When Google DeepMind trained AlphaFold 2 to predict the three-dimensional structure of proteins—a breakthrough that earned the 2024 Nobel Prize in Chemistry—the system learned from thousands of datasets it didn't have to create from scratch. Those datasets came from EMBL's European Bioinformatics Institute, a quiet powerhouse in Hinxton, England, that has become as essential to modern life sciences as electricity grids are to cities. A new economic report by Frontier Economics reveals just how much scientists worldwide depend on this open data infrastructure: EMBL-EBI generates £11.8 billion in annual productivity gains across the global life sciences community.
The findings underscore a truth that's easy to overlook in an era of flashy AI breakthroughs—the unglamorous work of organizing, curating, and sharing data freely is what makes discovery possible at scale. EMBL-EBI doesn't conduct experiments or publish papers under its own name. Instead, it manages vast repositories of biological information that researchers, startups, and pharmaceutical companies access daily. This is the institute's third independent economic assessment since 2016, creating a unique decade-long record of how open data translates into real value for science and innovation.
The numbers tell a compelling story. Over 2,500 EMBL-EBI users across academia and industry participated in the survey, offering a genuinely global snapshot of who relies on these resources. The £11.8 billion in annual productivity gains come from a remarkably concrete source: researchers save an average of 11 hours per week by accessing EMBL-EBI's datasets instead of generating the same information themselves. Seventy-one percent of respondents said EMBL-EBI enables work that would otherwise be impossible or require substantial additional time and effort. That's not hype—that's the backbone of how modern biology operates.
The report also captured how the institute's role has evolved in the age of artificial intelligence. More than a third of survey respondents now build new tools and databases on top of EMBL-EBI data, extending the institute's value across different research disciplines. Forty-two percent explicitly stated that EMBL-EBI data contributes to AI and machine learning model development. The AlphaFold Database—containing over 200 million protein structure predictions that EMBL-EBI helped make openly available—illustrates this perfectly. By democratizing access to AlphaFold's predictions, the institute likely expanded the volume and diversity of research that could use these predictions, from drug discovery to understanding rare genetic diseases.
This is where the story becomes urgent. As Jo McEntyre, the Interim Director of EMBL-EBI, notes in the report, no single institute or country can manage biological data at today's scale. EMBL-EBI's infrastructure exists through global collaborations and joint funding—but only if those commitments remain stable and long-term. The economic case is clear: for every pound invested in curating and sharing biological data openly, the returns ripple across thousands of labs worldwide, accelerating everything from vaccine development to personalized medicine.
For readers curious about the state of global science, the message is hopeful. The infrastructure that powers breakthrough discoveries isn't locked behind paywalls or controlled by a single corporation. It's freely available, globally managed, and generating immense value for researchers everywhere. That foundation matters more than ever as we face challenges in medicine and biotechnology that require the brightest minds from every corner of the world to work together.