Post on 11-Nov-2014
description
Developing a Community Capability Model Framework for data-intensive
research
Liz Lyon, Alex Ball, Monica Duke and Michael DayUKOLN, University of Bath
m.day@ukoln.ac.uk
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UKOLN is supported by:
m.day@ukoln.ac.uk
iPres 2012, Toronto, Canada, 1-5 October 2012
Presentation outline
• Contexts– Data-intensive research– Capability models
• Community Capability Model Framework– Project approach
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– Project approach– Brief outline of the main capability factors and
characteristics
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
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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
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
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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
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
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– 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)
Capability models (2)
• CCM 5-levels applied to research data management:– Australian National Data Service – Research Capability
Maturity Guide• Covered:
– Institutional policies and procedures
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– Institutional policies and procedures– IT infrastructure– Support services– Managing metadata
– Crowston and Qin• Applied the levels to data management within
research projects
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)
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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.
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/
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– 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)
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
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– Sometimes these recognise that characteristics will be points on a continuum, e.g.
– http://communitymodel.sharepoint.com/
Collaboration within the discipline/sector
Loneresearchers.
Departmentalresearch groups.
Collaboration across research groups within orbetween organisations.
Discipline organised at anational level.
International collaborationand consortia.
CCMF
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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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– Collaboration with the public
2. Skills and training
• Characteristics:– Skill sets– Pervasion of training
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3. Openness
• Characteristics:– Openness in the course of research– Openness in published literature– Openness of data– Openness of methodologies / workflows
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– Openness of methodologies / workflows– Re-use of existing data
4. Technical infrastructure
• Characteristics:– Computational tools and algorithms– Tool support for data capture and processing– Data storage– Support for curation and preservation
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– Support for curation and preservation– Data discovery and access– Integration and collaboration platforms– Visualisations and representations– Platforms for citizen science
5. Common practices
• Characteristics:– Data formats– Data collection methods– Processing workflows– Data packaging and transfer protocols
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– Data packaging and transfer protocols– Data description– Vocabularies, semantics, ontologies– Data identifiers– Stable, documented APIs
6. Economic and business models
• Characteristics:– Funding models– Public-private partnerships
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7. Legal and ethical issues
• Characteristics:– Legal and regulatory frameworks– Management of ethical constraints and norms
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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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– Quality and validation frameworks
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
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• 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
Thank you!
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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:
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• 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/