Geospatial Research Working Group
This working group was formed as the result of a September 2019 SCINet-funded workshop hosted by Deb Peters, the acting ARS Chief Science Information Officer. The focus of the group is to provide continued input on the development of SCINet, to determine the computational needs of ARS geospatial researchers, and to develop collaborative research projects. The group will also learn from each other on topics related to high-performance computing. For example, how to use Ceres (the ARS high-performance computer), optimizing code for parallel processing, getting software onto Ceres, etc. Monthly working group teleconference calls and working sessions are beginning soon.
If you would like to join the group, please contact Heather Savoy to be added to the group’s Teams.
|Working Group Leadership Team||Title|
|Heather Savoy||SCINet Computational Biologist (Data Scientist)|
|John Humphreys||Research Ecologist|
This working group contributes content to the Geospatial Workbook, a site dedicated to providing practical geospatial tutorials for SCINet users. Tutorials from our previous workshops and other meetings are available there for general use.
Provides hands-on tutorials on workflows to access the SCINet HPC systems and conduct geospatial research at scale and fosters geospatial research efforts.
The working group held their annual workshop over 6 separate Zoom sessions which were attended by over 60 scientists, post-docs, research leaders, and data managers. The sessions included an annual meeting of the working group, four tutorials on high-performance computing including 1 tutorial on the use of machine learning, and a symposium on the use of AI techniques in agricultural research. Detailed information on all the sessions, including recordings (to be posted soon), can be found on the session pages of the workshop website
A group of over 36 geospatial scientists, post-docs, research leaders, and data managers gathered at the ARS Jornada Rangeland Research Unit in Las Cruces, NM on September 10-11, 2019 to discuss high-performance computing (HPC) issues and artificial intelligence research methods applied to geospatial problems. The group identified computational issues with accessing and using SCINet and the ARS HPC system for geospatial research and also gained exposure to relevant machine learning and deep learning research methods. The workshop resulted in the creation of a SCINet Geospatial Research Working Group to continue identifying and addressing complex computational problems as well as to collaborate on new geospatial research projects.