S7: Community modeling, data and model interoperability
Organisers
Topics
- Data and models availability, metadata and catalogs
- Environmental observatories in the community modeling and data-model interoperability as a driver
- Differences in information models for observations data, and data access protocols. Harmonizing information models and data access protocols
- Differences in model needs with respect to the available data
- Differences in semantics between datasets available for different times and assembled in different research domains
- Difficulties in consensus building
Description
Community modeling is a promising paradigm to develop complex evolving and adaptable modeling systems that can share methods, data and models more easily within specialized communities and with outsiders. Why then are cooperative modeling communities still quite rare and do not propagate easily? Why has open source been so successful for software development, yet open models are still quite exotic? One big difference between software and models is that software shares some common language. Models often use very different principles and semantics. It becomes hard for one modeler to communicate these principles to another; it becomes difficult for one model to talk to another one. Similar problems prevail in data operations, when data sets (which are also models of sort) are hard to integrate with other data. Environmental observatories are becoming an important driver in the research community and also call for new interoperability standards and functionality.
There are two facets of the problem:
- Lack of common modeling and software tools to enable modularity and connectivity;
- Lack of social motivation and communication skills to enable communal work and sharing environments.
The goals of this session and linked workshop are to explore both of these areas.
- Understand the interoperability needs of the community in a participatory and collaborative effort;
- Develop research scenarios that would benefit from interoperability. Build consensus about interoperability architecture and standards supporting these scenarios;
- Expand on environmental system observatory ontologies, in particular for mapping variables to concepts;
- Discuss common access protocols, enabling models to automatically search for data needed and link to data servers. Design data interoperability for model input/output to help link models.