ESSI2.19

Together with the rapid development of sensor technologies and the implementation of environmental observation networks (e.g. MOSES, TERENO, Digital Earth, eLTER, CUAHSI, ICOS, ENOHA,…) a large number of data infrastructures are being created to manage and provide access to observation data. However, significant advances in earth system understanding can only be achieved through better and easier integration of data from distributed infrastructures. In particular, the development of methods for the automatic real-time processing and integration of observation data in models is required in many applications. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor metadata quality. Improvement in this field strongly depends on the capabilities of dealing with fast growing multi-parameter data and on effort employing data science methods, adapting new algorithms and developing digital workflows tailored to specific scientific needs. Automated quality assessment/control algorithms, data discovery and exploration tools, standardized interfaces and vocabularies as well as data and processing exchange strategies and security concepts are required to interconnecting distributed data infrastructures. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important aspect. This session focuses on the specific requirements, techniques and solutions to process, provide and couple observation data from (distributed) infrastructures and to make observation data available for modelling and other scientific needs.

Public information:
16:15–16:25: Introduction
16:25–16:29: MOSAiC goes O2A - Arctic Expedition Data Flow from Observations to Archives
16:29–16:33: Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube
16:33–16:37: Implementing FAIR principles for dissemination of data from the French OZCAR Critical Observatory network: the Theia/OZCAR information system
16:37–16:47: Discussion
16:47–16:51: Solutions for providing web-accessible, semi-standardised ecosystem research site information
16:51–16:55: Put your models in the web - less painful
16:55–16:59: Improving future optical Earth Observation products using transfer learning
16:59–17:03: Design and Development of Interoperable Cloud Sensor Services to Support Citizen Science Projects
17:03–17:13: Discussion
17:13–17:17: Providing a user-friendly outlier analysis service implemented as open REST API
17:17–17:21: Graph-based river network analysis for rapid discovery and analysis of linked hydrological data
17:21–17:25: SIMILE: An integrated monitoring system to understand, protect and manage sub-alpine lakes and their ecosystem
17:25–17:35: Discussion

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Convener: Dorit KerschkeECSECS | Co-conveners: Benedikt GrälerECSECS, Ralf Kunkel, Anusuriya DevarajuECSECS, Johannes Peterseil
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| Tue, 05 May, 16:15–18:00 (CEST)

Together with the rapid development of sensor technologies and the implementation of environmental observation networks (e.g. MOSES, TERENO, Digital Earth, eLTER, CUAHSI, ICOS, ENOHA,…) a large number of data infrastructures are being created to manage and provide access to observation data. However, significant advances in earth system understanding can only be achieved through better and easier integration of data from distributed infrastructures. In particular, the development of methods for the automatic real-time processing and integration of observation data in models is required in many applications. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor metadata quality. Improvement in this field strongly depends on the capabilities of dealing with fast growing multi-parameter data and on effort employing data science methods, adapting new algorithms and developing digital workflows tailored to specific scientific needs. Automated quality assessment/control algorithms, data discovery and exploration tools, standardized interfaces and vocabularies as well as data and processing exchange strategies and security concepts are required to interconnecting distributed data infrastructures. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important aspect. This session focuses on the specific requirements, techniques and solutions to process, provide and couple observation data from (distributed) infrastructures and to make observation data available for modelling and other scientific needs.

Public information: 16:15–16:25: Introduction
16:25–16:29: MOSAiC goes O2A - Arctic Expedition Data Flow from Observations to Archives
16:29–16:33: Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube
16:33–16:37: Implementing FAIR principles for dissemination of data from the French OZCAR Critical Observatory network: the Theia/OZCAR information system
16:37–16:47: Discussion
16:47–16:51: Solutions for providing web-accessible, semi-standardised ecosystem research site information
16:51–16:55: Put your models in the web - less painful
16:55–16:59: Improving future optical Earth Observation products using transfer learning
16:59–17:03: Design and Development of Interoperable Cloud Sensor Services to Support Citizen Science Projects
17:03–17:13: Discussion
17:13–17:17: Providing a user-friendly outlier analysis service implemented as open REST API
17:17–17:21: Graph-based river network analysis for rapid discovery and analysis of linked hydrological data
17:21–17:25: SIMILE: An integrated monitoring system to understand, protect and manage sub-alpine lakes and their ecosystem
17:25–17:35: Discussion

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