We are currently evaluating the effectiveness of OpenKnowledge in two testbeds. For more details of these testbeds, as well as details of other projects which are making use of the OK system, see Case Studies.

• Emergency Response

During an emergency situation - for example, a large flood, an earthquake or a terrorist attack - the ability of different agencies and individuals to interact quickly and effectively, understanding one another and building and executing mutual plans, is key and can potentially save many lives. There will generally be some kind of central command that has been put in place in anticipation of disaster and which directs other players along pre-arranged lines. However, the nature of an emergency means that nothing can be taken for granted: communication lines can fail; key players (and even the command centre) can become unavailable for any number of reasons; outside players who were not anticipated in pre-disaster planning can have valuable help to offer. OpenKnowledge provides the infrastructure for executing pre-arranged plans between expected players who know how to communicate with one another, but also, crucially, can allow productive communication and action to continue even when one of these major stumbling blocks are encountered.

We have developed a simulation based on real data from large-scale flooding in the Trentino region to demonstrate the ability of OpenKnowledge in this domain. Tests are currently being made to evaluate the effectiveness of our approach.

For more information, see Deliverables 6.5 - 6.7.

• Bioinformatics

Modern biological experimentation requires computational techniques of different kinds to enable large-scale and high-throughput studies. For example, structural genomics efforts aim to understand the function of proteins from their 3-D structures, which are either determined by experimental methods (e.g., X-ray crystallography and NMR spectroscopy) or predicted by computational methods. Proteomics efforts as another example, aim to understand the functional consequences of the collection of proteins that is present in a cell, or tissue, at a given time - particularly where differences are observed between healthy and disease states. In both examples the data, and the analytical methodology applied to them, are obviously central to accomplishing the aims of these scientific domains. In addition, however, a framework is required that allows researchers to access the data, interpret the data, and exchange knowledge with one another. In the OpenKnowledge peer-to-peer framework, any experimental protocol that is followed when one, or several, researchers are undertaking a bioinformatics experiment can be viewed as a series of interactions between the researcher(s), the databases from which the data are obtained, and the tools that are applied to derive secondary information from this data. Many bioinformatics protocols can be represented as consecutive interactions, or steps in a workflow. These two simple (though non-trivial) first bioinformatics analyses involving consistency checking amongst comparable data from different databases and different bioinformatics programs, respectively, were enacted (i), as a means of introducing the system to the bioinformatics community, and (ii), to illustrate the commonalities of the underlying interactions, and accordingly the transferability of the underlying protocols, in OpenKnowledge.

For more information, see Deliverables 6.1 - 6.3.