In the age of "big science" computational modelling underpins much of scientific research and decision-making processes. Concerns surrounding the reproducibility and accessibility of models and data have increased concurrent with the adoption of computational workflows. Furthermore, such models and their workflows may be resource-intensive to reproduce. This session explores standards, tools and frameworks, and the methods and approaches that support modelling workflows which increase transparency, reproducibility, and accessibility of data and code. We invite papers which aid to advance a culture and community of practice amongst modellers which values reproducibility, provenance, and accessibility of data and code which are the cornerstones of modern scientific progress.
Key topics: Model reproducibility, Transparent workflows, Standards, Technical uncertainty