I too would argue that what we seek is a system that distinguishes between "observations", measured in some sense (either directly using some device), or perhaps "mapped" using known or widely used conventions and higher level abstractions that often pass as qualitative "data", (ie leaf "ovate" or state "relatively derived" or "1").
Would it be too simplistic to simply allow the format to express the source (qualitative/quantitative, or a more detailed description) or a particular data set?
I believe that original measurement data should be part of the data set, together with rules how to map them unto categorical data (DELTA: "Keystate directive"). Knowing that categorical states for, e.g. length-to-width-ratio of a leaf have been measured rather than just guessed, solves only a small part of the problem.
Deciding which sources you trust, value, or otherwise wish to manipulate is an option you then have at the application level.
i.e. "I wish only direct measures of leaf shape, and not a description such as 'ovate'.
but ovate is rather difficult to parameterize. It may be possible to get ellipsoidal parameters, but width and length are clearly insufficient.
Does anybody know about good parameterization of shapes, so they can be stored in more objective forms?
Gregor ---------------------------------------------------------- Inst. for Plant Virology, Microbiology, and Biosafety Federal Research Center for Agriculture and Forestry (BBA) Gregor Hagedorn Net: G.Hagedorn@bba.de Koenigin-Luise-Str. 19 Tel: +49-30-8304-2220 14195 Berlin, Germany Fax: +49-30-8304-2203
Often wrong but never in doubt!