Collaborative Research: Geoinformatics: Facility: Paleobiology Database: Preserving and Presenting Ancient Data for Future Research

合作研究：地理信息学：设施：古生物学数据库：保存和呈现古代数据以供未来研究

• 批准号: 1948831
• 负责人: Mark Uhen
• 金额: $ 66.65万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948831&HistoricalAwards=false
• 关键词: Collaborative; Research; Geoinformatics; Facility; Paleobiology

The Paleobiology Database (PBDB) is an open access, community-curated resource containing information on the temporal and geographic distribution of almost 1.5 million fossil occurrences, their taxonomic classification, and associated bibliographic information. The PBDB has become the go-to data resource for answering questions such as, “what fossils are found in my backyard”, “how has biodiversity changed over time,” and “what are the drivers and consequences of mass extinctions?” The PBDB has cyberinfrastructure that allows data to be accessed and used within educational, commercial, and research web, mobile, and analytical applications. This project will develop and deploy new state-of-the-art machine reading and learning tools to automate key steps in the process of finding and extracting PBDB-relevant data from published scientific papers and reports. The primary objective is to decrease the high cost of expert time and effort that is currently required in order to find and enter new data into the system. The automated reading and learning tools will primarily focus on paleontological and taxonomic literature. However, this new machine reading and artificial intelligence system, which is capable of locating and extracting specialized information and automating key steps in database construction, will address a general cyberinfrastructure challenge that has far-reaching applications across science and industry.This project will facilitate research that characterizes natural geological resources, improves stratigraphic correlation and paleogeographic reconstructions, calibrates molecular clocks, and facilitates paleoecological assessments of the effects of modern and ancient environmental change. The project will establish a first-of-its-kind, AI-driven pipeline that would connect a rapidly growing digital library and computing infrastructure, called GeoDeepDive, to the large expert community of PBDB users. This will serve as a template for deployment of other such pipelines for other user communities. As part of this process, the PIs will complete development and deployment of the PBDB data acquisition application program interface (API), which will modernize the PBDB data entry workflow and enable the development of third-party apps designed for entering and editing specific PBDB data types, such as taxonomic opinion and classification data. This will likely lower the barrier for participation in the process of PBDB data entry and editing, and thus enhance a crowd-source model for future PBDB growth and improvement.This project is jointly funded by the Geoinformatics and the Sedimentary Geology and Paleobiology programs in the Division of Earth Sciences.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.

古生物学数据库（PBDB）是一种开放访问，由社区策划的资源，其中包含有关临时和地理分布近150万个化石事件的信息，它们的分类学分类以及相关的书目信息。 PBDB已成为回答问题的首选数据资源，例如“我的后院中发现了哪些化石”，“生物多样性如何随着时间变化”和“大规模扩展的驱动力和后果是什么？” PBDB具有网络基础架构，允许在教育，商业和研究网络，移动和分析应用程序中访问和使用数据。该项目将开发和部署新的最先进的机器阅读和学习工具，以在从发表的科学论文和报告中查找和提取PBDB相关数据的过程中自动化关键步骤。主要目的是减少目前需要的高度专家时间和精力来查找并将新数据输入到系统中所需的高成本。自动阅读和学习工具将主要关注古生物学和分类学文献。但是，这种新的机器阅读和人工智能系统能够找到和提取专业信息并在数据库构建中自动化关键步骤，将解决一般的网络网络基础构造挑战，该挑战在科学和行业之间具有深远的应用应用。该项目将促进研究的研究，以促进自然地理资源，改善地层矫正和层次构造，钙质量，分子分析，分子分析，分子分类，分类分子构建，分子分子分析，分子分析，分子分子构造，分子分子构造且分子构造。现代和古老的环境变化的影响。该项目将建立首个AI驱动的管道，该管道将将快速增长的数字图书馆和计算基础架构（称为GeoDeepdive）与PBDB用户的大型专家社区联系起来。这将作为用于其他用户社区其他管道部署的模板。作为此过程的一部分，PI将完成PBDB数据采集应用程序界面（API）的开发和部署，该界面（API）将现代化PBDB数据输入工作流程，并能够开发用于输入和编辑特定PBDB数据类型的第三方应用程序，例如分类观点和分类数据。这可能会降低参与PBDB数据输入和编辑过程的障碍，从而增强人群源模型，以实现未来的PBDB增长和改善。该项目由地球形式学和沉积地质学和沉积地质学和古生物学计划共同资助。更广泛的影响审查标准。

项目成果

Leveraging a Large Database to Increase Access to Undergraduate Research Experiences

利用大型数据库增加本科生研究经验的获取

• DOI: 10.18833/spur/2/4/6
• 发表时间: 2019
• 期刊: Council on Undergraduate Research Quarterly
• 作者: Lukes, Laura A.;Ryker, Kathrine;Millsaps, Camerian;Lockwood, Rowan;Uhen, Mark D.;George, Christian;Bentley, Callan;Berquist, Peter
• 通讯作者: Berquist, Peter