Digital ecosystem for FAIR time series data management in environmental system 
science

Bumberger, J.; Abbrent, M.; Brinckmann, Nils; Hemmen, J.; Kunkel, R.; Lorenz, C.; Lünenschloss, P.; Palm, B.; Schnicke, T.; Schulz, C.; van der Schaaf, H.; Schäfer, D.

doi:10.1016/j.softx.2025.102038

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Digital ecosystem for FAIR time series data management in environmental system science

Authors

Bumberger, J.
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Abbrent, M.
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/persons/resource/nils

Brinckmann, Nils
5.2 eScience Centre, 5.0 Geoinformation, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Hemmen, J.
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Kunkel, R.
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Lorenz, C.
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Lünenschloss, P.
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Palm, B.
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Schnicke, T.
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Schulz, C.
External Organizations;

van der Schaaf, H.
External Organizations;

Schäfer, D.
External Organizations;

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5035085.pdf
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Citation

Bumberger, J., Abbrent, M., Brinckmann, N., Hemmen, J., Kunkel, R., Lorenz, C., Lünenschloss, P., Palm, B., Schnicke, T., Schulz, C., van der Schaaf, H., Schäfer, D. (2025): Digital ecosystem for FAIR time series data management in environmental system science. - SoftwareX, 29, 102038.
https://doi.org/10.1016/j.softx.2025.102038

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5035085

Abstract

Addressing the challenges posed by climate change, biodiversity loss, and environmental pollution requires comprehensive monitoring and effective data management strategies that support real-time analysis and 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. With its modular and scalable architecture, the ecosystem enables automated workflows, enhances data accessibility, and supports seamless integration into larger research infrastructures, including digital twins and advanced environmental models. The use of 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.