Shiyuan Wang

Researcher Spotlight

Shiyuan is a PhD candidate in Environmental Engineering and Science at the University of Illinois Urbana-Champaign. Her research focuses on the links between global economic activities, air pollution, and public health. She uses multi-regional input-output (MRIO) modeling, atmospheric modeling, and geospatial analysis to quantify how emissions from production and trade contribute to PM₂.₅ exposure and related health impacts across regions. Her recent work examines how international trade redistributes air pollution and associated mortality burdens, with the goal of supporting more transparent, data-driven decision-making in environmental policy and sustainable development.

What do you see as the primary benefit to you of archiving your data in a repository? What influenced your decision to deposit your data in the Illinois Data Bank?

Honestly, I’d love for more people to see and use my work. Also, more citations are always welcome. But more importantly, sharing the data helps make the research more transparent and useful to others. Since my work involves large-scale modeling, making the data available allows others to explore it from different angles, build on it, or even find things I didn’t notice myself. It’s a way to extend the life and impact of the research beyond the paper. This dataset represents an effort to make complex modeling results more accessible. I hope it can be useful not just for researchers, but also for people interested in understanding how global economic activities are connected to air pollution and health outcomes. Even small steps toward making data more open and usable can make a difference.

As a UIUC student, I naturally felt connected to the Illinois Data Bank as a platform. Beyond that, the Research Data Service was incredibly helpful throughout the process. They carefully went through my dataset with me, pointed out things I hadn’t noticed, and helped improve the overall clarity and documentation. It made the whole process much smoother and gave me confidence that the dataset is well-prepared for others to use.