TY - GEN
T1 - A Survey on Sustainable Software Ecosystems to Support Experimental and Observational Science at Oak Ridge National Laboratory
AU - Bernholdt, David E.
AU - Doucet, Mathieu
AU - Godoy, William F.
AU - Malviya-Thakur, Addi
AU - Watson, Gregory R.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Experimental and observational science EOS
KW - Scientific software ecosystem
KW - Survey
KW - Sustainability
UR - http://www.scopus.com/inward/record.url?scp=85134292930&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08760-8_46
DO - 10.1007/978-3-031-08760-8_46
M3 - Conference contribution
AN - SCOPUS:85134292930
SN - 9783031087592
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 560
EP - 574
BT - Computational Science - ICCS 2022, 22nd International Conference, Proceedings
A2 - Groen, Derek
A2 - de Mulatier, Clélia
A2 - Krzhizhanovskaya, Valeria V.
A2 - Sloot, Peter M.A.
A2 - Paszynski, Maciej
A2 - Dongarra, Jack J.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd Annual International Conference on Computational Science, ICCS 2022
Y2 - 21 June 2022 through 23 June 2022
ER -