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This poster presents our first steps to define a roadmap to robust science for high-throughput applications used in scientific discovery. These applications combine multiple components into increasingly complex multi-modal workflows that are often executed in concert on heterogeneous systems. The increasing complexity hinders the ability of scientists to generate robust science (i.e., ensuring performance scalability in space and time; trust in technology, people, and infrastructures; and reproducible or confirmable research). Scientists must withstand and overcome adverse conditions such as heterogeneous and unreliable architectures at all scales (including extreme scale), rigorous testing under uncertainties, unexplainable algorithms in machine learning, and black-box methods. This poster presents findings and recommendations to build a roadmap to overcome these challenges and enable robust science. The data was collected from an international community of scientists during a virtual world cafe in February 2021 

Scientists using the high-throughput computing (HTC) paradigm for scientific discovery rely on complex software systems and heterogeneous architectures that must deliver robust science (i.e., ensuring performance scalability in space and time; trust in technology, people, and infrastructures; and reproducible or confirmable research). Developers must overcome a variety of obstacles to pursue workflow interoperability, identify tools and libraries for robust science, port codes across different architectures, and establish trust in non-deterministic results. This poster presents recommendations to build a roadmap to overcome these challenges and enable robust science for HTC applications and workflows. The findings were collected from an international community of software developers during a Virtual World Cafe in May 2021. 

Software is increasingly important to the scientific enterprise, and science-funding agencies are increasingly funding software work. Accordingly, many different participants need insight into how to understand the relationship between software, its development, its use, and its scientific impact. In this article, we draw on interviews and participant observation to describe the information needs of domain scientists, software component producers, infrastructure providers, and ecosystem stewards, including science funders. We provide a framework by which to categorize different types of measures and their relationships as they reach around from funding, development, scientific use, and through to scientific impact. We use this framework to organize a presentation of existing measures and techniques, and to identify areas in which techniques are either not widespread, or are entirely missing. We conclude with policy recommendations designed to improve insight into the scientific software ecosystem, make it more understandable, and thereby contribute to the progress of science.