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The impact of scientific research extends far beyond the laboratory. Breakthroughs like insulin and innovations like the internet have transformed our everyday lives. They also influence policymaking and the overall economy. To make the most of every discovery, we must understand how science and its funding ripple through society, but how do we trace that impact?

Researchers from Northwestern University have developed , an AI-based visual tool that can help researchers, funders, and even the public see the bigger picture of how science funding shapes impact.

Using data spanning 22 years (2000–2021), the team created a vast, interconnected dataset linking 7 million research grants to their downstream outcomes—ranging from direct outputs, such as papers, patents, and clinical trials, to broader societal impacts, including policy influence and media coverage. The findings are published on the arXiv pre-print server.

It is well known that breakthroughs don't happen in isolation: They rely on funding and public support. In 1945, Vannevar Bush, an American innovator and science administrator, argued that funding is fundamental and science is essential to ensure that real-world innovations for tomorrow can be drawn from it.

Billions of public dollars are invested in scientific research each year, yet for a long time, there was no systematic way to understand how these investments benefit society beyond academia.

Traditional funding assessments primarily focus on academic publications and citations within the scientific community, overlooking broader ripple effects such as shaping policy, enhancing public health, or influencing culture. The lack of a solid framework to trace the impact might lead to funding decisions and science policies that do not align with the social needs, leading to the public losing faith in science.

This study introduced FtF, an interactive visual framework that traces funding from initial grants to real-world applications, mapping how investments translate into downstream impacts.

They integrated four major datasets (Dimensions, Overton, Altmetric, and SciSciNet) to connect 7 million research grants to 140 million publications, 160 million patents, 10.9 million policy documents, 800,000 clinical trials, and 5.8 million news feeds, all connected through 1.8 billion citation linkages.

They designed ImpactGlyph, a tool within FtF, to visualize and compare the multidimensional impact of different grant topics. By making these connections visible, FtF provided the transparency essential for sustaining public trust and interest in scientific research.

The researchers also designed a predictive component that uses machine learning to forecast the likely future impact of recent grants that can help funders identify which research topics and researchers would be a better fit for their investment.

To test the effectiveness of this system, the team interviewed experts and applied FtF to case studies. They found that FtF enabled the successful translation of complex science funding data into accessible visualizations for technical and non-technical stakeholders.

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