Developing a Community Capability Model Framework for data-intensive research
1. Developing a Community Capability
Model Framework for data-intensive
research
Liz Lyon, Alex Ball, Monica Duke and Michael Day
UKOLN, University of Bath
m.day@ukoln.ac.uk
iPres 2012, Toronto, Canada, 1-5 October 2012
UKOLN is supported by:
www.ukoln.ac.uk
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2. Presentation outline
• Contexts
– Data-intensive research
– Capability models
• Community Capability Model Framework
– Project approach
– Brief outline of the main capability factors and
characteristics
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3. Data-intensive research (1)
• Jim Gray’s “Fourth Paradigm”
• Difficult to define, but (broadly speaking) involves:
– Research involving large amounts of data
– Data is combined from multiple sources, across multiple
disciplines
– Data requiring significant processing (computational
analysis)
• Becoming increasingly embedded in research practice
– Integral for many ‘big science’ disciplines
– Now influencing “long-tail sciences,” the humanities and
social sciences
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4. Data-intensive research (2)
• Existing data infrastructures are not always sufficient to
deal with ever growing amounts of data
– Tools lack integration and are difficult to disseminate and
maintain due to lack of resources
• Need for a framework to analyse the capacity of
communities (e.g. disciplines or institutions) to deal with
data-intensive research
– A framework that could help institutions, research funding
bodies and researchers:
• Profile current readiness
• Indicate priority areas for investment or innovation
• Support forward planning
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5. Capability models (1)
• Extensively used by industry to help identify key
business competencies and activities
– An evaluation tool
• Capability Maturity Model for Software (CMM)
– Developed by Carnegie Mellon University Software
Engineering Institute
– Five levels of maturity:
• Initial (ad hoc or chaotic) – Repeatable (some
discipline) – Defined (documented and standardised)
– Managed (measurement and control) – Optimizing
(continuous improvement and innovation)
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6. Capability models (2)
• CCM 5-levels applied to research data management:
– Australian National Data Service – Research Capability
Maturity Guide
• Covered:
– Institutional policies and procedures
– IT infrastructure
– Support services
– Managing metadata
– Crowston and Qin
• Applied the levels to data management within
research projects
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7. Capability models (3)
• Cornell Maturity Levels for digital preservation
– 5-stage model
• Acknowledge – Act – Consolidate – Institutionalise –
Externalise
– Applied to three dimensions: Organisation, Technology,
Resources (the three-legged stool)
• UK AIDA project
– Used the Cornell model to develop a scorecard for
benchmarking digital asset management
• Digital Curation Centre CARDIO
– Web-based tool to support self assessment of research data
management within institutions, departments, projects, etc.
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8. CCMF outline (1)
• Community Capability Model for Data-Intensive
Research (CCMDIR) project
– Collaboration of UKOLN with Microsoft Research
– Project running 2011-2012
– http://communitymodel.sharepoint.com/
• Process
– Mini-case studies based on interviews with key
stakeholder groups (funding bodies, HEIs, PIs)
– 5 consultative workshops (UK, USA, Sweden, Australia)
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9. CCMF outline (2)
• Community Capability Model Framework
– Defined eight capability factors covering human,
technical and environmental aspects
– Within each factor, CCMF identifies characteristics that
could be used to help judge community capability
– Sometimes these recognise that characteristics will be
points on a continuum, e.g.
Collaboration within the discipline/sector
Lone Departmental Collaboration across research groups within or Discipline organised at a International collaboration
researchers. research groups. between organisations. national level. and consortia.
– http://communitymodel.sharepoint.com/
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11. 1. Collaboration
• Characteristics:
– Collaboration within discipline / sector
– Collaboration and interaction across disciplines
– Collaboration and interaction across sectors
– Collaboration with the public
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12. 2. Skills and training
• Characteristics:
– Skill sets
– Pervasion of training
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13. 3. Openness
• Characteristics:
– Openness in the course of research
– Openness in published literature
– Openness of data
– Openness of methodologies / workflows
– Re-use of existing data
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14. 4. Technical infrastructure
• Characteristics:
– Computational tools and algorithms
– Tool support for data capture and processing
– Data storage
– Support for curation and preservation
– Data discovery and access
– Integration and collaboration platforms
– Visualisations and representations
– Platforms for citizen science
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15. 5. Common practices
• Characteristics:
– Data formats
– Data collection methods
– Processing workflows
– Data packaging and transfer protocols
– Data description
– Vocabularies, semantics, ontologies
– Data identifiers
– Stable, documented APIs
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16. 6. Economic and business models
• Characteristics:
– Funding models
– Public-private partnerships
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17. 7. Legal and ethical issues
• Characteristics:
– Legal and regulatory frameworks
– Management of ethical constraints and norms
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18. 8. Academic issues
• Characteristics:
– Productivity and return on investment
– Entrepreneurship, innovation and risk
– Reward models for researchers
– Quality and validation frameworks
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19. Conclusions
• CCMF is a tool for:
– Evaluating a community’s current readiness to perform
data-intensive research
– Identifying where changes could be made to increase
capability, both human and technological
• Next steps:
– Case studies to help validate the framework
– Explore further the role of customised versions of the
framework for different stakeholders (funding bodies,
research institutions, researchers)
– Consider role WRT other available tools
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21. Acknowledgments
• The Digital Curation Centre (DCC) is a world-leading centre
of expertise in digital information curation with a focus on
building capacity, capability and skills for research data
management across the UK's higher education research
community. The DCC is funded by JISC.
• More information is available from:
http://www.dcc.ac.uk/
• UKOLN receives support from JISC and the University of
Bath, where it is based.
• More information is available from:
http://www.ukoln.ac.uk/
www.ukoln.ac.uk
A centre of expertise in digital information management