Digital Ecosystem for FAIR Time Series Data Management in Environmental System Science
J. Bumberger, M. Abbrent, N. Brinckmann, J. Hemmen, R. Kunkel, C., Lorenz, P. L\"unenschlo{\ss}, B. Palm, T. Schnicke, C. Schulz, H. van der, Schaaf, and D. Sch\"afer

TL;DR
This paper presents a flexible, cloud-ready digital ecosystem for managing environmental time series data that adheres to FAIR principles, enhancing data sharing, quality control, and collaboration across diverse environmental research settings.
Contribution
It introduces a modular, transferable system with integrated metadata management, real-time visualization, and automated quality control tailored for environmental time series data.
Findings
The ecosystem supports scalable deployment across various environments.
It improves data accessibility and interoperability for stakeholders.
The system ensures data integrity through automated quality checks.
Abstract
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; timeIO, a platform for efficient time series data storage, transfer, and real-time visualization; and the System for Automated Quality Control (SaQC),…
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Taxonomy
TopicsResearch Data Management Practices · Scientific Computing and Data Management
