Guiding principles of NIF. Builds heavily on existing technologies. Information resources come in all sorts of size and shape.

Highest level NIF registry. Web index of resources which are relevant to neurosciences.

NIF resource diversity — three different levels of data, with increasing amount of structure.

Is GRM1 in cerebral cortex? NIF system allows searching over multiple different resources. But problems; inconsistent and sparse annotation of scientific data. Many different names of the same thing and so on. Added to this there are over 2000 databases in the registry.

Uses mixed searching so that both ontological information and string based systems important for where there is no annotation. Can also do query expansion with ontology to get better querying.

Building ontologies is difficult even for limited domains, never mind all of neurosciences. Trying to do this with multiple levels. NeuroLex — single inheritance, lexicon. NIFSTD, standardize modules under same upper ontology. NIFPlus — create intra-domain and more useful hierarchies using properties and restrictions. .

Using logical classification as a result of properties of the entities.

Question — how to get the community involved. Need to provide an easy to use platform for community collaboration. They have a semantic wiki for contributing to neurolex. Really lowers the barries for entry for domain experts who wish to use (and extend!) these terms.

Lots of people are starting to use the resources (they find this out because people complain when the systems are broken!).

Contributing to Neurolex. Don’t need an account, but better if you have one, everything online. Many thing that they are looking at is content, content, content. More stuff the better. Finally, getting people to value ontologists is really important.

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