Rahimi M1, Joughin E1, Bruns Jr L1
1Australian Urban Research Infrastructure Network (AURIN), University Of Melbourne, Australia
Biography:
Masoud Rahimi is a Lead Data Scientist at the Australian Urban Research Infrastructure Network (AURIN), where his work comes down to two things: helping researchers get the data they need and do more with it, through data access, expert consultancy, and technical enablement. Masoud brings a PhD in Information Systems (Spatial) from the University of Melbourne and a genuine interest in what happens when AI meets real-world research problems. He spends most of his time making complex data accessible, legible and useful to researchers, and thinking about how research infrastructure can play a more proactive role in creating societal impact.
Abstract:
National research infrastructure organisations face a persistent challenge in demonstrating societal impact at scale. Yet the dominant response remains ad hoc: when funding bodies, boards, or government stakeholders require evidence of return on investment, organisations typically assemble data manually, draw on inconsistent sources, and produce outputs that cannot be reproduced, validated, or compared across reporting cycles. This approach is duplicated across institutions, prone to error, and places a significant burden on staff when credible evidence matters most. There is no shared, transparent, or scalable approach to tracking this lineage of impact.
This presentation describes how AURIN, the Australian Urban Research Infrastructure Network, and a core part of Australia's national research infrastructure, developed an AI-powered system that automatically measures ecosystem impact, including publications, data use, citations, policy uptake, media presence, grants, and knowledge transfer, replacing ad hoc data gathering with a reproducible pipeline.
The system uses a large language model to surface impact stories, analyse patterns across research records, identify under-explored topics or grants, and produce plain-language summaries that inform strategic positioning and demonstrate tangible return on taxpayer investment. This is AI in service of public accountability, and where it demonstrates its value beyond productivity hype.
The approach is not unique to AURIN. Any research infrastructure organisation required to showcase its impact without the resources to do so at scale faces the same problem. The presentation proposes a shared, sector-wide methodology that replaces today's patchwork of manual processes with something systematic, transparent, and reproducible.
