Hutchins Lab

A member of the UW-Madison Metascience Research Lab

We seek avenues to improve the research enterprise and accelerate biomedical research advances using quantitative analysis of information networks and machine learning. Prof. Hutchins previously worked as a data scientist at NIH, where he developed the iCite bibliometrics dashboard, along with many of its quantitative metrics and the NIH Open Citation Collection database. He also spearheaded the development of the NIH COVID-19 Portfolio to track and disseminate cutting-edge COVID-19 research in real time.

Selected Publications

iCite profile

Davis MT et al. “Prediction of transformative breakthroughs in biomedical research.” bioRxiv 2025. doi: 10.64898/2025.12.16.694385

Arabi S et al. “Most researchers would receive more recognition if assessed by article level metrics than by journal level metrics.” PLOS Biology 2025. doi: 10.1371/journal.pbio.3003532

Zheng X et al. “Comparing the outputs of intramural and extramural grants funded by National Institutes of Health.” eLife 2025. doi: 10.7554/eLife.108929.1

Ni C and Hutchins BI. “Framework for assessing the risk to a field from fraudulent researchers: A case study of Alzheimer’s disease.” JASIST 2025. doi: 10.1002/asi.25009

Hoppe TA et al. “Predicting substantive biomedical citations without full text.” PNAS 2023. doi: 10.1073/pnas.2213697120

Nelson L et al. “Robustness of evidence reported in preprints during peer review.” Lancet Glob Health 2022. doi: 10.1016/S2214-109X(22)00368-0

Hutchins BI. “A tipping point for open citation data.” Quantitative Science Studies 2021. doi: 10.1162/qss_c_00138

Hutchins BI et al. “Predicting translational progress in biomedical research.” PLoS Biology 2019. doi: 10.1371/journal.pbio.3000416

Hoppe TA et al. “Topic choice contributes to the lower rate of NIH awards to African-American/black scientists.” Science Advances 2019. doi: 10.1126/sciadv.aaw7238

Hutchins BI et al. “The NIH Open Citation Collection: A public access, broad coverage resource.” PLoS Biology 2019. doi: 10.1371/journal.pbio.3000385

Hutchins BI et al. “Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level.” PLoS Biology 2016. doi: 10.1371/journal.pbio.1002541