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Institutional measures to encourage and facilitate effective data management and sharing. A matter of cash, careers or c

Institutional measures to encourage and facilitate effective data management and sharing. A matter of cash, careers or cultural change? 7 th DCC/RIN Research Data Management Forum University of Warwick, 3 rd November 2011 Miggie Pickton Research Support Librarian

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Institutional measures to encourage and facilitate effective data management and sharing. A matter of cash, careers or c

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  1. Institutional measures to encourage and facilitate effective data management and sharing. A matter of cash, careers or cultural change? 7th DCC/RIN Research Data Management Forum University of Warwick, 3rd November 2011 Miggie Pickton Research Support Librarian Library and Learning Services

  2. Outline • Background and context • Understanding data management practices at Northampton – DAF project • From project to policy • Positive outcomes • Where next at Northampton? • So is it a matter of cash, careers or cultural change?

  3. Background – the university • About The University of Northampton: • Achieved university status and research degree awarding powers in 2005 • Ambitious plans to develop research capacity • 200+ research students, ??? research active staff – numbers are rising • Increased focus on supporting the research community • Like everyone else... thinking about satisfying funders, increasing research impact, developing research environment, the REF...

  4. Background - the KeepIt project • The JISC-funded KeepIt project aimed to bring together existing preservation tools and services with appropriate training and advice to enable repository managers to formulate practical and achievable preservation plans • Led by a preservation expert – Steve Hitchcock • Featured four exemplar repositories: • eCrystals (science data) • EdShare (educational resources) • UAL Research Online (arts) • NECTAR (research) • A further 12 repository managers attended the KeepIt training course

  5. KeepIt course – tools • Data Asset Framework (DAF) - identify, locate, describe and assess research data assets • Assessing Digital Institutional Assets self assessment toolkit (AIDA) • Keeping Research Data Safe (KRDS) – benefits and costs of a repository • LIFE3 – predictive costing tool for digital content • Eprints preservation toolkit • DROID & JHOVE – file format identification and characterisation • PREMIS - data dictionary for preservation metadata • Plato - preservation planning tool from PLANETS • Digital Repository Audit Method Based on Risk Assessment (DRAMBORA) – repository risk assessment and reporting

  6. Data Asset Framework • Could see an immediate benefit in several tools, but particularly the Data Asset Frameworkfrom the Digital Curation Centre • What is DAF? “The Data Asset Framework is a set of methods to: • find out what data assets are being created and held within institutions; • explore how those data are stored, managed, shared and reused; • identify any risks e.g. misuse, data loss or irretrievability; • learn about researchers’ attitudes towards data creation and sharing; • suggest ways to improve ongoing data management.” (Digital Curation Centre, 2009, p.3)

  7. Why conduct a DAF project? • Little was known centrally about university researchers’ data storage requirements, or the research workflow that incorporates the creation and management of data • No university wide data storage policy or procedure existed • Aware that research funders were beginning to require data as well as published research outputs to be made openly available • In NECTAR (our institutional repository), we had available the infrastructure to store and preserve digital data • Previous studies had noted that the process of undertaking DAF had been valuable in itself, even if the resulting inventory of data was only partial

  8. DAF at Northampton • Project team: • two project researchers (graduate interns) plus a Project Board comprising staff with expertise in repositories, records management and collection development • Ran from May to June 2010 (eight weeks) • Data collection, three stages: • Initial interviews with research leaders in each School; online survey of researchers; one-to-one interviews with researchers • Topics covered: • Types, sizes and formats of research data; data ownership; storage; security; sharing and access (short and long term); funders’ requirements

  9. DAF at Northampton – some findings • Three generic types of researcher (each with characteristic needs and behaviours) - research student; independent researcher; group researcher/collaborator • Data storage needs, behaviours and vulnerabilities varied through the research lifecycle • Consensus in use of some file types (e.g. .doc, .xls, .jpeg) but not others (e.g. for audio, video, databases) • Very few Northampton researchers had applied for funding from an organisation that mandated open access to research data • Just over a half of respondents expressed interest in a university repository for data (either open or closed access)

  10. DAF at Northampton - concerns Lots of good practice, especially in data security, but in some cases: • Uncertainty over ownership of research data • Data still collected in out-dated formats • Data management practices guided by intuition rather than informed by good practice • Data neglected once a project is complete • Researchers often ill-informed (or misinformed) of the services available to them

  11. DAF at Northampton - recommendations • A Research Data Policy to be drafted and approved by the University Research Committee (URC) • University to clarify its position on the ownership of research data • Graduate School, Records Manager and Library staff to develop and promote training sessions and guides to RDM • Information Services to further develop and disseminate expertise in preservation planning to support researchers wishing to store and access their data over the medium to long term • Project findings to be disseminated to Schools and Research Centres, together with advice and guidance in line with the new policy (Full results and recommendations are described in the project report – see Alexogiannopoulos et al., 2010)

  12. From project to policy • October 2010 – DAF project report presented to URC • November 2010 - URC Research Data Working Group convened to discuss: • Scope of policy • Fit with research lifecycle • Procedure to support policy • Relationship with other university policies and practices (e.g. research ethics; academic misconduct) • November-December 2010 – policies from other institutions reviewed

  13. From project to policy • January 2011 – discussions with the DCC re ‘generic’ version of DMP Online • January 2010 – first RDM proposal presented to URC: • RCUK recommendations to be followed (RCUK, 2009) • Principal Investigator to complete a data management plan at the start of every project (DMP Online recommended for this purpose) • A central dedicated storage facility for research data to be provided • Support and training to be offered to researchers • Members of URC expressed concern

  14. From project to policy • URC concerns: • Duplication of effort - “we have to do this already ” ... for funders, professional bodies, etc. • Relevance or applicability to different disciplines • Reluctance to set disposal date (or even review date) – “I’d be very upset if my data were deleted” ...after I had left • Aversion to procedures being mandatory • Expense – who will pay for it? • So back to the drawing board...

  15. From project to policy Revised proposal eventually approved by URC in June 2011: • Emphasis on encouragement rather than mandate • No longer expected for every research project • Simplified internal procedures • Default five year review period • Additional help offered for identifying external data archives

  16. Positive outcomes • The DAF project gave us the chance to have much more meaningful and in-depth discussions with individual researchers – allowing us to learn more of their needs and to promote our services (including NECTAR) • Awareness of the full implications of good research data management has increased • Data management training is now a standard element of research student induction • Greater recognition among researchers of the expertise held by support staff in records and data management • Good research data management should support the University Records Manager’s role in dealing with FOI and EIR requests (JISC, 2010)

  17. Less positively... • Approval of the policy by URC has not yet resulted in obvious behavioural change • Major (ongoing) structural changes across the university support services have hindered progress since June • Without clear incentives (or sanctions), researchers lack motivation to conform to policy

  18. Where next? We need to: • Disseminate the new policy to all Schools and Divisions • Develop RDM training programme – paying particular attention to the needs of different disciplines • Fulfil our commitment to provide a dedicated research data storage facility • How? • Through multiple communication channels e.g. School Research Forums; university website; one-to-ones • Involve Records Manager, library staff and researchers in development of training sessions and guidelines • Seek authorisation from new budget holder; take advice on best way of implementing hardware and software

  19. Where next? How? Gain support from opinion leaders; focus on key messages; emphasise benefits to research groups and individuals. Raise awareness among both academic and support staff. Take advice from DCC and other experts We need to: • Promote the importance and value of effective data management and sharing • Seek ways to support the embedding of good data management practice in research workflows

  20. To conclude • A matter of cash, careers or cultural change? • Cash (i.e. funding) – only if the prospective funder clearly expects good RDM practice or data sharing (otherwise, why bother?) • Careers – there must be a demonstrable link between good RDM and career progression (e.g. data citation) • Cultural change – essential. • All three together? Effective data management and sharing will result from the application of appropriate carrots and sticks by funders and employers, combined with clear demonstration of career benefits and recognition of the value of good data management practice among the scholarly community.

  21. References • Alexogiannopoulos, E., McKenney, S. and Pickton, M. (2010) Research Data Management Project: a DAF investigation of research data management practices at The University of Northampton. Northampton: University of Northampton. Available from: http://nectar.northampton.ac.uk/2736/ [Accessed 27.10.2011]. • Digital Curation Centre (2009) Data Asset Framework: Implementation guide. Available from: http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf [Accessed 27.10.2011]. • JISC (2010) Freedom of Information and research data: Questions and answers. Available from: http://www.jisc.ac.uk/publications/programmerelated/2010/foiresearchdata.aspx [Accessed 27.10.2011]. • Research Councils UK (2009) RCUK Policy and code of conduct on the governance of good research conduct: Integrity, clarity and good management. Available from: http://www.rcuk.ac.uk/documents/reviews/grc/grcpoldraft.pdf [Accessed 27.10.2011].

  22. Acknowledgement We are grateful to the JISC for funding the KeepIt project; to the Graduate Boost programme for supplying the two DAF project researchers, Sam McKenney and Edward Alexogiannopoulos; and to Sarah Jones and Martin Donnelly of the Digital Curation Centre for their help and support with the DAF and DMP Online tools.

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