Convener: Jens Greinert | Co-conveners: Peter Dietrich, Andreas Petzold, Roland Ruhnke, Viktoria WichertECSECS
| Attendance Wed, 06 May, 10:45–12:30 (CEST)

Earth Sciences depend on detailed multi-variate measurements and investigations to understand the physical, geological, chemical, biogeochemical and biological processes of the Earth. Making accurate prognoses and providing solutions for current questions related to climate change, water, energy and food security are important requests towards the Earth Science community worldwide. In addition to these society-driven questions, Earth Sciences are still strongly driven by the eagerness of individuals to understand processes, interrelations and tele-connections within and between small sub-systems and the Earth System as a whole. Understand and predict temporal and spatial changes in the above mentioned Micro- to Earth spanning scales is the key to understand Earth ecosystems; we need to utilize high resolution data across all scales in an integrative/holistic approach. Using Big Data, which are often distributed and particularly very in-homogenous, has become standard practice in Earth Sciences and digitalization in conjunction with Data Science promises new discoveries.
The understanding of the Earth System as a whole and its sub-systems depends on our ability to integrate data from different disciplines, between earth compartments, and across interfaces. The need to advance Data Science capabilities and to enable earth scientists to follow best possible workflows, apply methods, and use computerized tools properly and in an accessible way has been identified worldwide as an important next step for advancing scientific understanding. This is particularly necessary to access knowledge contained in already acquired data, but which due to the limitations of data integration and joint exploration possibilities currently remains invisible. This session aims to bring together researchers from Data and Earth Sciences working on, but not limited to,
• SMART monitoring designs by dealing with advancing monitoring strategies to e.g. detect observational gaps and refine sensor layouts to allow better and statistically robust extrapolation
• Data management and stewardship solutions compliant with FAIR principles, including the development and application of real-time capable data management and processing chains
• Data exploration frameworks providing qualified data from different sources and tailoring available computational and visual methods to explore and analyse multi-parameter data generated through monitoring efforts/ model simulations