Sharing research data: expectations of research funders Nature Publishing Group meeting 14 November 2014 Dave Carr Wellcome Trust

Sharing research outputs: the Wellcome Trust position support research with the vision of achieving extraordinary improvements in human & animal health want the outputs of research we support to be accessed and used in a way that maximises benefits to health & society long-standing policies on open access publishing and data management and sharing

Making research data more readily available holds the potential to… Enable validity and reproducibility of research findings to be assessed Increase the visibility and use of research findings Enable research outputs to be used to answer new questions Reduce duplication and waste Enable access to other key communities - public, policymakers, healthcare professionals, etc

Data sharing – a growing consensus major challenges associated with increasingly vast & complex datasets, but also tremendous opportunities policy convergence between major funders in promoting sharing of research data  expectation that data outputs be preserved and shared in a way that maximises value  requirement for data management plans as integral part of the application process increasingly clear expectation that data underlying published research findings should be open

The Wellcome Trust’s experience Data management and sharing policy published 2007 (updated in 2010): - maximise access to research data with as few restrictions as possible - requires data management & sharing plan, where research likely to generate data of value as a resource for the community - commit to review and support costs of plans as integral part of the grant Reviews to date have suggested: - quality of DMPs variable, but evidence that they are improving - not clear that resource implications of plans being adequately considered or provisioned - post-award tracking remains a challenge

What we look for in a data management and sharing plan Concise answers to seven questions: i.What data outputs will the research generate and which have value to other researchers? ii.When will you share the data? iii.Where will you make the data available? iv.How will other researchers be able to access the data? v.Are any limits to data sharing required? vi.How will you ensure that key datasets are preserved to ensure their long-term value? vii.What resources will you require to deliver your plan?

Funder policies: similarities & differences Much consistency in approach, but some exceptions (especially EPSRC) Some variation in when data management plans required Most have similar challenges in implementing & monitoring policies Some have dedicated repositories (ESRC, NERC) All struggling with wider challenge of building resources and culture to support data sharing

Building an enabling environment there are significant barriers & constraints to overcome:  Infrastructural issues  Cultural issues  Technical issues  Professional issues  Ethical issues different disciplines at very different stages; different types of data raise distinct issues challenges will require funders to work in partnership, with each other & other key communities

Working in partnership – some of our current initiatives Expert Advisory Group on Data Access – advice on emerging issues relating to data access across genetics, epidemiology and social sciences Clinical Trial Data – funded programme of work with IOM and seeking to build an international consortia to support access to trial data Public Heath Research Data Forum – global cross funder initiative to increase access to research data generated by public health and epidemiology research

Incentives and Culture Change Report by Expert Advisory Group on Data Access (May 2014) Research culture and environment not perceived to provide sufficient support or rewards for data sharing Key recommendations focused on: - strengthening processes for review and tracking of data management plans; - providing more explicit recognition for high quality data outputs in key assessment processes (especially REF) - recognising and building career paths for data managers - enhancing key data resources

Issues for early-career researchers a risk that the burden of data sharing can fall disproportionately on postdoctoral researchers a key need for group leaders to support careers of all members of teams and clarity on roles participation in consortia in data intensive fields can create challenges enthusiasm for the concept of explicit recognition for data outputs, but low awareness, and a feeling publications still dominant metric

Some other emerging issues & questions Extension of policies for open access and data to other research outputs (especially software & materials) What role can/should journals play in ‘open data’ landscape? What are the roles of funders & journals in enforcing good practice How do we address key gaps in the data infrastructure? Which aspects should funders seek to take a lead, and where should we look to the community to lead?

Closing thoughts… Funders are committed to working with the community to maximise the value of research data – requirements for demonstrated good practice only likely to increase Researchers should actively consider how to: – plan approaches for data sharing as an integral part of planning research (and consider associated costs) – adopt good practice in ensuring datasets of value are discoverable, useable and accessible – recognise the value of data outputs - including through adoption of emerging systems for data citation It’s messy – practice is still emerging You aren’t alone – share experience and work together Bottom line: it isn’t easy, but it is worth it

Any Questions?