Distributed knowledge, data, models and software
k.LAB software underlies both the integrated development environment and integrated modeling web interface. The k.IM modeling language provides approximates, as best as possible, plain-English descriptions of scientific data and models. This yields a shared, but extensible, formalization of a scientific language for integrated modeling. Advanced users can code additional model functionality in k.IM using the Groovy and Java programming languages.
Both k.LAB and k.IM are designed to be both simple to use yet powerful and flexible, supporting both novice and advanced users, and to adhere to open data and open-source software principles. They thus support a spectrum of user types, as described below.
General, non-technical users run model workflows in an online interface by simply specifying their observable and the spatiotemporal context of interest. Nontechnical users thus primarily reuse existing data and models. The provenance information supplied with their results maintains transparency, while built-in decision rules for data and model selection reduce the likelihood that inappropriate data and models are accidentally selected.
Intermediate-skill modelers can both reuse others’ data and models and contribute their own as they test new ways to link disciplinary data and models. k.LAB and k.IM support construction of small, independent models that can be reassembled to simulate more complex, linked phenomena. This allows modelers of various skill levels and interests to contribute to a growing base of data, models, and knowledge.
Developers and other highly technical users can contribute more advanced scientific knowledge, extending and improving the knowledge base of both semantics and models on the integrated modeling cloud.
Finally, data providers can host data in a way that maximizes the utility and reusability of existing scientific data. By hosting data in a way that can interface with the k.LAB/k.IM integrated modeling platform, scientific data repositories can enable data to be more quickly ingested and reused in complex scientific workflows.
In sum, our approach provides a collaborative, Wikipedia-like environment for scientific simulation powered by key components of artificial intelligence – semantics, machine reasoning, and machine learning. It also offers a path forward in modeling complex systems – one where disciplinary experts have the capacity to model key phenomena in their area of expertise, while a machine connects the building blocks of more complex models. The burden of complexity in integrated modeling is thus transferred from humans to AI.
The Integrated Modelling Partnership provides a vehicle for partner organizations to steer the direction of new growth and expanded functionality of this integrated modeling system – speeding the development of advanced integrated modeling features and capacity for users at all levels.