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

地质数据的数字化收集和存储使人们能够找到和访问这些数据。然而，为了使数据可重用和可再现，需要对数据的质量进行一些评估和/或对收集数据的人的信任。该项目的重点是开发一个自动化系统，用于评估收集数据的质量和观测的完整性。这包括开发两种方法——一种基于机器学习，另一种基于专家推导的逻辑——来评估数据质量。此外，该项目涉及在两个领域——实地数据和实验室数据——测试方法，以确保这些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的数据库，并在数据收集过程中改进数据收集。这些评估对于专家用户重用和复制观察结果以及公众和非学科专家认识到高质量和完整的数据收集至关重要。该项目解决了为基于观测的地质数据提供自动化的QAQC（质量评估/质量控制）系统的转换任务。该方法集成了计算机科学、认知科学和地质学专业知识，以开发实现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互补的算法——一种基于机器学习，另一种基于专家推导的逻辑——以评估观测数据。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），以便地质学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。这种方法可以实现新的科学类型，包括：1)在区域范围内授权建模，超过该范围，单个地质学家或团队可以通过使用可信的共享数据实现；2)允许某一领域的专家将其专业知识之外的数据纳入模型或解释，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在学科内部和学科之间使用共享数据集。设计QAQC系统的迭代、协作过程将作为社区建设的努力。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由研究、创新、协同和教育部门（RISE）、地球科学部（EAR）和美国国家科学基金会地球科学理事会的构造学项目共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.