DOI: 10.12688/f1000research.183312.1 ISSN: 2046-1402

The Call for Open Science in Modeling

Rebecca Ringuette, Gavin A. Schmidt, Meng Jin, Adam Kubaryk, Chris Erdmann, Geerten Hengeveld, Ronald M. Caplan, Maxine Hartnett, Jeffrey C. Carver
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A model is a living portfolio of conceptual, formal and implemented versions of a physical description of ‘something’ - including possibly different mathematical and computational implementations in modeling software - in relation to a collection of documentations of analyses and applications of these different versions. As the culture in modeling software shifts towards Open Science, many in our science community imagine a future infrastructure designed so that anyone in science can fully participate in research and development in an open way, where significant progress in our understanding of physical phenomena and accuracy of forecasting can be realized on faster timescales, and next generation of experts can receive quality training regardless of their backgrounds. However, we must acknowledge that funders cannot fund every software execution, develop every model, or archive every output desired by the research community, and that Open Science is not a magical solution to every problem or a gold standard to be required of every effort. However, we can purposefully design a prioritization system and the necessary supporting infrastructure to better enable research, make access to relevant education more equitable, accelerate, support collaboration, and make modeling software and their outputs more FAIR 1 (Wilkinson et al. 2016). Accomplishing these goals promises to increase the return on investment funders, scientists, and research software engineers put into these computational models by increasing community trust in results through increased transparency, discoverability through increased interlinking, and interoperability and reusability through more standardized approaches to documentation and file formats. This paper focuses on improvements to the infrastructure in United States for computational models in natural sciences, with the understanding that several aspects of the envisioned structure will also benefit other disciplines that deal with computationally intensive modeling software and simulations, both in US and other regions of the world.

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