The Research Data Management movement is moving on apace. Tools are working and adoption is growing. Policy development is starting to back up the use of those tools and there are some big ambitious goals set out for the next few years. But has the RDM movement taken the vision of data intensive research to its heart? Does the collection, sharing, and analysis of data about research data management meet our own standards?

Cameron Neylon

::: {.wp-caption .alignleft style="width: 250px"}
[![Data Represented in an Interactive 3-D
Form](http://cameronneylon.net/wp-content/uploads/2011/11/5097547405_6701529706_m2.jpg "Data Represented in an Interactive 3-D Form"){.zemanta-img-inserted
.zemanta-img-configured decoding="async" loading="lazy" width="240"
height="160"}](http://www.flickr.com/photos/30369883@N03/5097547405)

Image by Idaho National Laboratory via Flickr
:::

I wrote this post for the [Digital Curation
Centre](http://en.wikipedia.org/wiki/Digital_Curation_Centre "Digital Curation Centre"){.zem_slink
rel="wikipedia"} blog following the [Research Data Management Forum
meeting](http://www.dcc.ac.uk/node/9184)Â run in Warwick a few weeks
back. If you feel moved to comment I'd ask you to [do it over
there](http://www.dcc.ac.uk/news/reflections-research-data-management).

*The Research Data Management movement is moving on apace. Tools are
working and adoption is growing. Policy development is starting to back
up the use of those tools and there are some big ambitious goals set out
for the next few years. But has the RDM movement taken the vision of
data intensive research to its heart? Does the collection, sharing, and
analysis of data about research data management meet our own standards?
And is policy development based on and assessed against that data? Can
we be credible if it is not?*

Watching the discussion on research data management over the past few
years has been an exciting experience. The tools, that have been
possible for some years, now show real promise as the somewhat raw and
ready products of initial development are used and tested.

Practice is gradually changing, if unevenly across different
disciplines, but there is a growing awareness of data and that it might
be considered important. And all of this is being driven increasingly by
the development of policies on data availability, data management, and
data archiving that stress the importance of data as a core output of
public research.

The vision of the potential of a data rich research environment is what
is driving this change. It is not important whether individual
researchers, or even whole community, gets how fundamental a change the
capacity to share and re-use data really is. The change is driven by two
forces fundamentally external to the community.

The first is political, the top down view from government that publicly
funded research needs to gain from the benefits they see in data rich
commerce.Â A handful of people really understand how data works at these
scales but these people have the ear of government.

The second force is one of competition. In the short term adopting new
practices, developing new ways of doing research, is a risk. In the
longer term, those who adopt more effective and efficient approaches
will simply out compete those who do not or can not. This is already
starting happening in those disciplines already rich in shared data and
the signs are there that other disciplines are approaching a tipping
point.

Data intensive research enables new types of questions to be asked, and
it allows us to answer questions that were previously difficult or
impossible to get reliable answers on. Questions about weak effects,
small correlations, and complex interactions.Â The kind of questions
that bedevil strategic decision-making and evidence based policy.

So naturally youâ€™d expect that the policy development in this area,
being driven by people excited by the vision of data intensive research,
would have deeply embedded data gathering, model building, and analysis
of how research data is being collected, made available, and re-used.

I donâ€™t mean opinion surveys, or dipstick tests, or case
studies.Â These are important but theyâ€™re not the way data intensive
research works. They donâ€™t scale, they donâ€™t integrate, and they
canâ€™t provide the insight into the weak effects in complex systems
that are needed to support decision making about policy.

Data intensive research is about tracking everything, logging every
interaction, going through download logs, finding every mention of a
specific thing wherever on the web it might be.

Itâ€™s about capturing large amounts of weakly structured data and
figuring out how to structure it in a way that supports answering the
question of interest. And its about letting the data guide you the
answers it suggests, rather than looking within it for what we
â€œknowâ€? should be in there.

What I donâ€™t see when I look at RDM policy development is the detailed
analysis of download logs, the usage data, the click-throughs on
website. Where are the analyses of IP ranges of users, automated
reporting systems, and above all, when new policy directions are set
where is the guidance on data collection and assessment of performance
against those policies?

Without this, the RDM community is arguably doing exactly the same
things that we complain about in researcher communities. Not taking a
data driven view of what we are doing.

I know this is hard. I know it involves changing systems, testing things
in new ways, collecting data in ways we are not used. Even imposing
disciplinary approaches that are well outside the comfort zone of those
involved.

I also know there are pockets of excellent practice and significant
efforts to gather and integrate information. But they are pockets. And
these are exactly the things that funders and RDM professionals and
institutions are asking of researchers. They are the right things to be
asking for, and weâ€™re making real progress towards realizing the
vision of what is possible with data intensive research.

But just imagine if we could support policy development with that same
level of information. At a pragmatic and political level it makes a
strong statement when we â€œeat our own dogfoodâ€?. And there is no
better way to understand which systems and approaches are working and
not working than by using them ourselves.

::: {.zemanta-pixie}
[![Enhanced by
Zemanta](http://img.zemanta.com/zemified_e.png?x-id=923497af-304f-447d-b03d-c58a80bc8d88){.zemanta-pixie-img
decoding="async"}](http://www.zemanta.com/ "Enhanced by Zemanta"){.zemanta-pixie-a}
:::


Image by Idaho National Laboratory via Flickr  I wrote this post for the Digital Curation Centre blog following the Research Data Management Forum meetingÂ run in Warwick a few weeks back. If you feel moved to comment I’d ask you to do it over there.
<em>
 The Research Data Management movement is moving on apace. Tools are working and adoption is growing.
</em>

Rogue Scholar Beiträge

Reflections on research data management: RDM is on the up and up but data driven policy development seems a long way off.