Article by Stefaan Verhulst: “The world has become more complex, more dynamic and more interconnected than ever before. The challenges we face – from health to climate, from democratic resilience to economic transformation – are deeply intertwined. And we need new ideas to meet these challenges.
Europe has never lacked intellectual ambition, but ideas alone aren’t enough. To make real progress, we need breakthrough discoveries. We need evidence of what works. And we need the institutional capacity to test, validate and scale solutions across borders and disciplines.
That’s where science comes in. Yet good science depends on data. And if we want AI to supercharge discovery and transform science, then data becomes even more important.
The ‘datafication’ of society
Digitalisation has led to an unprecedented datafication of society. When citizens engage with government services, visit a doctor, use a mobility platform, shop online or measure their steps and/or sleep through wearable devices, data are generated.
But this datafication doesn’t stop with individual behaviour. It extends deep into the productive fabric of our economies. Manufacturing systems, industrial supply chains, logistics networks, energy grids and robotic production lines are now embedded with sensors, connected devices and intelligent control systems. The implication is profound – data is no longer a by-product of digital services alone. It’s a structural feature of both our digital and physical infrastructures.
The remarkable feature of digital data isn’t merely its volume. It’s its reusability. When done responsibly, data created for one purpose can often be reused for entirely different objectives – including scientific research.
But there’s a fundamental constraint: access. Much of today’s most valuable data remains locked away in institutional stovepipes – within government agencies, universities and private companies. Despite its public value potential, it often remains inaccessible to scientists and public interest actors.
Europe has taken important steps to address this data asymmetry. Open data policies have expanded transparency. The Data Governance Act and the Data Act seek to facilitate data sharing and rebalance power in data markets. Article 40 of the Digital Services Act creates pathways for vetted researchers to access platform data. The European Open Science Cloud seeks to enable the sharing of scientific data. Sectoral data spaces – including those envisioned under the European Health Data Space – and Data Labs aim to provide structured, interoperable infrastructures for data access and use.
Yet instead of a steady expansion of access, we’re now witnessing a ‘data winter.’ Access to private sector data for research has declined in several domains. Open government data initiatives have slowed or been rolled back. Scientific datasets have become restricted or have disappeared. Open science has struggled to scale beyond pilot projects. And broader political retrenchment risks weakening some of the very infrastructures designed to enable responsible reuse.
Generative AI’s rapid expansion has also triggered backlash. Large-scale data scraping for AI training has blurred the line between openness and extraction. Consequently, institutions and content creators have become more protective, sometimes closing access altogether. And without reliable access to diverse, high-quality data, scientific progress risks stagnation.
What should Europe do? Three priorities stand out.
Access shouldn’t be only supply-driven
For too long, data policy has focused on releasing datasets without clearly articulating the questions they’re meant to answer. But the value of data – and increasingly the value of AI – depends directly on the value of the question.
In short, better questions define better discovery.
If we want to unlock meaningful access, we must invest in what might be called ‘question science’ – the systematic identification of high-priority societal questions; the structuring of those questions so they are researchable and actionable; the mapping of those questions to existing or potential data sources; and embedding them into funding frameworks, governance mandates, and institutional strategies.
When demand is vague, access debates remain abstract. When questions are clear, access becomes purposeful. Researchers, policymakers and data holders can align around concrete objectives. This requires structured, participatory processes that bring scientists, communities, funders and regulators together to define and prioritise the questions that matter most. ..(More)”.
