  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.
