Springer Nature's challenge
Challenge: How can we help researchers get more from their experimental research data, through faster, easier routes of discovery, organisation or sharing of data?
Open science should be about opening up all areas of research. Open access to research data can help speed the pace of discovery and deliver more value for funded research by enabling reuse and reducing duplication. The evidence is there that open data and good data management makes research studies more productive, more likely to be cited and unlocks innovation for the good of society including unexpected new discoveries and economic benefit.
The world’s funders are increasingly mandating good data practice, including data management plans and data sharing, and recognising the need for global collaboration on infrastructure and best practice. Across the research community, momentum is gathering in policy, strategy and working groups to achieve a future where research data are widely Findable, Accessible, Interoperable and Reusable (FAIR). Yet in 2017 only about half of research data were shared (according to surveys of researchers) and a much smaller proportion were shared openly or in ways that maximise discoverability and reuse.
Even when research data are shared, they aren’t necessarily easy to find, search, visualise or use.
We know quite a lot about what’s stopping researchers from sharing: fundamentally, lack of time and lack of knowledge about how to organise their data and where to share it. Researchers are intelligent, responsible, motivated people. They are also time-poor, and do not necessarily want to become data or licensing experts. So they need clear information, simple policies and advice.. They also understandably prioritise advancing their field, their own research, and building their careers. So they need tools and faster easier ways to share data that fit with their workflow, and credit and incentives to make good research data practice and open data worthwhile.
When it comes to using data, researchers also face challenges. Discovering data sets in the first place through search, drawing connections between them, serendipitous insights.