环境系统科学中FAIR时间序列数据管理的数字生态系统
Digital Ecosystem for FAIR Time Series Data Management in Environmental System Science
AI总结：
本文提出一个遵循FAIR原则、由SMS、time.IO和SaQC三组件构成的数字生态系统，用于环境系统科学中时间序列数据的管理，提升数据可访问性并促进合作。
摘要：
应对气候变化、生物多样性丧失和环境污染所带来的挑战，需要全面的监测和有效的数据管理策略，这些策略需适用于环境系统科学中的各种尺度。本文介绍了一个通用且可转移的数字生态系统，用于管理时间序列数据，其设计遵循FAIR原则（可查找、可访问、可互操作和可重用）。该系统高度适应性强、支持云部署，并适用于从小型项目到大规模监测计划的各种环境。该生态系统包含三个核心组件：传感器管理系统（SMS），用于详细的元数据注册和管理；time.IO，一个用于高效时间序列数据存储、传输和实时可视化的平台；以及自动化质量控制（SaQC）系统，通过实时分析和质量保证确保数据完整性。模块化架构结合标准化协议和接口，确保生态系统能够轻松转移并部署到不同环境和机构中。这种方法提高了广泛利益相关者（包括研究人员、政策制定者和公众）的数据可访问性，同时促进合作并推动环境监测领域的科学研究。
英文摘要：
Addressing the challenges posed by climate change, biodiversity loss, and environmental pollution requires comprehensive monitoring and effective data management strategies that are applicable across various scales in environmental system science. This paper introduces a versatile and transferable digital ecosystem for managing time series data, designed to adhere to the FAIR principles (Findable, Accessible, Interoperable, and Reusable). The system is highly adaptable, cloud-ready, and suitable for deployment in a wide range of settings, from small-scale projects to large-scale monitoring initiatives. The ecosystem comprises three core components: the Sensor Management System (SMS) for detailed metadata registration and management; time$.$IO, a platform for efficient time series data storage, transfer, and real-time visualization; and the System for Automated Quality Control (SaQC), which ensures data integrity through real-time analysis and quality assurance. The modular architecture, combined with standardized protocols and interfaces, ensures that the ecosystem can be easily transferred and deployed across different environments and institutions. This approach enhances data accessibility for a broad spectrum of stakeholders, including researchers, policymakers, and the public, while fostering collaboration and advancing scientific research in environmental monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
