Sorry Dr. Booth, but I can't accept this conclusion.
"We need technical solutions that will help us work through and around these social barriers."
The root problem with Linked Data is deeply embedded and I would argue inseparable from the current methods of the Mobile Web. The ID provided by a Mobile Device is not the ID best used for Linked Data to prosper.
A little thought experiment: Say you had a telescope with a single magnification and aimed it at a forest nearby. At some optimum magnification you could identify individual trees (Linked Data) and at some much higher magnification you would see tree bark (uncoupled microdata) everywhere you looked. Linked Data only works when the observed "body" (tree) is at rest. Newton's First Law and the Heisenberg Uncertainty Principle vanish only to return with a vengeance when initial conditions are modified (the body moves OR magnification is increased step-wise).
For socio-economic data including Health Data, the implication is that a Mobile Device is suitable to distribute data but cannot be used to infer metadata in a current experiment. Collecting data in real time is problematic too. I believe that if Mobile Device Technology is the "constant" and Linked Data ID's are "flexible" we will never get to where we want to go. Technical solutions for research data are not impeded by social barriers but rather by the tools (The Mobile Web) which exaggerate the commercial value of Personally Identifiable Information and over-magnify the data rendering making said data useless for research. Smart Home Appliances are not the answer either, but I've talked too much already.
________________________________
From: David Booth <david@dbooth.org>
To: Stefan Decker <stefan.decker@deri.org>
Cc: Helena Deus <helena.deus@deri.org>; Melvin Carvalho <melvincarvalho@gmail.com>; "nathan@webr3.org" <nathan@webr3.org>; Michael Hausenblas <michael.hausenblas@deri.org>; "semantic-web@w3.org" <semantic-web@w3.org>; "public-lod@w3.org" <public-lod@w3.org>; "www-rdf-interest@w3.org" <www-rdf-interest@w3.org>; "protege-discussion@lists.stanford.edu" <protege-discussion@lists.stanford.edu>; "semanticweb@yahoogroups.com" <semanticweb@yahoogroups.com>; "dbworld@cs.wisc.edu" <dbworld@cs.wisc.edu>; "machine-learning@egroups.com" <machine-learning@egroups.com>; "taverna-users@lists.sourceforge.net" <taverna-users@lists.sourceforge.net>; "bbb@bioinformatics.org" <bbb@bioinformatics.org>
Sent: Friday, July 20, 2012 9:00 AM
Subject: Re: position in cancer informatics
On Fri, 2012-07-20 at 10:22 +0100, Stefan Decker wrote:
> The discussion seem to point to a deeper question: how to enable crowd
> sourcing of the analysis of these kind of data sets? This may involve
> running of analysis code or maybe even manual work.
> What kind of computational infrastructure would we need to enable
> this? And how do we validate and aggregate results?
Unfortunately, in the USA at least, the biggest barriers are not
technical, but social, because: (a) health information privacy laws such
as HIPAA
http://www.hhs.gov/ocr/privacy/
make it difficult or impossible to publish the raw data that would be
most useful for research; and (b) researchers do not have the incentive
to publish their data that might allow other researchers to make
discoveries.
There is a tension between privacy and the usefulness of data for
research, because full de-identification removes information that can be
critical to determining cause and effect, such as dates, times and
locations.
We need better ways -- both bottom-up, such as http://weconsent.us/, and
top-down, such as legal changes -- to both encourage the availability of
research data and to facilitate appropriate access to it, such as
establishing well-defined tiers of access for different purposes.
We need technical solutions that will help us work through and around
these social barriers.
--
David Booth, Ph.D.
http://dbooth.org/
Opinions expressed herein are those of the author and do not necessarily
reflect those of his employer.