, education and outreach programs, which will benefit not only science and engineering but also the society at large.The research question to be addressed is the heterogeneity of geologic time concepts that hamper the data synthesis among multiple data facilities. Accordingly, the objective of this project is to build a knowledge base of deep time to automate geoscience data access and integration in the open data environment, and to support data synthesis in executable workflows for data-intensive scientific discovery. The development approach will include both top-down and bottom-up tracks to leverage previous works on geologic time ontologies and address end user needs through use case analyses. With carefully designed activities and work plan, deliverables from this project will include a machine-readable knowledge base of aligned geologic time standards, services and packages for accessing and querying the knowledge base, and best practices of data synthesis in workflow platforms for studying the co-evolution. The developed knowledge base of deep time will provide powerful support to co-evolution researchers to tackle data heterogeneity issues. Robust services the knowledge base will be built to support automated data synthesis in workflow platforms to advance the co-evolution research. The source code and metadata of the knowledge base will be released on GitHub and registered on community repositories to enable reuse and adaptation. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Activities Program of the Division of Earth Sciences within the NSF Directorate for Geosciences, and the OAC Cyberinfrastructure for Emerging Science and Engineering Research (CESER) program.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.

该项目将导致创建一个软件，将支持在地球的深层时间历史的研究。地圈与生物圈的协同演化是21世纪地球科学的基本问题之一。协同进化研究问题的多学科特征体现在需要整合的数据集的学科众多。在过去的几十年里，许多开放数据设施都是在NSF和其他来源的支持下建立起来的。然而，缺乏有效的方法来访问和综合多源数据集阻碍了数据密集型的协同进化研究。地质时间是地圈和生物圈共同演化过程中的一个重要课题，可以作为数据孤岛之间连接各种参数的共同参考。该项目将提高各种全球、地方和区域地质年代标准的机器可读性和一致性，并建立一个深时知识库及其网上服务。所有可交付成果都将有详细记录，并在开放获取的情况下提供，以促进国家网络基础设施生态系统。计划中的任务和活动将利用现有数据设施，促进可执行和可复制的工作流程，生成跨学科数据科学的最佳实践，为教育计划生成最先进的材料，并吸引女性和代表性不足的群体参与。在国家网络基础设施中共享的知识库将能够支持广泛的研究、教育和推广计划，这不仅有利于科学和工程，而且有利于整个社会。要解决的研究问题是地质年代概念的异质性，这阻碍了多个数据设施之间的数据合成。因此，该项目的目标是建立一个深时知识库，以自动化开放数据环境中的地球科学数据访问和集成，并支持数据密集型科学发现的可执行工作流程中的数据合成。开发方法将包括自上而下和自下而上的轨道，以利用地质年代本体的以前的工作，并通过用例分析解决最终用户的需求。通过精心设计的活动和工作计划，该项目的交付成果将包括一个机器可读的地质年代标准知识库，用于访问和查询知识库的服务和软件包，以及用于研究共同演变的工作流程平台中数据综合的最佳做法。深度时间知识库的建立将为协同进化研究者解决数据异质性问题提供有力的支持。将建立强大的知识库服务，以支持工作流平台中的自动数据合成，从而推进协同进化研究。知识库的源代码和元数据将在GitHub上发布，并在社区存储库中注册，以实现重用和调整。该奖项由NSF高级网络基础设施办公室颁发，并得到NSF地球科学理事会地球科学部跨领域活动计划的联合支持，OAC Cyberinfrastructure for Emerging Science and Engineering Research（CESER）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进行评估，被认为值得支持的搜索.

项目成果

DATA VISUALIZATION IN MINERAL EVOLUTION STUDIES

矿物演化研究中的数据可视化

• DOI: 10.1130/abs/2019am-337787
• 发表时间: 2019
• 期刊: Geological Society of America Abstracts with Programs
• 作者: Ma, Xiaogang

A method to decipher the time distribution in astronomically forced sedimentary couplets

破译天文强迫沉积对中时间分布的方法

• DOI: 10.1016/j.marpetgeo.2020.104399
• 发表时间: 2020
• 期刊: Marine and Petroleum Geology
• 影响因子: 4.2
• 作者: Ma, Chao;Meyers, Stephen R.;Hinnov, Linda A.;Eldrett, James S.;Bergman, Steven C.;Minisini, Daniel
• 通讯作者: Minisini, Daniel

TEMPORAL TOPOLOGY FOR NOMINAL AND NUMERICAL ENTITIES THE DEEP-TIME KNOWLEDGE BASE

名义实体和数值实体的时态拓扑 深度时间知识库

• DOI: 10.1130/abs/2021am-370148
• 发表时间: 2021
• 期刊: Geological Society of America Abstracts with Programs
• 作者: Ma, Xiaogang

A knowledge graph and service for regional geologic time standards

区域地质时间标准知识图谱及服务

• DOI: 10.1016/j.gsf.2022.101453
• 发表时间: 2022
• 期刊: Geoscience Frontiers
• 影响因子: 8.9
• 作者: Ma, Chao;Kale, Amruta Suresh;Zhang, Jiyin;Ma, Xiaogang
• 通讯作者: Ma, Xiaogang

Appearance and disappearance rates of Phanerozoic marine animal paleocommunities

显生宙海洋动物古群落的出现和消失率

• DOI: 10.1130/g49371.1
• 发表时间: 2021
• 期刊: Geology
• 影响因子: 5.8
• 作者: Muscente, A.D.;Martindale, Rowan C.;Prabhu, Anirudh;Ma, Xiaogang;Fox, Peter;Hazen, Robert M.;Knoll, Andrew H.
• 通讯作者: Knoll, Andrew H.