On 13 February 2012 15:57, Nate Vack <[log in to unmask]> wrote:
> My take on this discussion, coming from a research lab: Metadata isn't meta.

Well, coming from a publishing and repositories world, my take is
slightly different.

> For example, in recordings of, say, blood pressure over time, it's
> common to think about things such as participant identifiers,
> acquisition dates, event markers, and sampling rates as "metadata,"
> and the actual measurements as "data."
>
> But really: those meta things aren't ancillary to data analysis;
> they're essential in keeping analyses organized, and often important
> parameters in running an analysis at all.

That's an interesting distinction though. Do you need all that data in
order to make sense of the results? You don't [necessarily] need to
know who conducted some research, or when they conducted it in order
to analyse and make sense of the data. In the context of having the
data, this other information becomes irrelevant in terms of
understanding what that data says.

But in a wider context, you do need such additional information in
order to be able to use it. If you don't know who conducted the
research, when it was conducted, etc. then you can't reference it.You
can't place it into another context (a follow up study to validate the
findings, or see if something has changed over time). And it's not a
case of saying something has to fall into one category or another, it
may be necessary / useful in both.

> Breaking things down into data versus metadata I think, encourages a
> false (and not very interesting) dichotomy. If information has a use,
> call it what it is: data. Store everything that's useful.

The problem isn't that we have labels for data to be used in different
contexts. It's that just because something does have a label [and may
not necessarily be important to you in your context], that doesn't
mean that it's any less important than something else with another
label.

G