A research funder backs a technology she believes is rising. A company commits to a promising R&D direction. A region builds its innovation strategy around sectors that look strategically important. By the time these bets are evaluated, the field may have moved. The signals that would have flagged the shift were often there all along, but too scattered to read in time.

The problem is rarely a lack of data. It is the difficulty of turning fragmented information into timely, contextual intelligence. As technological change accelerates, the ability to read the research and innovation landscape at scale is becoming a prerequisite for effective policy and strategy, not a luxury.

The signal is in the connections

Any single source answers only part of the question. A publication shows what research is happening. A patent shows that someone is trying to protect an invention. Funding data shows where investment is flowing. Company and policy sources show where applications and institutional attention are forming.
A technology that appears in publications, patents, company activity, funding flows and policy priorities tells a far stronger story than any single source can. It may indicate scientific momentum, commercial interest, application potential or an ecosystem beginning to take shape.

But reading sources together depends on something very practical: recognising that the author of a paper, the inventor on a patent and the company behind a product can be the same actor, even when every source names it differently. That problem has to be solved from a trustworthy starting point: the research record itself.

This is where the OpenAIRE Graph becomes critical for OPIX, an Athens-based consultancy that builds Big Data and AI solutions for research and innovation strategy and policy.

The OpenAIRE Graph: the research foundation

Innovation begins in research. Before an idea becomes a technology or a product, it takes shape as findings, methods and data, shared through publications and scholarly collaboration. The OpenAIRE Graph is one of the most comprehensive open maps of the global research record.

It brings together more than 218 million publications, over 100 million research data items and hundreds of thousands of software outputs, alongside the projects, funders, organisations and researchers behind them; over 350 million research products in all. These are drawn from around 2,200 direct sources and many more through trusted aggregators, then connected into one structure that captures not only outputs but the relationships between them: who produced what, what funded it and how it connects to everything else.

What makes it a foundation rather than a pile of records is quality. The data is deduplicated, cleaned and disambiguated, then enriched: full text is mined from around 70 million publications, and outputs are classified by Fields of Science and by Sustainable Development Goals. It is updated monthly, openly licensed and built on transparent, open-source methods.

Because OPIX uses the Graph as its core research source, this quality directly shapes the strength of the intelligence layer built on top of it.

OPIX: the intelligence layer

OPIX builds on the OpenAIRE Graph by adding the rest of the innovation signal: 95 million patents, 300,000 company websites per sector, ESG data, regulatory and policy sources, and other sector-specific datasets. In total, the OPIX platform already holds more than 500 million records, with the OpenAIRE Graph as its core research source.

Its role is to carry the structure of the research record into the wider innovation landscape. The Graph helps establish who produced what, who funded it and how research outputs relate to one another. OPIX extends that logic across patents, company websites, policy sources and sector datasets, so technologies, applications, organisations and key actors can be followed beyond the scholarly record.

The result is not just a larger dataset, but configurable intelligence: signals that show where topics and technologies are emerging, how they connect to applications, which actors and collaborations matter, where early commercialisation may be forming, and how regions or sectors are specialising.

What this enables

The same evidence base answers different questions, depending on who is asking.

Where to invest. OPIX shows which technology areas are gaining real scientific traction, reading publication momentum, patent activity and funding flows together, so funders, agencies and investors can commit before a trend becomes obvious.

What to track. It surfaces emerging technologies and applications early, giving corporate R&D and foresight teams the lead time to respond strategically rather than reactively.

Who is driving a field. It maps the ecosystem around a technology, the companies, institutions, startups and researchers, their collaborations and their relative strengths, so it is clear who matters and where.

Whether public funding is working. Because the OpenAIRE Graph links funding and projects to the outputs they generate, OPIX can show whether a programme is producing the outputs and downstream impact it was meant to.

How regions compare. It benchmarks regional innovation ecosystems against global peers on a shared evidence base, useful input for smart specialisation strategies and Horizon Europe programme design.

Evidence you can act on

A signal is only useful if you can stand behind it. When OPIX flags an emerging technology or a shifting field, the claim is not a figure to take on trust. It can be traced back to its sources, and those sources can be found, opened and read within a rich, connected research record. Evidence has to be both traceable and retrievable, and a foundation like the OpenAIRE Graph is what makes this possible.

This is also the direction European research and innovation policy is taking, from the European Open Science Cloud to Responsible Research Assessment, where open, connected evidence is becoming central to how research is assessed, funded and governed. For the funder, the company or the region deciding where to commit, this is what changes: not more data, but signals they can read in time, trust in context and stand behind when they act.