New UCD1+ for SIA protocol
Patricio F. Ortiz
pfo at star.le.ac.uk
Thu Mar 25 10:22:20 PST 2004
I fully agree with you, I think you hit the nail, it is the dataset
that the user gets what needs proper description, regardless of what's
been done to reach this result.
A dataset has an angular scale ("/pix) or a linear scale (km/pix, eg,
solar or planetary images), the dataset's origin may be real
observations or simulations, simple observations (classical CCD image),
mosaics, images based on radio data, multiwavelength composites, whatever.
Scales can be degraded or modified from the initial products (eg, DSS)
As you said, something similar happens when describing the time
coverage. Quantities which make sense to individual observations don't
necessariry apply to final products (datasets), for instance,
exposure_time != end_of_exposure - start_of_exposure in a dataset which
does not represent an individual observation.
Color composite images are another example of a final product where the
individual observations' parameters may vary considerably, talking about
instrument.scale or obs.scale does not seem appropriate anymore. At the
time of the construction of UCD0 it did make sense to group these items (read
drop these items) in the "instrumental" category. That doesn't apply
On Thu, 25 Mar 2004, Doug Tody wrote:
> On Thu, 25 Mar 2004, Anita Richards wrote:
> > > 1) VOX:Image_Scale --> instr.scale
> > >
> > > A much better alternative could be the definition of the new ucd1+:
> > > obs.image.scale
> > That still suggest to me that the scale is inherant to the observations -
> > but it is better
> This is a general issue with what we are calling the "observation"
> data model. This may be just a naming issue but I worry that we may try
> to describe actual observations.
> What we really need to do for VO is characterize the physical attributes
> of a dataset. A dataset may be a calibrated observation, or it may be
> the result of an arbitrary amount of processing of multiple observations,
> or it may be synthetic data. It does not matter how the dataset was
> generated if we describe only the physical attributes of the actual final
> dataset we are dealing with. Scale and resolution are good examples of
> such physical attributes.
> For VO data analysis where we may need to deal uniformly with data from
> many origins, physical dataset characterization is what is needed. At this
> level we should not have any information about the actual observations,
> instrument characteristics, etc., (if any) used to produce the dataset.
> Such information may be present in each individual dataset, and can be
> useful to fully understand individual datasets, but is not very useful
> for automated processing of data from many origins.
> A simple example is exposure time. While a typical attribute of an
> individual original observation, it tells us almost nothing about an
> arbitrary dataset. To understand what this means we would have to
> understand the full instrumental model and configuration, and all the
> post-processing done to get to the final dataset we are actually looking at.
> The related physical dataset attributes are the sampling and coverage in
> time of the final (possibly aggregate) dataset, and some physical measure
> of the limiting flux of a signal detected by the dataset.
Patricio F. Ortiz pfo at star.le.ac.uk
Department of Physics and Astronomy
University of Leicester Tel: +44 (0)116 252 2015
LE1 7RH, UK
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