RDM requirements gathering with DAF


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An overview to the Data Asset Framework (DAF) and how it has been used to gather requirements for Research Data Management

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RDM requirements gathering with DAF

  1. 1. … because good research needs good data Funded by: DAF: gathering requirements on research data management Sarah Jones DCC, University of Glasgow sarah.jones@glasgow.ac.uk
  2. 2. … because good research needs good data BACKGROUND TO DAF
  3. 3. … because good research needs good data Recommendation for DAF “Jisc should develop a Data Audit Framework to enable all universities and colleges to carry out an audit of departmental data collections, awareness, policies and practice for data curation and preservation” Liz Lyon, Dealing with Data: Roles, Rights, Responsibilities and Relationships, (2007)
  4. 4. … because good research needs good data The problem How can organisations realise the value of their research data assets when it is unclear: •what data is held •where it is located •and how it is being managed?
  5. 5. … because good research needs good data What is DAF? • A methodology to ‘audit’ data holdings and investigate data management practice • Created in a 6-month Jisc project in 2008 • Four pilot projects also funded by Jisc • The name has changed!
  6. 6. … because good research needs good data The methodology www.data-audit.eu/DAF_Methodology.pdf
  7. 7. … because good research needs good data Testing the method: pilot projects • University of Edinburgh – physiology, divinity, history, brain imaging, astronomy • King’s College London – humanities, social science, physical science, engineering, medical • Imperial College London – Chemical engineering, physics & business school • University College London – Scandanavian studies, archaeology, physics/astronomy, language and communication, phonetic sciences, interdisciplinary www.data-audit.eu/users.html
  8. 8. … because good research needs good data What we found • Lots of data being created • Few policies for data creation, storage & management • Researchers unsure where to begin • Unaware of available support • Often no place of deposit or funds for preservation • But, some pockets of good practice to build on www.ijdc.net/ijdc/article/view/91/109
  9. 9. … because good research needs good data Key conclusion for using DAF Most unis are in the very early stages RDM infrastructure is lacking  emphasis on scoping needs rather than registering data
  10. 10. … because good research needs good data HOW TO GATHER REQUIREMENTS
  11. 11. … because good research needs good data Basic steps to follow 1. Determine what you want to find out – Define scope / expected outcomes – Research organisational context 2. Survey current RDM practices and provision – Set up online questionnaires, interviews, meetings… – Identify major gaps and weaknesses to be addressed 3. Provide recommendations for RDM work
  12. 12. … because good research needs good data What are you trying to find out? • General overview of RDM practice and needs • Focusing on a specific discipline / research group’s needs • Capacity planning exercise i.e. IRs starting to take data • Service gap analysis e.g. Oxford scoping digital repository study www.ict.ox.ac.uk/odit/projects/digitalrepository • Responding to specific need identified e.g. improving archiving workflow in GUARD at Glasgow
  13. 13. … because good research needs good data Who are you going to speak to? • PhD students / Research Assistants • PIs / Research Group Leaders • Local IT / research support • Administrators (locally & research office) • Professional service staff (library, IT, FoI...)
  14. 14. … because good research needs good data How will you gather information? • Desk-research • Questionnaires • Interviews • Focus groups • Shadowing / observational approach
  15. 15. … because good research needs good data Pros and cons of methods (1) • Desk-based research  Good for initial planning stage and to collate background information  Remote access a challenge and data could be hard to understand. TIP: use PhD students • Focus groups  Good for reaching consensus and developing ideas  May be difficult to set up. TIP: Work through local advocates. • Shadowing / observational approach (e.g. data diaries, immersion...)  Good to spot workflow inefficiencies or issues that can’t be well-articulated.  Gives an understanding of researchers’ practices and needs for support.  Very resource intensive. TIP: Use in focused pilots.
  16. 16. … because good research needs good data Pros and cons of methods (2) • Online questionnaires  Good for collecting basic overview and to obtain wide participation  Can be useful to identify potential interviewees  Uptake can be low – best if pushed by internal advocate  Make sure software meets needs. TIP: Use Bristol Online Surveys • Interviews  Provides quality information – ability to develop questions to follow up on key points  Allow you to gauge awareness of data issues better  Could trial short interviews e.g. 20 mins over phone  Quite time consuming – TIP: one person to interview and one to note-take, or record. Lifecycle model can provide useful framework to guide discussion.
  17. 17. … because good research needs good data How will you ensure participation? • Senior management support e.g. circulating invites • Internal advocates / champions • Prizes / incentives • Sell the benefits to the individual and institution • Imperial College – http://ie-repository.jisc.ac.uk/307 pp19-20 • University of Oregon business case for DAF audit - http://libweb.uoregon.edu/inc/data/faculty/datainventorybizcase.pdf
  18. 18. … because good research needs good data Themes addressed in DAF surveys • Data: type / format, volume, description, creator, funder • Creation: procedures, metadata & documentation, naming, versioning • Management: storage, backup, roles and responsibilities, planning • Access: restrictions, rights, security, frequency, collaboration, publish • Sharing: requirements to share, methods, attitudes / fears • Preservation: selection / retention, repository services, obsolescence • Gaps / needs: services, advice, support, infrastructure
  19. 19. … because good research needs good data DAF process at Northampton Data collection in three stages 1. initial interviews with research leaders in each School 2. online survey of researchers 3. 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 Report at: http://nectar.northa
  20. 20. … because good research needs good data DAF questionnaire at UEL 1. About you and your research – School, role, research activities, data types, volume, ownership... 1. Current practice and awareness – Storage, backup, responsibilities, guidelines, DMPs... 1. Sharing data – Attitudes towards sharing, methods, use of data centres... 1. Issues encountered – Data loss, lack of infrastructure & support, confidence in RDM... 1. Support at UEL – Awareness of support, who they’d contact, what they’d like... 1. Follow up – Other comments, willingness for interview / case study...
  21. 21. … because good research needs good data DAF interviews at Oxford 1. Briefly explain your area of research / types of research questions 2. Discuss research tasks that involve data management at: a) Funding application e.g. planning data creation / management b) Data collection e.g. data types, standards, methods c) Processing of data e.g. annotation, storage, security d) Publishing e.g. plans, data sharing, deposit 1. Support at local / institutional level for the management of data 2. Challenges when managing data / service requirements 3. Final questions / de-brief Report at: http://www.disc-uk.org/docs/DAF-Oxford.pdf
  22. 22. … because good research needs good data Further examples of DAF studies • 'Pre-interview questionnaire' from QMUL: http://rdm.c4dm.eecs.qmul.ac.uk/blog/DAF-interviews • UWE 'researcher questionnaire: www1.uwe.ac.uk/library/usingthelibrary/servicesforresearchers/ datamanagement/managingresearchdata/projectoutputs/workpackages1and2.aspx • 'Interview protocol' from Uni of Hertfordshire: http://research-data-toolkit.herts.ac.uk / 2012/06/rdm-audit-and-project-benefit-metrics • Online survey from Uni of Newcastle: http://iridiummrd.wordpress.com/ 2012/05/22/iridium- research-data-management-requirements-online-survey • Uni of Southampton report: http://eprints.soton.ac.uk/196243/1/IDMB_Survey_Report.pdf and questionnaire: http://eprints.soton.ac.uk/195959 • Various examples in DAF implementation guide: http://www.data-audit.eu/docs/ DAF_Implementation_Guide.pdf • And more …
  23. 23. … because good research needs good data Lessons and tips • A data champion can help persuade others to get involved • PhD students are well-placed to help. They understand the field, know the researchers and often manage the data. • Questionnaires are a good way to identify people for interview. • DCC lifecycle model can help to structure discussion in interviews. • If you frame interviews in terms of DMPs, it will benefit researchers – they then know what to write on the next grant proposal.
  24. 24. … because good research needs good data Thanks - any questions? DCC guidance, tools and case studies: www.dcc.ac.uk/resources Follow us on twitter: @digitalcuration and #ukdcc
  25. 25. … because good research needs good data Discussion • What do you want to investigate at Reading? – Consider key themes to cover and questions to ask • How will you go about collecting the information? – Consider which methods you’ll use, who will you ask etc • How will you ensure participation? • How does this fit into the broader programme of work?