facing the data challenge: institutions, disciplines, services and risks
DESCRIPTION
Presentation given by Liz Lyon (UKOLN) at the 1st DCC Regional Roadshow, Bath, UK in November 2010.TRANSCRIPT
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A centre of expertise in digital information management
www.ukoln.ac.uk
UKOLN is supported by:
Facing the Data Challenge : Institutions, Disciplines, Services & Risks
Dr Liz Lyon, Director, UKOLN, University of Bath, UK
Associate Director, UK Digital Curation Centre
1st DCC Regional Roadshow, Bath November 2010
This work is licensed under a Creative Commons LicenceAttribution-ShareAlike 2.0
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Overview1. Facing the data challenge :
Requirements, Risks, Costs
2. Reviewing Data Support Services : Analysis, Assessment, Priorities
3. Building Capacity & Capability : Skills Audit
4. Developing a Strategic Plan : Actions and Timeframe
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Institutional Diversity
http://www.flickr.com/photos/mintchocicecream/7491707/
Facing the Data Challenge
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Case studies
Oxford, Cambridge, Edinburgh, Southampton
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Based on DCC Curation Lifecycle Model
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Disciplinary Diversity
eScience Case studies
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http://www.flickr.com/photos/30435752@N08/2892112112/
SCARP Case studies
• Atmospheric data
• Neuro-imaging
• Tele-health
• Architecture
• Mouse Atlas
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http://www.dcc.ac.uk/sites/default/files/documents/publications/SCARP%20SYNTHESIS.pdf
Recommendations:
• JISC• HE & Research funders• Publishers & Learned societies• HEIs and research institutions• Researchers & scholars
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http://www.data-archive.ac.uk/media/203597/datamanagement_socialsciences.pdf
http://opus.bath.ac.uk/20896/1/erim2rep100420mjd10.pdf
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• Quick & simple deposit• Software tools • Laboratory archive
• Crystallography community engaged• ‘Embargo’ facility• Structured foundations• Discoverable & harvestable
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Data Curation Profiles
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Exercise 1a: Gathering requirements
• What are the researchers’ data requirements? • 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?
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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?
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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?
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• 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
Requirements gathering: Approaches and tools
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Benefits:
Prioritisation of resources
Capacity development and planning
Efficiency savings – move data to more cost-effective storage
Manage risks associated with data loss
Realise value through improved access & re-use
Scale:
Departments, institutions
Dealing with Data Report : Rec 4
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• DAF Implementation Guide October 2009
• Collating lessons of pilot studies
• Practical examples of questionnaires and interview frameworks
• DAF online tool autumn 2010
http://www.data-audit.eu/docs/DAF_Implementation_Guide.pdf
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Methodology
http://www.data-audit.eu/DAF_Methodology.pdf
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Data Audit / Asset Framework pilots May-July 2008
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http://sudamih.oucs.ox.ac.uk/docs/Use%20of%20the%20DAF.pdf
http://eprints.ucl.ac.uk/15053/1/15053.pdf
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Some lessons learned….
“CeRch had four false starts before finding a willing audit partner”“Pick your moment” ….“Timing is key” (avoid exams, field trips,
Boards…)
Plan well in advance!“Be prepared to badger senior management”
Little documentation/knowledge of what exists:“a nightmare”
Defining the scope and granularity is crucial
Collect as much information as possible in interviews/surveys
Variable openness of staff and their data
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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)
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http://foiresearchdata.jiscpress.org/
Freedom of Information FAQ (Draft)
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Sustainability:Who owns?Who benefits?Who selects?Who preserves?Who pays?
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Dimension 1
Direct Indirect (costs avoided)
Dimension 2
Near-term Long-term
Dimension 3
Private Public
Benefits Taxonomy: Summary
Keeping Research Data Safe2 Report: April 2010
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KRDS• Which costs?• Effect over time?• Benefits taxonomy• Repository models• Case studies• Key cost variables• Recommendations
• User Guide, business templates forthcoming 2010
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Reviewing Data Support ServicesAnalysis, Assessment, Priorities
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1. Scale, Complexity, Predictive Potential
2. Continuum of Openness3. Citizen Science4. Credentials, Incentives,
Rewards5. Institutional Readiness &
Response6. Data Informatics Capacity &
Capability
http://www.ukoln.ac.uk/ukoln/staff/e.j.lyon/publications.html#november-2009
•Open Science at Web-Scale
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A centre of expertise in digital information management
www.ukoln.ac.uk
1. Leadership 2. Policy 3. Planning
4. Audit
6. Repositories & Quality assurance
8. Access & Re-use
10. Community building
9. Training & skills
Data Informatics
Top 10
5. Engagement7. Sustainability
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Exercise 2: Analysis, Assessment, Priorities
• Institutional stakeholders?• Data support services?• Range, scope, coverage?• Gaps?• Fitness for purpose?• Timeliness?• Resources?• Skills?
• SWOT
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Strengths Weaknesses (Gaps)
ThreatsOpportunities
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Digital Preservation Policies Study
High-level pointers and guidance
Outline policy model/framework
Mappings to institutional strategies
ExemplarsReport October 2008
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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
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Jeff Haywood, RDMF V October 2010 http://www.dcc.ac.uk/sites/default/files/documents/RDMF/RDMF5/Haywood.pdf
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Jeff Haywood, RDMF V October 2010 http://www.dcc.ac.uk/sites/default/files/documents/RDMF/RDMF5/Haywood.pdf
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Jeff Haywood, RDMF V October 2010 http://www.dcc.ac.uk/sites/default/files/documents/RDMF/RDMF5/Haywood.pdf
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Assessing cloud options
3 JISC Reports in 2010 :• Technical Review
• Cloud computing for research
• Environmental & Organisational issues
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• North Carolina universities
• Cyber-infrastructure project
• Data cloud across three campuses
• “regional”
• Policy & practice
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Policy
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• 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
Planning Dealing with Data Report : Rec 9
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http://www.dcc.ac.uk/dmponline
DMP OnlineCurrently updating Version 1.0
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Checklist questions mapped to funder’s data requirements
Checklist for a Data Management Plan
Slide : Martin Donnelly, DCC
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DMP Online v2.0 (coming soon)
• Cleaner interface• Funder-specific guidance• Versioning feature• CSV output
Slide : Martin Donnelly, DCC
http://www.dcc.ac.uk/dmponline
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DMPs next steps?
• Embed DMPs in funder policies & 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
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Building a University Data registry…
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Building Capacity & Capability
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Data challenges?1. Data management plans2. Appraisal: selection criteria3. Data retention and handover4. Data documentation: metadata,
schema, semantics5. Data formats: applying standards6. Instrumentation: proprietary formats 7. Data provenance: authenticity8. Data citation & versions: persistent IDs 9. Data validation and reproducibility10. Data access: embargo policy11. Data licensing12. Data linking: text, images, software
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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?
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Skills AuditSkill Source / Gap Comment
• Be specific• Prioritise core skills
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Data Access & Re-use
“Community Criteria for Interoperability” (Scaling Up Report 2008)
• Domain data format standard: CIF• Domain data validation standard: CheckCIF• Metadata schema: eCrystals Application Profile
http://www.ukoln.ac.uk/projects/ebank-uk/schemas/
• Crystallography Data Commons: TIDCC Data Model in development
• Domain identifier: International Chemical Identifier • Citation & linking: DOI
http://dx.doi.org/10.1594/ecrystals.chem.soton.ac.uk/145
• Embargo & Rights http://ecrystals.chem.soton.ac.uk/rights.html
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Data Licensing
• Bespoke licences• Standard licences• Multiple licensing• Licence mechanisms
• Forthcoming 2010
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What to keep?
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Repositories
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Quality AssuranceTrust
Standards
Audit and certification tools
• TRAC
• DRAMBORA
• PLATTER
• NESTOR
• DANS Data Seal of Approval
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Sustainability
PREMIS Data Dictionary
OAIS
• Representation Information
• Registry/Repository RRORI
http://www.loc.gov/standards/premis/
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Data citation
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Training
• Consortial• Institutional• Departmental• Laboratory• Project• Library• Computing Services• Research staff / postdocs• Postgraduate students
“excellent : probably the best course I have been on since starting my role as an Informatics Liaison Officer”
Research Data Management Forum http://www.dcc.ac.uk/data-forum/
1) Roles & Responsibilities 2) Value & Benefits
3) Sensitive Data: Ethics, Security, Trust 4) Economics of Applying & Sustaining
digital curation
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• Online resources• Includes training for• Data handling• Software• SPSS, NVIVO
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• Live arts• Department of Drama• Researcher-practitioner focus
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Embedding data informatics education...faculty & LIS...
Doctoral Training Centres
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Developing a Strategic Plan
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Optimising organisational support
• Organisational structures• Library / IT / IS / research support structure• Where does data management fit?• Leadership?• Co-ordination?
• Roles : data librarian, data manager, research support officer, data scientist, data curator...
• New roles?
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New data support structures
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Exercise 4: Actions and Timeframe
• Vision and Objectives: Are they clear?• Organisational structures: Fit for purpose?• Library / IT / IS structure : Is it optimal?• Roles : who is best placed to take action?• Responsibility : for each service / activity?• Priorities : what will you stop doing?• Resources : Do you need to bid for funding?• Partnerships : Who do you need to talk to?• Plan: What? Who? How? When?
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Actions and TimeframeShort-term0-12 months
Medium-term12-36 months
Long-term>3 years
• Identify quick wins• What can you do tomorrow?
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Take homes1. Understand the research data
requirements of your campus / institutional consumers
2. Agree research data service delivery priorities
3. Define data roles and responsibilities
4. Collaborate and strengthen the data support provided
5. Be pro-active! Engage! Be part of team science!
http://www.pnl.gov/science/images/highlights/computing/biopilotlg.jpg
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Chicago Mart Plaza, 6-8 December 2010