[ad_1]
group leader
Computational Geoscience Group
computing and computational science
What is your biggest takeaway from AGU23?
It was great to see so many of ORNL’s early career researchers enthusiastically presenting their science to such a large and diverse audience.
It is also clear that research groups around the world are adopting machine learning for data science and modeling in the earth and environmental sciences. Many teams are developing and applying fundamental models for various scaling and forecasting applications. All of these applications are powered by high performance computing similar to ORNL.
How does “wide open science” fit into your thinking about your research goals for the coming year?
Our projects include free open source software we develop, such as the Department of Energy’s Energy Exascale Earth System Model, tools such as the International Land Model Benchmarking Package, and tools we curate and distribute globally. Now we have more opportunities to share our excitement about open data. Research community, including simulation output available on the Earth System Grid Federation. It is easy to argue that if code and data are not open, they do not embody the principles of science.
It’s clear that today’s research community is hungry for more ways to access and analyze data. They expect tools and libraries that are scalable, reliable, and easy to use. These needs are a priority for the new development of Earth System Grid Federation technology that we are developing at his ORNL and partner laboratories.
[ad_2]
Source link
