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The Landscape of Research Data Management


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

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The Landscape of Research Data Management

  1. 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 1
  2. 2. A generic landscape …. 2
  3. 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. 3
  4. 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 4
  5. 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? 5
  6. 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 6
  7. 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 7
  8. 8. All large US funders have data management policies UK Research Councils have underlying Data Policy to underpin each separate area of study Funders 8
  9. 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 9
  10. 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 10
  11. 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 11
  12. 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 12
  13. 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 13
  14. 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 14
  15. 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. 15
  16. 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 16
  17. 17. How will the landscape alter? 17
  18. 18. Journals, Publishers, Funders, Repositories will continue to engage with data management, and respond to researcher practice and innovation 18
  19. 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. 19
  20. 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 20
  21. 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? 21
  22. 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? 22