Following a successful start in 2017 EFSA has launched a second crowdsourcing challenge aimed at ‘people scientists’ and computer whizzes.
This second challenge aims to find ways to automate systematic reviews – the ‘data extraction’ step of scientific assessments.
The prize of US$28,000 (EUR 23,000) is targeted at software experts or amateur computer enthusiasts who can build information extraction algorithms to identify data elements from a full-text scientific article. The challenge was launched on 11 April 2018 – and runs for 90 days until 10 July.
The first challenge, held in 2017, asked for ideas about how to visualise scientific uncertainty – the limitations in available knowledge. Three participants based in Australia and the US won prizes ranging from US$1,000 to US$2,500 for their ideas.
EFSA’s technical staff are looking at how their ideas can be adapted to EFSA’s work and used to support its new approach to assessing scientific uncertainties.
Who can apply?
Anyone can apply after registering on the InnoCentive platform. It is quick and easy, requiring only five simple steps.
Why are the prizes in dollars?
‘Solvers’ registered on the InnoCentive platform come from across the world so the prizes are in US dollars.
How is EFSA involved in crowdsourcing?
In 2015 EFSA launched a project on ‘Crowdsourcing: Engaging communities effectively in food and feed risk assessment’ to explore the risks and opportunities in applying crowdsourcing and citizen science to collect and process data and increase the openness of EFSA.
Why is EFSA interested in crowdsourcing?
Several factors make crowdsourcing appealing: engaging the public and stakeholders in the process of scientific assessment, widening our evidence base, promoting data sharing and re-use of data, better use of expertise, encouraging public enthusiasm and support for science, and increasing transparency and trust in science.
Is EFSA part of other European crowdsourcing initiatives?
EFSA is part of Citizen Science Cost Action where we participate on activities related to scientific quality, standardisation and interoperability of data.
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