Open Earth System Science Data and Artificial Intelligence (AI) / Machine Learning (ML) to Advance Scientific Discovery
Open Earth System Science Data and Artificial Intelligence (AI) / Machine Learning (ML) to Advance Scientific Discovery
Session ID#: 282130
Session Description:
The volume, velocity, variety, veracity, and value of Earth, biology, and environmental data are increasing rapidly. Data from observational networks, remote sensing and modeling platforms are expanding with ever increasing spatial resolution and temporal sampling. To realize the promise of new scientific discoveries in Earth system science, new analysis paradigms, novel hardware and software infrastructure, efficient and scalable data discovery, access, and management tools are required. This session solicits abstracts that describe high performance storage and computing infrastructure; data access and analytics platforms; strategies for fostering or ensuring FAIR (findability, accessibility, interoperability, and reusability) data principles; unique methods for data fusion, synthesis, and analysis of open data; standards for AI-ready training and benchmarking data; methods for building and using ML foundational models from Big Data archives; advances in research data platforms and technologies; and reports on scientific discoveries or research results employing large and/or complex Earth system science data.
Co-Sponsor(s):
- A - Atmospheric Sciences
- B - Biogeosciences
- H - Hydrology
- OS - Ocean Sciences
Index Terms:
1912 Data management, preservation, rescue [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1932 High-performance computing [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Forrest M. Hoffman, Oak Ridge National Laboratory, Computational Sciences & Engineering Division, Oak Ridge, United States
Conveners: Charuleka Varadharajan, Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States, Jitendra Kumar, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States and Justin Jay Hnilo, U.S. Department of Energy, Biological and Environmental Research, Germantown, United States
See more of: Informatics