The Metascience Research Lab is asking big questions about the future of science and discovery
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UW-Madison Information School ยท College of Computing and Artificial Intelligence
Studying how science works, and how it can work better.
The Metascience Research Lab uses large-scale data, information networks, machine learning, and policy-aware analysis to understand scientific discovery, research careers, scholarly credit, biomedical translation, and knowledge transfer.
Founded in 2020
A home for science of science and metascience at the iSchool
MSRL is co-led by B. Ian Hutchins and Chaoqun Ni. We study complex scholarly, workforce, funding, biomedical, and communication data to understand how science is organized, evaluated, corrected, and translated into impact. The lab builds on work in scientific portfolio analysis, iCite and citation metrics, open citation data, research workforce studies, preprints and peer review, and equity in scientific careers and recognition. Our goal is to produce evidence that helps researchers, institutions, funders, and policymakers build more effective, equitable, and trustworthy systems for discovery.
What we study
We study the systems that shape science.
Our work follows how knowledge, credit, people, institutions, and technologies move through the research enterprise.
- How scientific knowledge is produced, evaluated, communicated, corrected, and translated into biomedical and technological impact.
- How careers, incentives, institutions, and inequities shape research participation and recognition.
- How AI, citation networks, open data infrastructure, and new evaluation tools are changing scientific discovery.
Latest
Recent work and coverage
Career effects of preprints get mixed reviews from biomedical researchers
Science interview
Selected publications updated with recent work from the team
Publication highlights
Join us
We welcome curious students who want to research research.
Prospective students interested in PhD or research opportunities are encouraged to contact the PIs with a short note about fit.
Good fits often bring
- Curiosity about how science works
- Experience with data, computation, or social science methods
- Interest in policy, equity, integrity, AI, or scholarly communication