Collaborative Research: Frameworks: Automated Quality Assurance and Quality Control for the StraboSpot Geologic Information System and Observational Data

合作研究：框架：StraboSpot 地质信息系统和观测数据的自动化质量保证和质量控制

• 批准号: 2311822
• 负责人: Basil Tikoff
• 金额: $ 58.15万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311822&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Automated; Quality

Digital collection and storage of geological data allow for it to be findable and accessible. For the data to be reusable and reproducible, however, there needs to be some evaluation of the quality of the data and/or trust in the person who collected it. This project focuses on developing an automated system for evaluating the quality of collected data and the completeness of observations. This includes the development of two methods - one based on machine learning and one on logic derived from experts - to evaluate data quality. Furthermore, the project involves testing methods in two areas – field- and lab-based data – to ensure that the methodologies apply to different data types. A mechanism will also be provided that allows scientists to evaluate existing databases and improve data collection while it is occurring. These assessments are essential for expert users to reuse and reproduce observations and for the general public and non-disciplinary experts to recognize high-quality and complete data collections.This project addresses the transformational task of providing an automated QAQC (Quality Assessment/Quality Control) system for observationally-based geological data. The approach integrates Computer Science, Cognitive Science, and Geology expertise to develop algorithms to implement a QAQC system. The basis of these CI resources is the StraboSpot geologic information system. Project activities include the development of two complementary algorithms – one based on machine learning and one on logic derived from experts – to evaluate observational data. Expert testing will be done to improve algorithmic performance. A GUI (Graphical User Interface) will also be developed to allow geology practitioners to evaluate others’ datasets and improve their own during data collection. This approach enables new kinds of science, including: 1) empowering modeling at a regional scale beyond which a single geologist or team could achieve through their use of trusted shared data; and 2) allowing experts in one area to incorporate data outside of their expertise into a model or interpretation, without having to learn how to collect the data and or assess someone else’s data quality. Thus, the proposed work will promote and facilitate using shared data sets within and between disciplines. The iterative, collaborative process through which the QAQC system is designed will serve as a community-building endeavor.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Research, Innovation, Synergies, and Education (RISE), the Division of Earth Sciences (EAR), and the Tectonics Program within the NSF Directorate for Geosciences.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.

地质数据的数字收集和存储使其可以找到和访问。但是，要使数据可重复使用和可复制，需要对收集数据的人的数据质量和/或信任进行一些评估。该项目着重于开发自动化系统，以评估收集的数据的质量和观察结果的完整性。这包括开发两种方法 - 一种基于机器学习的方法，另一种是基于从专家衍生的逻辑来评估数据质量的方法。此外，该项目涉及在两个领域（基于实验室和实验室数据）的测试方法，以确保方法学应用适用于不同的数据类型。还将提供一种机制，该机制使科学家可以在发生数据库时评估现有数据库并改善数据的发生。这些评估对于专家用户重复使用和复制观察结果至关重要，以及对公众和非学科专家识别高质量和完整的数据收集。该项目解决了为基于观察的地理数据提供自动化QAQC（质量评估/质量控制）系统的转型任务。该方法集成了计算机科学，认知科学和地质专业知识，以开发实施QAQC系统的算法。这些CI资源的基础是Strabospot地质信息系统。项目活动包括开发两种完整的算法 - 一种基于机器学习，一种基于从专家衍生的逻辑来开发，以评估观察数据。将进行专家测试以提高算法性能。还将开发GUI（图形用户界面），以允许地质实践者评估他人的数据集并在数据收集过程中改进自己的数据集。这种方法可以使新的科学类型，包括：1）在区域规模上授权建模，超越单个地质学家或团队可以通过使用可信赖的共享数据来实现； 2）允许一个领域的专家将其专业知识以外的数据纳入模型或解释，而无需学习如何收集数据和评估他人的数据质量。这是拟议的工作将促进和促进学科之间和学科之间的共享数据集。 QAQC系统设计的迭代，协作过程将成为社区建设的努力。 NSF高级网络基础设施办公室的这一奖项由研究，创新，协同和教育部共同支持，地球科学部（EAR）和NSF地球科学局内的构造计划颁发了这一奖项，这反映了NSF的法定任务和审查的范围，该奖项通过评估范围来进行评估。