Transcript Presentation

Monica Duke
[email protected]
Project Manager, SageCite Project
http://blogs.ukoln.ac.uk/sagecite/
#sagecite
JISC Digital Preservation Benefits Tools
Project Dissemination workshop
Tuesday 12th July 2011, London South Bank University
UKOLN is supported by:
www.ukoln.ac.uk
A centre of expertise in digital information management
Overview
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What is the SageCite project
What is Sage Bionetworks
Specifics of this case study
Outcomes of applying the tool
Next steps
What we’ve learnt
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Citation in the domain of disease network modelling
Funded: August 2010 – July 2011
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SageCite project overview
• Review of data citation (issues,
technology)
• Understanding the domain
– Sage Bionetworks partners in project
– Site visit
– Documenting processes (workflow tools)
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SageCite project overview
• Demonstrator
– Adding support for data citation
– Using DataCite services
• Working with publishers
• Benefits analysis: KRDS Taxonomy
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Sage Bionetworks overview
• US-based non-profit organisation
• Creating a resource for communitybased, data-intensive biological
discovery
• Community-based analysis is
required to build accurate models
• www.sagebase.org
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Sage data and processes
Data
curation
Statistical
QC
Genomic
analysis
Network
construction
Network
analysis
Data mining
Validation
• The idealised Sage modelling process can be divided
into 7 stages
• A combination of phenotypic, genetic, and expression
data are processed to determine a list of genes
associated with diseases
• Different people are responsible for different stages of
the modelling process. One person oversees the
whole process.
www.ukoln.ac.uk
A centre of expertise in digital information management
Data
curation
Statistical
QC
Genomic
analysis
Network
construction
Network
analysis
www.ukoln.ac.uk
A centre of expertise in digital information management
Data mining
Validation
Additional steps for
citing data
Slide by Jonathan Derry Sage Bionetworks
Slide by Lara Mangravite Sage Bionetworks
Case Study summary
• Case Study undertaken by a project
• Based on an organisation whose
main business/expertise is science
• Immature stage of addressing digital
asset management
• Citation focus for benefits analysis
• Earlier version of the Benefits Tools
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Benefits of Data Citation (Direct)
• Better discovery of network models
– citation makes the model explicit and creates a
link between the model and parameters on
which discovery services can be based e.g.
contributor names help in building a service
which can find all models linked to a specific
researcher.
• Better access
– a citation can provide information and
mechanisms to locate and retrieve network
models.
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Benefits of Citation (Indirect)
• Increasing trust and reproducibility of
research
• Research assessment metrics
• Assessment is more equitable
• Improved career development path
• The public has more trust and belief in the
work of scientists
• Enabling more inclusive research metrics
– improves the range of metrics that are
considered.
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Benefits of citation (Near Term)
• In the short term, more of the people in the
value chain producing the models benefit if
all types of contributions are attributed
(more equitable attribution)
• Machine readibility
• Recognition for contributors as early
pioneers in data contributions
• Journal articles are able to provide more of
the evidence supporting the article.
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Slide by Lara Mangravite Sage Bionetworks
Benefits of citation (Longer Term)
• Wider interdisciplinary work
– the concept of interdisciplinarity will grow but that is a
longer term benefit
• Scholarly record enriched for future generations
– better able to understand development of methods and
data over time (how we got here) because of a stronger
evidence base.
• Longer-term track record and reputation of
contributors grows over time.
• Cumulative metrics can be computed and
different metrics can be devised.
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Benefits (Internal: project)
• Funders (JISC) citation of data in one domain helps to
inform future programs and transfer of lessons to other
domains.
• Policy makers: informs policy on what metrics to include in
their assessments.
• Sage bionetwork scientists and network team: larger
range of measures for assigning credit for contributions
becomes possible.
• Datacite/BL: a complex case study to inform technical
development; Sage Bionetworks: for improving their
infrastructure
• Nature/PLoS (publishers): papers can be validated;
strengthens the peer-review process; a stronger evidence
base supports the article.
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Benefits (External)
• Society: better disease treatments in the longer
term
• Funders (e.g. Wellcome Trust) : enhanced ROI
cascaded research funding
• Other scientists: able to create metamodels
• Increased public trust in science
– public: benefits because of diminished bad feeling about
science
– science: benefits from better public support for funding?
• Other publishers: have a model to follow
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Next steps
• Validate the analysis with the domain
experts (ongoing)
• Update the analysis using the new
versions of the tools
• Further (mediated) work on Impact
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What we have learnt
• The benefits framework was easy to
apply and helped articulate benefits
• An intermediary may be required to
facilitate the process
• Digital Management background and
motivation matters
• Terminology matters
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In summary…..
• We have tested the Benefits
Framework in one domain against
one aspect of curation (citation)
• We have seen positive changes to the
tools and their documentation
• More work needed on ability of
researchers to use the tools directly
– Validate outcomes of analysis
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Acknowledgements
• University of
Manchester
– Carole Goble
– Peter Li
• British Library
– Max Wilkinson
– Tom Pollard
• Sage Bionetworks
• UKOLN
– Liz Lyon
– Monica Duke
• Nature Genetics
– Myles Axton
• PLoS Comp Bio
– Phil Bourne
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