Using bibliometrics to evaluate outcomes and influence of translational biomedical research centers

Abstract

Introduction: Federal grant funding to support infrastructure development of translational biomedical research centers is a form of public health intervention. Establishing rigorous methods for measuring center success and outcomes is essential to justify continued funding. Methods: Bibliometric data compiled from a 5-year funding cycle of neurodegeneration and translational neuroscience research center were analyzed using the package bibliometrix for open-source software R and the NIH-developed research tool iCite. Results: The research team and their collaborators (n = 485) produced 157 grant-citing publications from 2015-2020. The science was produced by small research teams clustered around three main communities of topics: Alzheimer's Disease, brain imaging, and neuropsychological testing in the elderly. Using the relative citation ratio, the publications produced by the research team were found to be influential when compared to other R01-funded publications. Conclusion: Recent developments in bibliometric analysis expand beyond traditional measurement capabilities to better understand the characteristics, outcomes, and influences of research teams. These findings can be used to inform researchers and institutions about research team composition, productivity, and success. Measures of research influence may be used to justify return on investment to funders.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.