"...My arguement is that we need to be able to use all of these vocabs, or none, to describe experiments to a practical level of detail...."

I agree, FuGE will allow you to do this.

With all these things, especially modelling, there are lots of ways to reach the end goal. With respect to vocabularies, often these are just recommend strings (which are valuable in themselves). However ontologies, specifically OBI are an effort to assign meaning and machine interpretability to terms as a community wide consensus, to avoid the issues you mention.

Tools: tools are a big issue and often a stumbling block, ideally as you suggest the information or datafiles should be automatically exported from instrumentation. This is currently not the case and therefore there is a high cost involved, for some this is an unacceptable cast, to the "humble experimenter", often the benefits of annotation and sharing are not obvious to the person that generated the data, but I am preaching to the converted :)

In regards to the data model. FuGE is the data model [PMID: 17921998]. It is a heavy weight solution, although it is designed to represent the common aspects of a scientific experiment. The GelML case is an extension of the FuGE data model to be more specific in the representation of a gel electrophoresis experiment.

However, if you are looking for a data model for the representation of scientific experiments, FuGE should be your first port of call. You do not have to use-it out of the box as one entity but re-use components. It allows the representation of protocols - the standard instructions/templates and the application of those protocols - capturing the day to day changeable information such as dates and lot numbers without having to fill in the full protocol again. Ontologies or controlled vocabularies are recommended although not restricted, you can use free text.

Its feasible that text from wikis or blogs could be extracted, by text mining or rdf queries and then placed in a model like this, However this is close to the data integration paradigm that currently exists in bioinformatics. Its alot of wasted effort. I would argue it is better to structure an annotate the data at the source on day one, than trying to extract information later. I have this problem with my current project, in that semantic extraction involves picking up the phone and asking the experimenter what you did and what does it all mean. I can see similar parallels here, the only difference is that the lab book is electronic and not paper, its still unstructured information.

Like I say, there are many ways to achieve the same end. A common data model for scientific experiments exists, which has undergone a long, cross community and public consultation process. It does need tool support. However, just hypothesising, even an xslt representation maybe able to be "plugged" into a blog or a wiki to provide the structure.

I am afraid I am in the data model, structured and semantic data camp. Electronic notebooks and open data are a step in the right direction - a massive step. However I still believe that unless the data comes structured with semantics, either data models RDF or whatever the Zeitgeist technology, we are still not maximising the potential form this information.