Collaborative modeling: an introduction
This page contains a brief outline of its final contents, which will be completed in 2019.
Collaborative modeling, which allows scientists to contribute their own knowledge and reuse that contributed by others, is critical to successful interdisciplinary science that can be relevant for addressing complex societal challenges.
Proper reuse of scientific data and models, following an open-source, open-data paradigm, can enhance the credibility and transparency of scientific modeling, and improve the replicability of complex scientific workflows.
Collaborative modeling – if imbued with proper semantics – is poised to thrive as the open-data culture continues to grow (supported by open-data initiatives in academia, government, and scientific publishing). Open data considers scientific data as first-class research objects, each identified by a persistent uniform resource identifier (URI) and stored on data repositories. Collaborative modeling on the semantic web can enable these repositories to better live up to their full potential, as data become more fully Findable, Accessible, Interoperable, and Reusable (FAIR).