For those in science policy, a clear lesson emerges from the way many governments have dealt with the COVID-19 pandemic: openness matters – in handling scientific data and in managing collaboration.
In the early stages, when most likely lives could have been saved in China and elsewhere by faster action, a more-open and transparent airing of scientific data and advice to government would have helped. In the current stage, as we race to find effective treatments and vaccines, open sharing of health data among researchers is crucial. And in the future, as we apply lessons from this nightmare, an open society – in which we collaborate internationally and trust scientific evidence – will be essential.
Or as Yuval Noah Harari, a well-known historian and philosopher put it earlier this month: “The real antidote to epidemic is not segregation, but rather cooperation.”
I have spent several years, first at the European Commission and now at VUB university in Brussels, advocating for open science. The importance of openness – sharing scientific results and data immediately and freely – has become orthodoxy in many governments around the world, in word if not always in deed. The EU has been a pioneer in this field, in 2018 formally launching a European Open Science Cloud initiative to federate data-sharing systems around the EU, and successfully urging other science funders around the world to sign up for “Plan S” to reform the once-closed scientific publishing industry.
In my view, the COVID-19 response so far highlights three lessons – for now and the future, in health research and beyond.
1. We need global, reliable and reusable research data – a.k.a. open science
A huge effort is going into pooling all public and private research data needed to fight the pandemic, around the world. In the news, we can all see how French, Swedish, British, American and other researchers are consulting each others’ COVID work – a truly global effort. Several open science initiatives have appeared, such as the Virus Outbreak Data Network, a public-private effort to make all possible COVID data, in whatever language or format, findable, accessible, interoperable and reusable (FAIR, in the language of open science proponents).