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At least 19 records

The DIARieS Ecosystem – A Software Ecosystem to Simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing of Scientific Results and Environments in Heliophysics

The infrastructure of the Heliophysics discipline has promising components but with several missing gaps, drastically reducing research and development efficiency. Developing an online discovery and analysis software ecosystem will close several of these gaps. The five main focuses on this ecosystem should be Discovery, Implementation, Analysis, Reproducibility, and Sharing of results (DIARieS). In this paper, we give a detailed description of how the proposed software ecosystem should operate, and point out the large range of possible applications to benefit many disparate groups, such as researchers, operational staff, decision-makers, and educators. The infrastructure components and technological capabilities necessary for its completion are either currently available or in development, making such an ecosystem possible for the first time. One main focus of current infrastructure investments must be to adapt and connect these pieces together into a cohesive whole to increase our research and development efficiency.

infrastructure↗

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

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. Here, 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.

97 MATHEMATICS AND COMPUTING↗

A Survey on Sustainable Software Ecosystems to Support Experimental and Observational Science at Oak Ridge National Laboratory

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.

Bernholdt, David↗

Building an open source software ecosystem for cross-disciplinary plasma research and education

Software is crucial to all areas of modern plasma science research. Laboratory plasma physicists use software to interpret plasma diagnostics, analyze experimental results, and glean insights using advanced techniques from data science. Space scientists use software to reduce and understand in situ observations. Numericists use software to simulate the behavior of laboratory, heliospheric, and astrophysical plasmas, and then analyze or visualize the results. Theorists use symbolic manipulation software to perform or check derivations. Cross-disciplinary research and cross-comparisons between experiments, observations, simulations, and theories all require software. Despite our heavy reliance on software, funding agencies have traditionally had few avenues available to support development of general purpose research software infrastructure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗