UKOLN is supported by:

1. Monica Duke m.duke@ukoln.ac.uk 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:

2. 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

3. Citation in the domain of disease network modelling Funded: August 2010 – July 2011

4. SageCite project overview • Review of data citation (issues, technology) • Understanding the domain • Sage Bionetworks partners in project • Site visit • Documenting processes (workflow tools)

5. SageCite project overview • Demonstrator • Adding support for data citation • Using DataCite services • Working with publishers • Benefits analysis: KRDS Taxonomy

6. 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

7. 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.

9. Additional steps for citing data

10. Slide by Jonathan Derry Sage Bionetworks

11. Slide by Lara Mangravite Sage Bionetworks

12. 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

13. 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.

14. 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.

15. 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. 

16. Slide by Lara Mangravite Sage Bionetworks

17. 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.

18. 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.

19. 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

20. 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

21. 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

22. 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

23. Acknowledgements • UKOLN • Liz Lyon • Monica Duke • Nature Genetics • Myles Axton • PLoS Comp Bio • Phil Bourne • University of Manchester • Carole Goble • Peter Li • British Library • Max Wilkinson • Tom Pollard • Sage Bionetworks