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UKOLN is supported by:
Monica Duke [email protected]
Project Manager, SageCite Project
http://blogs.ukoln.ac.uk/sagecite/
#sagecite
JISC Digital Preservation Benefits Tools Project Dissemination workshopTuesday 12th July 2011, London South Bank University
A centre of expertise in digital information management
www.ukoln.ac.uk
Overview
• 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
A centre of expertise in digital information management
www.ukoln.ac.uk
Citation in the domain of disease network modellingFunded: August 2010 – July 2011
A centre of expertise in digital information management
www.ukoln.ac.uk
SageCite project overview
• Review of data citation (issues, technology)
• Understanding the domain– Sage Bionetworks partners in project– Site visit– Documenting processes (workflow tools)
A centre of expertise in digital information management
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SageCite project overview
• Demonstrator– Adding support for data citation– Using DataCite services
• Working with publishers
• Benefits analysis: KRDS Taxonomy
A centre of expertise in digital information management
www.ukoln.ac.uk
Sage Bionetworks overview
• US-based non-profit organisation
• Creating a resource for community-based, data-intensive biological discovery
• Community-based analysis is required to build accurate models
• www.sagebase.org
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Sage data and processes
• 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.
A centre of expertise in digital information management
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Additional steps forciting data
Slide by Jonathan Derry Sage Bionetworks
Slide by Lara Mangravite Sage Bionetworks
A centre of expertise in digital information management
www.ukoln.ac.uk
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.
A centre of expertise in digital information management
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Slide by Lara Mangravite Sage Bionetworks
A centre of expertise in digital information management
www.ukoln.ac.uk
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
A centre of expertise in digital information management
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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
A centre of expertise in digital information management
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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
A centre of expertise in digital information management
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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