- Tools for creation of formal metadata
- Metadata in Plain Language
Editorial: Top ten metadata mistakes
Lynda Wayne asked a number of people:
What are the top ten most common metadata errors?
(Lynda got replies from a number of people and has provided
an edited version of the compiled responses on the FGDC web site.
I've provided my response to the initial inquiry here because some
of my concerns aren't reflected in her final version.)
As the 'brain-trust' of the national metadata effort, your input would be
appreciated. Feel free to interpret the question in your own manner -
specific fields that are commonly misunderstood, bad approaches, general
confusion, or organizational/management issues.
Reply by Peter Schweitzer on 25 July 2000:
I would enlarge the problem beyond the metadata itself to the
information processing in general and the process of data
management. Here's my list, Letterman-style:
(Later I noticed that I had written 12, not 10, so I've renumbered them here.)
(for Arc/Info users)
Taking time to document things that are consequences of the GIS,
like making detailed descriptions of AREA, PERIMETER, LPOLY#, RPOLY#,
FNODE#, TNODE#, cover#, cover-ID, and the like.
(for Arc/Info users)
Simply dumping the results of ITEMS into an
Entity_and_Attribute_Overview and calling that enough.
People need to know the units of measured variables, and "percent"
is not a unit of measure.
Putting too much
faith in mp.
Human review is the thing that really matters.
mp can help, but isn't the arbiter of what is and what is not good
metadata. Prioritize errors like this, from most serious (fix) to
least serious (understand and let go):
- Indentation problems
- Unrecognized elements
- Misplaced elements
- Too many of some element
- Missing elements
- Empty elements
- Improper element values
- Warnings and upgrades
Making too many metadata records. People who try to document every GIS coverage or data table
can wear themselves out. Some aggregation is good for both the producer
and the user. Ancillary coverages can be described as
Not making enough
metadata records. Trying to cram all of the information about
an entire research program into a single metadata record will drive
you and your potential users crazy. Split when sources, processing,
or spatial reference varies.
Agonizing over the most
These include, but probably aren't limited to
and, to a lesser extent,
Misunderstanding Enumerated_Domain as numerical values. Attributes
measure (or count), categorize, or characterize real things.
Those functions are expressed as Range_Domain (for measures
or counts) and Enumerated_Domain (for categories). The hardest
part is how to describe attributes whose values are simply text
describing something, or are names like place names. This is a
deficiency in the FGDC standard; there should be another type
of Attribute_Domain_Values for descriptions; a better alternative
might be to make Attribute_Domain_Values mandatory if applicable
and not applicable if the values are sufficiently descriptive.
about precision for statements about accuracy. I do this often,
because what I know is how variable the values are, and I don't know
the true values that they estimate.
Larding the metadata with uninformative values.
People can honestly disagree about this, but
I find it aggravating to see more than a few
see above (or below)
. Reasonable defaults should be
assumed. For example, if no Process_Contact
people should assume that the Point_of_Contact
the processing or knows who did, and that the people who did
are either Originators
or are listed in Data_Set_Credit
Likewise, any of the elements
if missing, should be assumed to have the value "this report"
or some similar self-reference.
Choosing a tool because it's free or because it's commercial.
Making, maintaining, reviewing, and reading metadata cost so
much more time and energy than the tools do that price per se
shouldn't direct the choice of tools.
Not recognizing that "the metadata problem" involves not only tools and training,
but also work-flow strategy and even the philosophy governing how
your organization interacts with the users of its data.
Not asking for help from the community. Beyond all the hype and promises (empty and
fulfilled), beyond all the tools, training, and technology, what NSDI
has done is bring a common language and common purpose to a highly
diverse group of people, and we have found in each other consolation,
challenge, and care.