1. Application of the Benefits Analysis Tools for MRC population health studies Professor Dipak Kalra Centre for Health Informatics and Multiprofessional Education (CHIME) University College London d.kalra@ucl.ac.uk Professor Dipak Kalra Centre for Health Informatics and Multiprofessional Education (CHIME) University College London d.kalra@ucl.ac.uk

2. Background: MRC Data Support Service (2009-11) Funded by MRC to better understand the different approaches to data sharing across its population health studies identify drivers identify approaches identify barriers provide support to studies where needed contribute to a review of MRC policy and to future policy guidance from MRC 6 exemplar studies were investigated in detail, working closely with study Directors, PIs and data managers Todays review of the benefits tools draws on all six, and from less detailed review of other population health studies (a kind of virtual study based on aggregated and anonymised insights)

3. MRC study data sharing context (Not like a central data archive) Each MRC study: defines the overall research mission might have other funding parties e.g. CRUK, international undertakes the data collection has to maintain cohort relationships for ongoing collection is responsible for maintaining confidentiality designs the data schema documents some metadata, but the depth needed for shared use is often complex undertakes most of the data cleaning and derivations analyses and publishes its own research on the data shares voluntarily, as a complement to its core mission The wide range of study types and their maturity means the opportunities and benefits will vary between studies

4. MRC style data sharing: arises through direct (person, telephone, email, web form) contact sometimes includes a funding contribution for some studies almost never sometimes for data cleaning + support may entail a significant explanatory and support effort as well as data extraction and provision sometimes builds academic partnerships may result in co-authorship may result in future joint grant applications

5. Example benefits: direct Funding (e.g. from MRC or data sharing collaborator) to support data sharing costs New areas of research enabled through a data sharing collaboration Enriched quality and scope of grant applications, greater chance of funding and/or a larger award Grant income received as a co-applicant alongside a data sharing partner Co-authorship of papers with external researchers

6. Example benefits: indirect Inputs to the design of study instruments and repositories Use of standard data definitions and metadata Collateral support: personnel effort from data sharing partners who contribute to the core curation, cleansing and/or derivation tasks of the study Secondary analyses from combined data sets Additional derived or cleaned variables provided back by a data sharing partner

7. Stimulating new networks and collaborations Localised expression: Enriched quality and scope of grant applications, greater chance of funding and/or a larger award Action: Form collaborations with other research groups for joint applications for funding and/or share resources from individual grant awards KRDS Outcome type: Direct benefit, in 2-4 years Stakeholders: Internal: PIs, academics; External: grant funders Quantitative benefit: Larger scale of research funding through larger grant or pooling of grant incomes Qualitative benefit: Increased visibility and kudos Weighting: 3

8. Re-purposing and re-use of data Localised expression: Lower costs of data collection and curation, faster route to usable data Action: Agree shared use of pre-existing data held by another study to avoid new data collection KRDS Outcome type: Indirect benefit, in 1 year Stakeholders: Internal: PIs, academics, data managers Quantitative benefit: Cost saving, shorter time to publications Qualitative benefit: ~ Weighting: 4

9. Conclusions The Benefits Framework and the Impact Tool can accommodate the kinds of benefit from good data curation practice and from data sharing in MRC population health studies Detailing the active steps to realise each benefit, when the result might be realised and who benefits seem to be useful ingredients for putting forward a case for funding or for prioritising resource utilisation with a study Whilst initial population might be done by one person, completing the spreadsheet and working out weightings might be nicely undertaken in a team workshop