The Landscape of Research Data Management

September 2016, 4TU Board Meeting, Utrecht

Alastair Dunning, @alastairdunningHead, TU Delft Research Data Services & Coordinator, 4TU Centre for Research Data

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A generic landscape …. 2

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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It measures visibility and popularity and hype of a technology as it matures across time

Technology Trigger

Peak of InflatedExpectations

Trough of Disillusionment

Slope of Enlightenment

Plateau of Productivity

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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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Research Data Management StakeholdersWhere 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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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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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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- 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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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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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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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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• 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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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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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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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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How will the landscape alter?

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Journals, Publishers, Funders, Repositories will continue to engage with data management, and

respond to researcher practice and innovation

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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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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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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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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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