By looking to the past we may be able to better understand the flow of scientific knowledge going forward, and possibly even predict translational research outcomes. In their October PLOS Biology paper, Drs. Ian Hutchins and George Santangelo from the NIH’s Office of Portfolio Analysis devised a machine-learning strategy that taps into the trajectory of science by tracking knowledge flow from bench to bedside.
My colleagues within the NIH Office of Portfolio Analysis sought to answer this call. Drs. Ian Hutchins and George Santangelo embarked on a hefty bibliometric endeavor over the past several years to curate biomedical citation data. They aggregated over 420 million citation links from sources like Medline, PubMed Central, Entrez, CrossRef, and other unrestricted, open-access datasets. With this information in hand, we can now take a better glimpse into relationships between basic and applied research, into how a researchers’ works are cited, and into ways to make large-scale analyses of citation metrics easier and free.