Response to "some generic issues to consider"

(a) can we really develop common science-language standards on a
continent-wide basis?

 Maybe, I'm not really sure.  I see tremendous variability in terminology
and especially in nuances.  Just in the example below, (b),
glaciers can have deltaic sediments associated with them and
a fault is a geologic contact. hence versus is the wrong term
from my point of view.  We recently tried to merge the new digital
Yukon geologic map with our digital data base of Alaska maps
and found some terminology was, at least at first cut, not able 
to be translated.  So we guessed.  In some cases they were good
guesses, in others, we don't know.

(b) can we really do this at a level deeper than "granite versus basalt" or
"glacial versus deltaic" or "geologic contact versus fault", etc?

Maybe, following on to my comments above, I believe that this will be
incredibly
difficult.  In the USGS, we seem to be abandoning our adherence
to the North American Stratigraphic Code with respect to naming
and defining geologic units.  We are proposing here not only
to turn back the clock, but add even more structure.

(c) what role do regional geologic differences and geologic-mapping
traditions play in the development of science-language standards?

Significant.  Having been educated in three regions of the US
(midwest, west, and east), the language and concepts were very 
different.  This is not to say that they can't come together,
but it does mean a level of externally imposed structuring.

(d) should there be one single terminology standard, or multiple standards
linked by translators and equivalency tables?

Not sure.  RGB, CMYK, it is all color but described for different 
applications.  Do we have a similar situation?

(e) what kinds of scientific queries should be supported by standard
terminologies at the National, Regional, and Local levels, and should a
single science-language structure support each and all levels?

Lets see the outcome of the 20 questions exercise before trying to
answer this.

(f) To what audience(s) will the data-model science language speak on behalf
of our various agencies?  Technical only?  Hybrid technical and
non-technical?  One language for technical, a second language for
non-technical?

Like it or not, if we don't attempt to serve most audiences, we will serve
few.  As we specialize more and more, we before less able to
communicate.  Yet as geologic maps are one of our fundamental
points of communication, we have to made understandable
to as wide an audience as possible.

(g) What does each map-producing agency expect to query (search for and
retrieve) from geologic-map data bases produced by the data model? (agency
point of view)

See 20 questions

(h) What kind of geologic information will the typical geologist expect to
put INTO the data model and retrieve FROM it? (geologist point of view)

See 20 questions.  Also, I disagree with Steve, the typical
geologist will build data bases.  Twenty years ago, the
"typical" geologist didn't use computers, word process, do
graphics, or many other things that are standard today.
Much as I don't like it, the days of walking ridges, and
handwriting notes with pencil and paper are numbered.

(i) What kinds of interdisciplinary science should be incorporated into the
data model science language?  Or, put differently, how should the data model
be structured and populated to ensure its utility to the geophysics,
geo-engineering, earthquake, geochemical, and hydrogeologic communities?

Could I see the 20 questions outcome first?

(j) What kinds of feature-level locational-accuracy issues should be
addressed by our science language, as these bear on agency accountability?

I think this is a critical issue but I'm not sure the "science language"
should address this, but the data structuring must by
incorporating reliability criteria.  I believe this is a
different language issue from the "science language" issue.

(k) What kinds of feature-level scientific-confidence issues should be
addressed by our science language, as these bear on agency accountability?

I can see developing a language, I'm just not confident of
its balanced application.  I can see some one acknowledging
that their interpretations are speculative, but can you see
some one acknowledging their interpretation is "of marginal
reliability due to inadequate data"

(l) What kinds of feature-level data-origination issues should be addressed
in our science language, as these bear on agency accountability?

Again, this is not a science language issue but a sourcing issue.
It is critical information and needs to be a part of the data base,
butit isn't a language issue.

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