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

David E. Bernholdt; Mathieu Doucet; William F. Godoy; Addi Malviya-Thakur; Gregory R. Watson

doi:10.1016/j.jocs.2023.102033

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

David E. Bernholdt, Mathieu Doucet, William F. Godoy, Addi Malviya-Thakur, Gregory R. Watson

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	102033
Journal	Journal of Computational Science
Volume	71
DOIs	https://doi.org/10.1016/j.jocs.2023.102033
State	Published - Jul 2023

Funding

Notice: This manuscript has been authored by UT-Battelle, LLC, USA under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). Notice: This manuscript has been authored by UT-Battelle, LLC, USA under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

Computational science and engineering CSE
Experimental and observational science EOS
Scientific software ecosystem
Survey
Sustainability

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10.1016/j.jocs.2023.102033

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title = "Experiential findings for sustainable software ecosystems to support experimental and observational science",

abstract = "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.",

keywords = "Computational science and engineering CSE, Experimental and observational science EOS, Scientific software ecosystem, Survey, Sustainability",

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

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