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This presentation explores the challenges institutions and disciplines face in managing and curating data. It covers topics such as data requirements, risks, costs, and building capacity and capability. The session also includes case studies and recommendations for data management.

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  1. Facing the Data Challenge : Institutions, Disciplines, Services & Risks Kevin Ashley, director@dcc.ac.uk Dr Liz Lyon, Associate Director, UK Digital Curation Centre Director, UKOLN, University of Bath, UK 5th DCC Regional Roadshow, Cambridge, November 2011 DCC is supported by: This work is licensed under a Creative Commons LicenceAttribution-ShareAlike 2.0

  2. Overview • Facing the data challenge : Requirements, Risks, Costs • Reviewing Data Support Services : Analysis, Assessment, Priorities • Building Capacity & Capability : Skills Audit • Developing a Strategic Plan : Actions and Timeframe

  3. Facing the Data Challenge Institutional Diversity http://www.flickr.com/photos/toranosuke/3004864392//

  4. Based on DCC Curation Lifecycle Model

  5. Case studies Oxford, Cambridge, Edinburgh, Southampton

  6. Disciplinary Diversity eScience Case studies

  7. SCARP Case studies • Atmospheric data • Neuro-imaging • Tele-health • Architecture • Mouse Atlas http://www.flickr.com/photos/30435752@N08/2892112112/

  8. Recommendations: • JISC • HE & Research funders • Publishers & Learned societies • HEIs and research institutions • Researchers & scholars http://www.dcc.ac.uk/sites/default/files/documents/publications/SCARP%20SYNTHESIS.pdf

  9. http://opus.bath.ac.uk/20896/1/erim2rep100420mjd10.pdf http://www.data-archive.ac.uk/media/203597/datamanagement_socialsciences.pdf

  10. Quick & simple deposit • Software tools • Laboratory archive • Crystallography community engaged • ‘Embargo’ facility • Structured foundations • Discoverable & harvestable

  11. Data Curation Profiles

  12. Disciplinary focus Capability? “Maturity”?

  13. Exercise 1a: Gathering requirements • What are the researchers’ datarequirements? • What datasets exist already? Standards? • What are their data priorities? Skills? • Research methodologies? Plans? • Equipment and instrumentation? Formats? • Where are the “pain points”? • How will you find out? Approaches to use? • How will you use the information?

  14. Exercise 1b: Motivation, benefits, risks • What are the RDM drivers and enablers for research staff and post-grad students? • RDM drivers and enablers for Libraries / IT / Computing Services / Information Services? • RDM drivers and enablers for the institution? • What are the barriers? What are the risks? • How will you articulate the benefits? • How will you find out? Approaches to use? • How will you use the information?

  15. Exercise 1c: Costs & sustainability • What are the costs associated with RDM? • For the researcher? • For the institution? • Direct / indirect costs? Fixed / variable costs? • What cost data already exists? • What time horizon are you considering? • How will you find out? Approaches to use? • How will you use the information?

  16. Group 1 Group 2 • What are the RDM drivers and enablers for research staff and post-grad students? • RDM drivers and enablers for Libraries / IT / Computing Services / Information Services? • RDM drivers and enablers for the institution? • What are the barriers? What are the risks? • How will you articulate the benefits? • How will you find out? Approaches to use? • How will you use the information? • What are the researchers’ datarequirements? • What datasets exist already? Standards? • What are their data priorities? Skills? • Research methodologies? Plans? • Equipment and instrumentation? Formats? • Where are the “pain points”? • How will you find out? Approaches to use? • How will you use the information? • What are the costs associated with RDM? • For the researcher? • For the institution? • Direct / indirect costs? Fixed / variable costs? • What cost data already exists? • What time horizon are you considering? • How will you find out? Approaches to use? • How will you use the information? Group 3

  17. Requirements gathering: Approaches and tools • Survey e.g. Oxford, Parse.Insight • Focus groups : semi-structured interviews • Case studies departmental / disciplinary • Joint R&D projects • Data champions in departments • Data Preservation readiness : AIDA tool • Data audit / assessment : DAF tool

  18. DAF Implementation Guide - 4-stage methodology • Lessons from pilot studies • Practical examples of questionnaires and interview frameworks • DAF online tool http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf

  19. Data Audit / Asset Framework pilots May-July 2008

  20. http://sudamih.oucs.ox.ac.uk/docs/Use%20of%20the%20DAF.pdf http://eprints.ucl.ac.uk/15053/1/15053.pdf

  21. Identifying risks • Data loss (institution, research group, individual) • Increased costs (lack of planning, service inefficency, data loss) • Legal compliance (research funder, H&S, ethics, FoI) • Reputation (institution, unit, individual)

  22. FoI help & guidance JISC FoI FAQ ISC FoI guide forthcoming

  23. Sustainability: Who owns? Who benefits? Who selects? Who preserves? Who pays?

  24. KRDS • Benefits taxonomy • Case studies

  25. KRDS Activity Model Benefits & Metrics Use Case 1 : National Crystallography Service Use Case 2 : Researcher in the lab • KRDS/I2S2 Project • Extending the Benefits Framework • Developing Value Chain and Impact Analysis tool • Applied to different domains • Toolkit now available http://beagrie.com/krds-i2s2.php

  26. KRDS Toolkit: • Benefits Framework Tool • Value Chain & Benefits Impact Worksheet • Worked examples

  27. Reviewing Data Support ServicesAnalysis, Assessment, Priorities

  28. Exercise 2: Analysis, Assessment, Priorities • Institutional stakeholders? • Data support services? • Range, scope, coverage? • Gaps? • Fitness for purpose? • Timeliness? • Resources? • Skills? • SWOT

  29. Strengths Weaknesses (Gaps) Opportunities Threats

  30. State-of-the-Art Report : Models & Tools (Alex Ball, June 2010) Data Lifecycles Data Policies (UK) incl DMP Standards & tools Data Asset Framework (DAF) DANS Seal of Approval Preservation metadata Archive management tools Cost / benefit tools

  31. CARDIO Tool collaboratively assess data management requirements, activity, and capacity at your institution build consensus between data creators, information managers and service providers identify practical goals for improvement in data management provision and support; identify operational inefficiencies and opportunities for cost saving; make a compelling case to senior managers for investment in data management support ….Currently under test

  32. Policy Summary from DCC http://www.dcc.ac.uk/resources/policy-and-legal

  33. Institutional Policy

  34. Institutional Policy Article in Int J Digital Curation 6(2) (2011)

  35. Data storage policy?

  36. Assessing cloud options • 3 JISC Reports in 2010 : • Technical Review • Cloud computing for research • Environmental & Organisational issues

  37. UMF Shared Services & Cloud Programme

  38. Policy

  39. Responding to policy drivers http://www.dcc.ac.uk/resources/how-guides/develop-data-plan

  40. Planning • Data types, formats, standards, capture • Ethics and Intellectual Property • Access, sharing and re-use • Short-term storage & data management • Deposit & long-term preservation • Adherence and review

  41. DMP Online http://www.dcc.ac.uk/dmponline

  42. Checklist for a Data Management Plan Checklist questions mapped to funder’s data requirements Slide : Martin Donnelly, DCC

  43. DMP Online v2.0 (V3 coming soon) • Cleaner interface • Funder-specific guidance • Versioning feature • CSV output http://www.dcc.ac.uk/dmponline Slide : Martin Donnelly, DCC

  44. DMPs next steps? • Embed DMPs in institutional research lifecycles as the norm • Code of Conduct for Research • Assess & review DMPs (not just the science content of proposals) • Educate reviewers (DCC guidance for social science in prep) • Manage compliance of researchers • Infrastructure to share DMPs • Integrate in institution research management information system

  45. Building Capacity & Capability

  46. Data challenges? • Data management plans • Appraisal: selection criteria • Data retention and handover • Data documentation: metadata, schema, semantics • Data formats: applying standards • Instrumentation: proprietary formats • Data provenance: authenticity • Data citation & versions: persistent IDs • Data validation and reproducibility • Data access: embargo policy • Data licensing • Data linking: text, images, software

  47. Exercise 3: Skills Audit • What skills do you have in house? • What are your strengths? Core data skills? • Gaps? Do these matter? • Can / should they be developed? • How? Resource implications? • Other sources of expertise? • Key partnerships? • Team science roles?

  48. Skills Audit • Be specific • Prioritise core skills

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