A comment by Frederic (Ric) Wilson about
(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.
Further discussion of Response to "some generic issues to consider" (this page):
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