[@OKau] "High value" datasets

Markus Buchhorn markus at intersect.org.au
Tue Apr 14 07:35:05 UTC 2015

Hi Cassie

One of the projects I was involved in (RDSI - www.rdsi.edu.au) had to 
tackle this to allocate infrastructure funding towards the support of 
research data in Australia. That was not easy! In effect it had to rank 
research data collections, across disciplines, and even disciplines had 
their own, quite different, merit criteria.

However, since RDSI was publicly funded and supported publicly funded 
research, having the dataset (more) publicly accessible was one key 
measure. That includes the data formats, the description, the 
registration/discover-ability, a presentation mechanism, etc. Then you 
get a whole range of other characteristics. At the same time there had 
to be a scalable assessment process, which simplified elements, but it 
pulled up a lot of quite challenging questions, which had not been 
tackled before anywhere.

It's a little skewed from your question because it's not a generic 'high 
to low' measure but a 'can we fund it from this finite dollar pool' 
metric, however it has some useful ideas.

There's a summary of the characteristics/approach in one of the RDSI 
- the last section or so. Apologies for the style but it was part of an 
annual report to the Govt. I can't find a simpler version at the moment 
but will keep looking. The criteria were fleshed out in subsequent 
discussions with the service providers and communities.

Happy to chat more about if if folks are interested


On 14/04/2015 4:30 PM, Cassie Findlay wrote:
> Hi all
> Has anyone come across good criteria or defined methods for 
> identifying 'high value' datasets? If, for example, you are looking at 
> a whole of government jurisdiction. I found some in thisEU report 
> <http://ec.europa.eu/isa/documents/publications/report-on-high-value-datasets-from-eu-institutions_en.pdf> 
> but would like to gather some more.
> I realise that value is a highly subjective thing to assert (valuable 
> for whom, why?) and really like Rosie's work on defining the problems 
> first, in order to then work out where you might find datasets of 
> value, but all that aside :) - are there examples out there of work to 
> define high value stuff?
> Many thanks
> Cassie Findlay
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