The Landscape of Research Data Management - where are funders, researchers and publishers on the hype cycle

1. The Landscape of Research Data
Management
September 2016, 4TU Board
Meeting, Utrecht
Alastair Dunning, @alastairdunning
Head, TU Delft Research Data Services &
Coordinator, 4TU Centre for Research Data
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2. A generic landscape ….
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3. The hype cycle is a graphic
… developed and used
by American
information technology
research and advisory
firm Gartner for
representing the
maturity, adoption and
social application of
specific technologies.
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4. It measures visibility and
popularity and hype of
a technology as it
matures across time
Technology
Trigger
Peak of Inflated
Expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
Productivity
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5. Can we map the landscape
of research data
management across this
cycle ?
Can we paint on the
landscape where
different stakeholders
are currently sitting?
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6. Research Data Management Stakeholders
Where can we put them on the landscape?
• Funders
• Libraries / Institutional
Repositories
• Private Companies
● Journals and Publishers
● Researchers
○ 1. Mainstream
○ 2. Engaged
○ 3. Cutting Edge
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7. EU - Dealing with Data
Management Plan now
obligatory for all
Horizon2020 projects
NWO has made Data
Management obligatory
for all its programmes
from 1 October 2016
Funders
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8. All large US funders have
data management
policies
UK Research Councils have
underlying Data Policy
to underpin each
separate area of study
Funders
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9. - Nature, Where are the Data?
“All research papers in Nature
(and 12 other related titles)
will be required to include info
how others can access the
underlying data”
- Science - “After publication, all
data and materials necessary
to understand, assess, and
extend the conclusions of the
manuscript must be available
to any reader of Science”
Journals and
Publishers
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10. Increasing number of data
journals that allow for
publication and review of
the data
But Flagship journals are not
the full range of journals -
the long tail of journals are
still focussed on, with the
resulting scholarly impact
focussed on citation at
article level
Journals and
Publishers
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11. Some disciplines (big data
physics, some areas of
life sciences) have
specific methods for
creating and publishing
data
The Elixir project for life
sciences is an excellent
example
Cutting Edge
Researchers
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12. Data is not just an add for
reproduction or
verification.
It is integral part of the
intellectual process,
capable of being reused
and reanalysed
sometimes with more
intellectual capital than
related articles
Cutting Edge
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13. • Appreciates the
importance of good
data management
• Importance of
maintaining their own
data for their own or
maybe others’ use
• Archiving may be
needed for verification
of the data
• But not an essential part
of their workflow
Engaged
Researchers
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14. Still to be affected by data
management.
Mainstream
Researchers
• This is not my priority
• Why would I do that?
• People will steal my results
• Data management is a waste
of time
• Nobody will understand my
data
• It would take me 5 years to
find all my data
• We’re already doing it
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15. Private
Companes
Some private companies have
fantastic data archiving,
although often deeply
embedded in proprietary
systems that work against
sharing
But most, especially, smaller
and medium enterprises do
not have time nor money
to invest in data archiving.
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16. Most repositories dealing with the
engaged researcher, especially
institutional repositories.
Subject based repositories tend to offer
more specific services for the
discipline in question
Repositories /
Archives
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17. How will the landscape alter?
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18. Journals, Publishers, Funders, Repositories
will continue to engage with data management, and
respond to researcher practice and innovation
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19. Researchers
Mainstream researchers will
become more engaged as data
management becomes part of
everyday practice. Archiving
data (somewhere) will be a
normal part of activity.
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20. Researchers
The number of Cutting Edge
researchers will grow; living
data (ie data that is linked as
part of the linked open web,
and is constantly added or
modified) will become more
frequent
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21. Implications for Data Archives
• Visibility - More and more options for archiving are available. Local
solutions must be known and trusted for those who simply need to
archive data
• Coping with subjects / formats and open data - More data will be
living and changing. How do we provide a service that allows data to
becoming fully open? Should we focus on data / formats from
certain disciplines?
• Integration with other tools - More and more data tools are build.
How does an archive of data interact with , for example, automated
sensors, data manipulation tool, data cleaning software?
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22. Implications for Data Archives (2)
• Data will be re-used more often - the licences must allow for re-use
and combining for other datasets. It must be described and exposed
in ways that make it easier to find
• Other technical universities have similar issues - closer alliances with
them at international level will help tackle these issues
• Questions?
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