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Experiential Findings for Sustainable Software Ecosystems to Support Experimental and Observational Science

by David E Bernholdt, Mathieu Doucet, William F Godoy, Aditi A Malviya Thakur, Gregory R Watson
Publication Type
Journal
Journal Name
Journal of Computational Science
Publication Date
Page Numbers
1 to 10
Volume
71
Issue
102033

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. The study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: (i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and (ii) data management requirements for data-driven science using artificial intelligence.