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Innovative methods for integrating and exploring heterogeneous environmental data
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Edited by Isabelle Braud, Jan Bumberger, Martin Abbrent
The implementation of the Whole System Approach and of critical zone science relies on the use of multidisciplinary data coming from heterogeneous sources, including sensor data but also sample-based data, biodiversity data, remote sensing data, and model data, that are now provided at unprecedented high resolution and frequency. This diverse array of data is crucial for advancing our understanding and modelling of the Earth System, enabling the development of predictive models for the Earth's evolution. The data collected from various observatories and their management systems are often highly heterogeneous. Effective utilisation of these data requires significant efforts in employing data science methods, adapting new algorithms, developing data pipelines tailored to specific needs, and creating interoperability between heterogeneous systems to support multidisciplinary research. The development of methods for the automatic real-time processing and integration of observation data into models is essential for many applications. There is a need for automated quality assessment and control pipelines, tools for data discovery and exploration, standardised interfaces and vocabularies, data exchange strategies and security concepts. These elements are critical for interconnecting distributed data infrastructures following FAIR and open science principles. This session welcomes submissions dealing with novel techniques for data management, processing, analysis and sharing.
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