Drivers and Barriers to Digital Curation and Preservation

1

• Drivers and Barriersto Digital Curation and
  Preservation
• Where are we now
• Where do we want to be in 10 years
• How do we get there
• what are the research questions (2008-2014)

2
Emerging Themes

• Incentives for Researcher Engagement
• Disciplinary Differences
• Business Cases
• Access and Rights Framework for IP to support
  Digital Preservation over the full lifecycle
• Good and Bad case studies
• Modernisation of the metadata (lifecycle and
  tools)
• Workforce Capacity Building

3
Emerging Themes

• Incentives for Researcher Engagement
• Disciplinary Differences
• Business Cases
• Access and Rights Framework for IP to support
  Digital Preservation over the full lifecycle
• Good and Bad case studies
• Modernisation of the metadata (lifecycle and
  tools)
• Workforce Capacity Building

4
Working groups for this morning

• A - Incentives for Researcher Engagement and
  Workforce Capacity Building
• Christine Borgman, Martin Lewis, Charlotte
  Capener, Allison McDonald, Paul Jeffreys
• B - (breaking down) Disciplinary Differences
• Micheal Jubb, Kevin Schurer, Martin Waller,
  Michael Bright
• C - Business Cases, Rights and Responsibilities
• Chris Rusbridge, Charlotte Waelde, Laurie Hunter,
  Luigi Fusco, Carlos Olivera, Robert Sharpe
• D - Modernisation of the metadata (lifecycle,
  tools and scale)
• Keith Jeffery, Murray Weston, Andrew Wilson,
  Simon Tanner
• Where are we now
• Where do we want to be in 10 years (2014)
• How do we get there what are the research
  questions (2008-2014)

5
Incentives for Researcher Engagement

• Where are we now?
• Lack of participation by academic researchers
• Get funding to engage academics
• Confused
• How do we engage the research community and make
  digital preservation vital?
• Data citations are recognised in very few areas
• Low level awareness of needs of digital
  preservation
• No link between incentives and work force
  capacity explicitly
• The reward system for researchers does not match
  the incentives for digital preservation
• If anything, the opposite..

6
Incentives for Researcher Engagement

• Where do we want to be in 10 years?
• Widespread appreciation of digital preservation
• Must make digital preservation key to academic
  life
• Where data citations are mainstream
• Generating excitement of new type of science
  which can be done with the new combinations of
  data
• Address who does the preservation
• Address basic skills needed
• Want to reconcile the reward system and digital
  preservation, so that data is capital in the
  research enterprise
• Digital preservation must be embedded in the life
  of a researcher, and in fabric

7
Incentives for Researcher Engagement

• How do we get there what are research qs?
• Exemplar projects, incentivize other
  organisations to participate
• Really good example to catch hearts and minds
• Medical?
• Accredited community resources (PDB) with cachet
  of adding your data
• Gap analysis
• Map out generic processes in digital preservation
• Identify those which are discipline specific
• Need two-pronged approach
• Much more understanding of researcher incentives
• Look for ways to reward system (RAE, hiring, )
• Need to change some of pedagogy in academic
  fields
• Innovative data programmes which focus on data
• Generic research training
• Cadre of preservation of professional who
  understand data
• Training of existing and new workforce

8
Disciplinary Differences

• Now
• There is real heterogeneity
• Deposit arrangements and requirements
• Preservation arrangements
• Data and information use
• Observational vs experimental data external
  sources
• Generational, organisational and intitutional
• Research process and methods
• Workforce skills

9
Disciplinary Differences

• 10 Year goals
• Cultural shift in attitude to data sharing
• Interdisciplinarity
• Facilitating/promoting interactions between
  disciplines
• Developed Infrastructure
• Physical
• Interoperability
• Roles and responsibililties
• Focus on re-access and re-use
• Common framework of policies and procedures

10
Disciplinary Differences

• How
• Cultural Change
• Driven by key leaders and resource providers
• Promoting Interaction
• Issue driven research programmes
• Infrastructure
• Repository development
• National and international partnerships
• Preservation an usage layers
• Roles and Responsibilities
• Roadmap
• Common Framework of Policies and Procedures
• Workforce development

11
Business cases, rights and responsibilities

• Now
• Cost
• Increasing understanding
• Risk
• Lots from other areas to be applied
• Value
• Not well explored at all

12
Business cases, rights and responsibilities

• Where we want to be
• Well understood applied
• Business cases
• Rights
• responsibilities
• Good practice guidance widely available
• Applicable to different sectors
• Real preservation structures available
• deployed and used
• Legal change needs understood
• and advocated

13
Business cases, rights and responsibilities

• How to get there?
• Business case and legal research
• Using rich case studies
• Cost, risk, benefits
• Wide stakeholder engagement
• Range of scenarios
• Modelling
• Use case studies to extend to new areas and
  opportunities
• 4 approaches
• Funder engagement
• Top down (management, policy)
• Bottom up (researcher etc)
• Practical deployment

14
MODERNISATION OF METADATA

• DP making persistent
• DC characterise in context
• 2 Levels Discovery level, Domain-specific level

15
MODERNISATION OF METADATA

• Where We Are Now
• Metadata standards discovery level
• Container, some content little context
• Machine readable not machine understandable
• Metadata standards domain-specific level
• Machine readable, maybe machine-understandable
• Domain ontologies starting
• Process
• Some staged metadata collection (creation,
  ingest, update, use)
• Little clarity on responsibility / authority
• Little integration
• Value
• No idea how to value metadata

16
MODERNISATION OF METADATA

• Where We Want To Be
• Metadata standards discovery level
• Container, content and context (semantic
  web/RDF/logic)
• Machine understandable and processable
• Metadata standards domain-specific level
• Machine understandable (deep semantics)
• Domain ontologies populated and linked
• Process
• Staged metadata collection (creation, ingest,
  update, use) supported with easy-to-use
  interface
• Automation of metadata creation and update
• Clear responsibility / authority
• Integrated and crosswalked
• Value
• Understand and quantify value of metadata for
  curation and value of particular fields of
  metadata

17
Where We Want To Be

• Research Issues
• Discovery Metadata
• Machine understandable container, content,
  context
• Supported by general domain ontologies
• Domain-specific metadata
• Machine understandable container, content,
  context
• Supported by specific (but linked) domain
  ontologies
• Process
• Definition of process steps, availability of
  metadata information, IT support systems
• Automated metadata extraction
• Responsibility / authority models and
  implementations
• Integration methodologies
• Value
• Models for value estimation and value proving