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: 2311819
• 负责人: Andrew Davidson
• 金额: $ 188.63万
• 依托单位: University of Kansas Center for Research Inc
• 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=2311819&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.

然而，地质数据的数字收集和存储使其易于查找和访问，但为了使数据可重复使用和可再现，需要对数据的质量和/或对收集者的信任进行一些评估。该项目的重点是开发一种自动化系统，用于评估收集的数据的质量和观察的完整性，这包括开发两种方法（一种基于机器学习，一种基于专家的逻辑）来评估数据质量。项目涉及两个领域的测试方法 –基于现场和实验室的数据——以确保方法适用于不同的数据类型，还将提供一种机制，使科学家能够评估现有数据库并改进数据收集，这些评估对于专家用户的重用至关重要。并重现观测结果，并让公众和非学科专家认识到高质量和完整的数据收集。该项目解决了为基于观测的地质数据方法提供自动化 QAQC（质量评估/质量控制）系统的转型任务。整合计算机科学，开发实施 QAQC 系统的认知科学和地质学专业知识是 StraboSpot 地质信息系统的基础，该项目活动包括开发两种互补算法，一种基于机器学习，另一种基于专家逻辑。还将开发 GUI（图形用户界面）来评估观测数据，以允许地质从业者在数据收集过程中评估他人的数据集并改进自己的数据集。 ,包括：1）在区域范围内进行建模，单个地质学家或团队可以通过使用可信的共享数据来实现这一目标；2）允许一个领域的专家将其专业知识之外的数据纳入模型或解释中，而无需学习如何收集数据和/或评估其他人的数据质量，因此，拟议的工作将促进和促进学科内部和学科之间共享数据集的使用，QAQC 系统的设计将作为一个社区。 -建设努力。这个奖项由 NSF 高级网络基础设施办公室颁发，并得到 NSF 地球科学理事会研究、创新、协同和教育部 (RISE)、地球科学部 (EAR) 和构造学项目的共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。