Norms of transparency and knowledge sharing in science encourage new research to build on prior discoveries, leading to rapid innovation and significant societal returns. This project establishes a new activity to help make scientific computation and data science more transparent and accessible. Computation has evolved from tools for assisting scientific research to digital laboratories where fundamental scientific discoveries take place. This is increasingly so for social and ecological sciences, which are combining big data with computational models to better understand the social and earth systems whose complex dynamics underlie many of the grand challenges faced by humanity today. Organized as a Spoke in the National Science Foundation's Big Data Innovation Hub and Spoke network, this project establishes a next generation, online Computational Model Library (CMLX).
In the CMLX, software code for computational models used in social and ecological sciences is published and freely accessible to the scientific community and general public, and linked with an online database of scientific papers reporting on associated model-based science. In addition to the code library, the CMLX is developing an online repository to archive end-to-end workflows of model-based science, where the entire research process can be followed and reproduced, from data synthesis, to modeling, analysis, and visualization. A regional Working Group - including software developers, data providers, and user communities - will provide advice and expertise, as well as help establish community-wide standards for transparency and accessibility in scientific computation. The CMLX is enabling transparent access to scientific procedures, models, and data that can be used to assess the consequences of alternative scenarios, policies, and assumptions for more sustainable management of complexly coupled human and earth systems.
