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UK Research Data Management: overview to ADBU congress, 19 Sep 2013 by Laura Molloy


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Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ABDU congress, 19 Sep 2013 in Le Havre.

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UK Research Data Management: overview to ADBU congress, 19 Sep 2013 by Laura Molloy

  1. 1. Supporting Research Data Management in UK Universities: the Jisc Managing Research Data Programme and university libraries Laura Molloy Humanities Advanced Technology and Information Institute (HATII), University of Glasgow, Digital Curation Centre and Jisc Managing Research Data programme Thursday 19 September 2013 ADBU, Le Havre 1
  2. 2. Why is managing research data important?  Jisc considers it a priority to support universities in improving RDM  Drivers: research funder policies, legislative frameworks, open data agenda …  Good data management is good for research – More efficient research processes, avoidance of data loss, scrutiny can encourage better practice, research benefits of data reuse  Alignment with university missions – Universities want to provide excellent research infrastructure. – Universities want to have better oversight of research outputs. 2
  3. 3. Research Data Challenges  Challenges: the ‘data deluge’… huge quantities of digital data – But it’s not just about addressing storage issues.  Opportunities: data reuse, meta-studies, interdisciplinary grand challenges. – Increasing awareness of research data as an asset. – Digital research data has reuse value - important to obtain full return on public investment.  Results in policy drivers from funders. – Need improved knowledge of how best to realise these policies.  Increasing emphasis on the role of universities and research institutions to provide infrastructure and support for RDM. 3
  4. 4. Royal Society, UK Science as an Open Enterprise Report, 2012  ‘the conduct and communication of science needs to adapt to this new era of information technology’.  ‘As a first step towards this intelligent openness, data that underpin a journal article should be made concurrently available in an accessible database. We are now on the brink of an achievable aim: for all science literature to be online, for all of the data to be online and for the two to be interoperable.’  Royal Society June 2012, Science as an Open Enterprise, ience-public-enterprise/report/ 4
  5. 5. Science as an Open Enterprise Report: six key changes 1. a shift away from a research culture where data is viewed as a private preserve; 2. expanding the criteria used to evaluate research to give credit for useful data communication and novel ways of collaborating; 3. the development of common standards for communicating data; 4. mandating intelligent openness for data relevant to published scientific papers; 5. strengthening the cohort of data scientists needed to manage and support the use of digital data (which will also be crucial to the success of private sector data analysis and the government’s Open Data strategy); 6. the development and use of new software tools to automate and simplify the creation and exploitation of datasets. Royal Society 2012, Science as an Open Enterprise, 5
  6. 6. UK drivers: Research Funder Policies 1. Public good: Publicly funded research data are produced in the public interest should be made openly available with as few restrictions as possible 2. Planning for preservation: Institutional and project specific data management policies and plans needed to ensure valued data remains usable 3. Discovery: Metadata should be available and discoverable; Published results should indicate how to access supporting data 4. Confidentiality: Research organisation policies and practices to ensure legal, ethical and commercial constraints assessed; research process should not be damaged by inappropriate release 5. First use: Provision for a period of exclusive use, to enable research teams to publish results 6. Recognition: Data users should acknowledge data sources and terms & conditions of access 7. Public funding: Use of public funds for RDM infrastructure is appropriate and must be efficient and cost-effective. RCUK Common Principles on Data Policy: 6
  7. 7. EPSRC Research Data Policy Expectations  Policy and expectations:  Research organisations to have RDM policy, advocacy and support functions. (i, iii)  Research data to be effectively managed and curated throughout the life-cycle (viii)  Research organisations to maintain public catalogue of research data holdings, adequate metadata and permanent identifier (v)  Publications to indicate how research data can be accessed (ii)  Data to be retained for 10 years from last access (vii)  Research data management to be adequately resourced from appropriate funding streams (ix)  Roadmap in place by 1 May 2012  Compliance by 1 May 2015 7
  8. 8. Institutional data repositories as an elevator for data collections The Data Pyramid: taken from Royal Society Report, Science as an Open Enterprise: Jisc MRD Blog on role of institutional data repositories repositories-and-the-curation-hierarchy-reflections-on-the-dcc-icpsr-workshop-at-or2012-and-the-royal-societys-science-as-an- open-enterprise-report/ 8
  9. 9. Development of Institutional RDM Capacity The Royal Society Science as an Open Enterprise report recommended that the JISC Managing Research Data Programme ‘should be expanded beyond the pilot 17 institutions within the next five years.’ [Royal Society 2012, Science as an Open Enterprise, p.73] 9
  10. 10. Building Institutional Capacity: Second MRD Programme, 2011-13 Second JISC MRD Programme, 2011-13: Institutional RDM Infrastructure Services 17 Projects RDM Training 5 projects RDM Planning 10 projects Data Publication 3 projects Ownership: High level ownership of the problem, senior manager on steering . Sustainability: Large institutional contributions. Develop business cases to sustain work. Encouraged to reuse outputs from first programme and elsewhere. Mix of pilot projects and embedding projects. Holistic institutional approach to RDM. 10
  11. 11. Reading, Bristol, BADC PIMMS Subject Specific Metadata York, ADS SWORD-ARM arm/ Subject Specific Deposit/Costing Glasgow, St. Andrews, Sunderland CERIF4Datase ts Consortium (CERIF) Bath Research360 Single Institution (A1) Bristol Data.bris Single Institution (A1) UCA, UAL, GSA, Goldsmiths KAPTUR Consortium of Institutions (Creative Arts) (A1) Essex RD@Essex Single Institution (A1) Hertfordshire RDTKHerts Single Institution (A1) Leeds RoaDMaP Single Institution (A1) Lincoln Orbital Single Institution (A1) Newcastle iridium Single Institution (A1) Nottingham ADMIRe Single Institution (A1) UWE UWE RDM Pilot Single Institution (A1) Exeter Open Exeter Single Institution (A2) Manchester MiSS Single Institution (A2) Oxford DaMaRO Single Institution (A2) Southampton DataPool Single Institution (A2) 11
  12. 12. RDMRose (Sheffield) All disciplines, LIS Liaison librarians (PGT and CPD delivery) RDMTPA (Hertfordshire) Physics and astronomy Postgraduate students, early career researchers http://research-data- SoDaMaT (QMUL) Digital music Researchers of all grades oject/sodamat TraD (UEL) Psychology, computer science Librarians, research support staff, postgraduate students, researchers of all grades RDM training projects: phase two 12
  13. 13. Data Management Planning Managing Active Data Processes for selection and retention Deposit / Handover Data Repositories/ Catalogues Components of RDM support services RDM Policy and Roadmap Business Plan and Sustainability Guidance, Training and Support Research Data Registry 13
  14. 14. Jisc MRD MRD Projects DCC DCC IEs How to Develop RDM Services 14
  15. 15. Successful Business Cases  Successful business cases. – Many examples, including Bristol, 2.5 year pilot project with 5 staff; Bath (2FTE); Manchester (total of c.5FTE); Soton (total 3.5FTE); Lincoln (1.x FTE).  Bristol have set KPIs for ongoing service: 1. 30% per annum increase in the total number of datasets deposited into the data.bris repository and issued with a DOI 2. 10% per annum increase in total number of Bristol DOIs cited within publications 3. 20% increase in the number of visitors to the online data.bris repository portal and in secondary data users 4. An increase of 1% in University of Bristol research grant income attributable to high quality Data Management Plans (as a result of DMP support and grant writing surgeries). 5. A reduction of 2.5% in researcher time spent on generic RDM tasks, such as controlled data sharing, preparation of technical metadata, and data publication (as a result of training and the provision of RDM systems and storage). 15
  16. 16. Institutional Policies and Roadmaps  Institutional Research Data Management Policies: policies/uk-institutional-data-policies  Institutional Roadmaps to meet EPSRC Expectations on Research Data: roadmaps 16
  17. 17. Data Management Planning  Detailed guidance on funder requirements for DMPs from DCC: DataPlanReqs_v4%204.pdf  DCC How to Develop a Data Management and Sharing Plan:  DCC DMPonline tool:  Jez Cope, University of Bath, R360 Project 17
  18. 18. Institutional data repositories Research Data@Essex; DataPool, Southampton; RoaDMaP, Leeds; C4D, Glasgow; Cambridge, Exeter, Herts, QMUL Data.bris, Bristol; Orbital, Lincoln; KAPTUR; iridium, Newcastle RD@Essex ReCollect App 18
  19. 19. Metadata Schema for Institutional Data Repositories 19
  20. 20. ReCollect App for Eprints Data Repositories 20
  21. 21. Library skills needed for RDM  Literature searching  Literature evaluation (including appraisal and retention decision-making)  Expert knowledge of subject resources and databases  ‘Background knowledge’ (i.e. discipline-specific knowledge)  Technical knowledge (bibliometrics etc)  Knowledge of information literacy and digital literacy  Understanding of the width of the information landscape and the research lifecycle  Negotiation skills  Complaints and expectation management  Coordination of practice across institution  Advocacy, promotion [of good practice] etc RIN RILADS project (2013) 21
  22. 22. Librarians well positioned for RDM • existing data and open access leadership roles • often run publication repositories • have good relationships with researchers • proven liaison and negotiation skills • knowledge of information management, metadata, information retrieval, etc. The library is leading on most DCC institutional engagements: 22
  23. 23. Potential library roles in RDM  Leading on local (institutional) data policy  Bringing data into undergraduate research-based learning  Teaching data literacy to postgraduate students  Developing researcher data awareness  Providing advice, e.g. on writing DMPs or advice on RDM within a project  Explaining the impact of sharing data, and how to cite data  Signposting who in the Uni to consult in relation to a particular question  Auditing to identify data sets for archiving or RDM needs  Developing and managing access to data collections  Documenting what datasets an institution has  Developing local data curation capacity  Promoting data reuse by making known what is available RDMRose Lite 23
  24. 24. So existing skills = highly relevant  Many existing librarian skills are highly relevant to RDM  Corrall et all (2013): majority of librarians surveyed in favour of RDM training as part of professional education, including CPD 24
  25. 25. JISCMRD Training Projects, phase 1 and 2  Need for discipline-specific research data management / curation training, integrated with PG studies  Five projects in the first programme to design and pilot (reusable) discipline- focussed training units for postgraduate courses:  Heath studies; creative arts; archaeology and social anthropology; psychological sciences; social sciences and geographical sciences: training  Four projects in the second programme: training-five-new-jiscmrd-projects/  Psychology and computer science; digital music; physics and astronomy; subject and liaison librarians. 25
  26. 26. MANTRA Training Materials, University of Edinburgh  Online course built using OS Xerte toolkit. Sections include: – DMPs – Organising Data – File Formats and Transformation – Documentation and Metadata – Storage and Security – Data Protection – Preservation, sharing and licensing  Also software practicals for users of SPSS, R, ArcGIS, Nvivo  Research Data MANTRA:
  27. 27. Sharing and remixing training materials!  MANTRA RDM Training materials:  UEL supportDM training course:  DCC / Northampton, RDM for Librarians:  Nottingham Short Course on RDM: using-rdm-training-materials/  Summary from Laura Molloy on MRD Evidence Gatherer blog: 27
  28. 28. Training for librarians  RDM for librarians, DCC  RDMRose, University of Sheffield  Data Intelligence for librarians, 3TU, Netherlands  DIY Training Kit for Librarians, University of Edinburgh  SupportDM modules, University of East London 28
  29. 29. DCC training course: RDM for Librarians  3 hour course by the DCC covering: – Research data and RDM – Data management planning – Data sharing – Skills – RDM at [INSERT YOUR UNI]  Slides and accompanying handbook  Used UKDA guide as pre-event reading  29
  30. 30. Jisc MRD training: RDMRose  Taught and CPD learning materials in RDM tailored for information professionals, by the Uni of Sheffield  8 sessions, each = 0.5 day of study  Strong emphasis on practical hands-on activities  Also offer a short (2hr) course called RDMRose Lite  30
  31. 31. Data Intelligence for Librarians  A course produced by 3TU, a consortium of technical universities in the Netherlands  Combination of online and face-to-face education  Four meetings to learn and share knowledge  Theory (on website) and assignments are conducted between sessions  31
  32. 32. DIY Training Kit for Librarians  By EDINA and Data Library at University of Edinburgh  Self-directed course, intended to be used by a group of librarians to build confidence in supporting researchers  MANTRA modules as pre-reading, short presentation, reflective questions and exercises to guide discussion  Five face-to-face sessions – Data Management Planning – Organising and documenting data – Data security and storage – Ethics and copyright – Data sharing  32
  33. 33. SupportDM  SupportDM comprises five sessions – About research data management – Providing guidance and support for researchers – Data Management Planning – Selecting which data to keep – Cataloguing and sharing data  Each topic is introduced in a face-to-face session and explored via exercises and discussion  Learning is reinforced via an online tutorial and practical exercises  33
  34. 34. Published reports / articles: academic librarians in RDM  Swan, Brown (2008): The skills, role and career structure of data scientists and curators  CILIP (2008): Data librarianship – a gap in the market terview%20with%20Macdonald%20and%20Martinez-Uribe.htm  Lyon (2009): Open science at web scale  Pryor, Donnelly (2009): Skilling Up to Do Data: Whose Role, Whose Responsibility, Whose Career? doi:10.2218/ijdc.v4i2.105  Molloy, Snow (2011): DaMSSI final report (inc. training recommendations)  Auckland (2012): Reskilling for Research. skilling-research  Corrall et al (2013): Emerging trends in library support for research. Library Trends, May 2013. doi:10.1353/lib.2013.0005 34
  35. 35. Merci / thank you!  E-mail:  Twitter: @LM_HATII ; #jiscmrd ; #UKDCC  JISC Managing Research Data Programme:  JISC MRD Programme Blog:  Digital Curation Centre:  RESEARCH-DATAMAN Discussion List: DATAMAN  Slide acknowledgements: Dr Simon Hodson, Joy Davidson, Sarah Jones and colleagues at the DCC 35