Genome projects, and in fact, a number of modern multidisciplinary projects involve people who in the main are not used to thinking about appropriate data sharing models. The opportunity to squander the effort of generating and analyzing the data so that only one group has access, or only one group gets to publish the sexy bits without really giving the data generators or contributors fair accreditation or a chance to perform the analyses as part of the team is a common problem. There needs to be progress above and beyond piecemeal efforts or closed collaborations who are not in a position to export their own successful policies.
The bioinformatics team might be the most concerned. Although listened to, bioinformaticians tend not to be treated as the last word on data sharing policy – often an MD in charge of putting together the ethical approval, or putting together the collaboration, is the one who sets the policy – and often that’s a showstopper that needs diplomatic triage to bring the project to success.
Welcome then the recent publication in Genome Medicine (disclaimer, I am a member of the editorial board) by Knoppers et al “Towards a data sharing Code of Conduct for international genomic research “. The groups in this publication have thought up a logical set of principles specific to the context of collaborative international genomics research. Frankly these can be applied locally – and therein lies the opportunity .
The groups wisely position their approach to propose seven different principles and a preliminary international data sharing Code of Conduct as a call for comment – setting the stage for an opportunity to make progress in this gnarly area.
Equitable: any approach to the sharing of data should recognize and balance the needs of researchers who generate and use data, other analysts who might want to reuse those data, and communities and funders who expect health benefits to arise from research.
Ethical: all data sharing should protect the privacy of individuals and the dignity of communities, while simultaneously respecting the imperative to improve public health through the most productive use of data.
Efficient: any approach to data sharing should improve the quality and value of research and increase its contribution to improving public health. Approaches should be proportionate and build on existing practice and reduce unnecessary duplication and competition.’
key points are:
1. Quality – key issue: Harmonization of data collection and archiving methods and tools ensures validation of scientific quality.
SOPs, curation and security to promote “Harmonization of deposit, access procedures and use promotes accessibility, equity and transparency”.
Responsible governance should be shared between funders, generators and users of data with encouragement of interoperability
Trust and the promotion of data sharing rely on data management and security mechanisms
Key policies on publications, intellectual property, and industry involvement should be public and website is important for public and peer review
Inter-agency co-operation and funding
Goes without saying…