Experiential findings for sustainable software ecosystems to support experimental and observational science

• Published in: Journal of computational science Vol. 71; p. 102033
• Main Authors: Bernholdt, David E., Doucet, Mathieu, Godoy, William F., Malviya-Thakur, Addi, Watson, Gregory R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.07.2023; Elsevier
• Subjects: Computational science and engineering CSE; Experimental and observational science EOS; MATHEMATICS AND COMPUTING; Scientific software ecosystem; Survey; Sustainability; Survey; Sustainability; Scientific software ecosystem; Experimental and observational science EOS; Computational science and engineering CSE
• ISSN: 1877-7503; 1877-7511
• DOI: 10.1016/j.jocs.2023.102033

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. •First study to frame critical components of Experimental and Observational Science.•Understanding of challenges and opportunities for evolving EOS software landscape.•Guidelines on adopting AI/ML, cloud, training, and automation in EOS software.