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Many fields of science rely on software systems to answer different research questions. For valid results researchers need to trust the results scientific software produces, and consequently quality assurance is of utmost importance. In this paper we are investigating the impact of quality assurance in the domain of computational materials science (CMS). Based on our experience in this domain we formulate challenges for validation and verification of scientific software and their results. Furthermore, we describe directions for future research that can potentially help dealing with these challenges.
Although computer science papers are often accompanied by software artifacts, connecting research papers to their software artifacts and vice versa is not always trivial. First of all, there is a lack of well-accepted standards for how such links sho
Managing and growing a successful cyberinfrastructure such as nanoHUB.org presents a variety of opportunities and challenges, particularly in regard to software. This position paper details a number of those issues and how we have approached them.
Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in
Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data u