1. Code of Conduct for the Collection, Analysis and Sharing of Health Related Research Data in Developing Countries Elizabeth Pisani Consultant, Wellcome Trust Bamako 2008 Code of Conduct for Sharing of Health Related Data Moving into the OpenEpi age

2. Were data sharing dinosaurs. Whats our excuse? Incentives stacked against sharing data Few data management and analytic skills Concerns about the ethics of sharing data Technical hurdles seem daunting

3. Incentives not to share data Publish or perish –Grant applications, hiring and promotion Extra work –Translation, management, user support Misuse of data –Loss of credibility and trust Getting rumbled –Data quality issues, protocol violations Loss of income

4. On the other hand... Funders want data to be more accessible… (…recognise that it costs money, and will pay?) Public data get more attention… …which attracts more brains and money… …and ups the ante for quality data Researchers get more support for data management, documentation archiving… …leads to greater data discovery and use …which adds value to data New tools benefit everyone

5. Code of conduct: Incentives Data sharing record considered in awarding grants and jobs Grants depend on strong data sharing plans Citation standards and indices developed Require registration of public-health research Require data archiving for journal publication Promote open source/ copyleft norms Support ombudsman to oversee fair use

6. Do we have what it takes? Capacity and costs Universally, dearth of data management skills… …and no career path In South, few trained analysts… …few analysts being trained… …and not much of a career path Data migration is not the solution, but who picks up the tab for capacity building? Costs of data sharing extend beyond project Bang for buck: sharing low quality data?

7. Code of conduct: capacity Invest in data management skills and careers Invest in increasing analysis skills, including better rewards for teaching/mentoring Require teamwork on analysis Fund curation infrastructure Fund full documentation of data to ensure accessibility at end of project

8. Is it ethical to share data? Disclosure of individual identity Stigmatisation of small communities Stretching of original informed consent Allowing participants to withdraw from a studies On the other hand… Ethical imperative to maximise use of data Saving taxpayers money Protecting people from research fatigue Leading to more sensible investments in health

9. Code of conduct: Ethics Assert ethical imperatives for data sharing IRB reviews should require and review plans to maximise data use Support development of anonymisation techniques Encourage broader informed consent procedures Develop standards for appropriate data access

10. Code: Levels of access Maximum access that protects privacy, rewards primary researchers, delivers health benefits Time-limited exclusive access in most cases. Then: 1.Fully open access 2.Controlled public access 3.Collaborative access among scientists 4.Exclusive access for primary investigators

11. What are the technical hurdles? Meta-data standards (extending DDI) Anonymisation methods for longitudinal data DOI standards and data tracking Policing access Open source software (Cleaning up the past)

12. Technical issues: code of conduct proposals Commit to DDI standards for metadata in future data collection activities Support development of open source tools for data collection, management, analysis. Make machine-readable meta data available regardless of access rules for micro-data Develop a database of public health metadata

13. A call to action on data sharing The draft Call to Action supports: Capacity development for data synthesis and use Development of codes of conduct to ensure accountability transparency and access to health data and the results of research The collection of reliable health data and the maximization of free and unrestricted availability of such data in the public domain

14. If everyone adopts the code: Incentives will change: good for researchers Skills will increase: good for researchers and good for science Duplication will be reduced and money will be saved: good for funders and taxpayers Learning will accelerate so services can improve faster and people can led healthier, happier lives: good for everyone