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National Institutes of Health (NIH) is the world largest public funder of biomedical research, investing more than $30 billion dollars to achieve its mission to enhance health, lengthen life, and reduce illness and disability. Here, by leveraging individual‐level characteristics and contextual/time‐dependent features of professional scholarly network, we investigate the chance of NIH Mentored K (MK) to NIH R01 grant (independent research grant) or equivalent (R01‐Eq) transition success. The aim of this work is to explore the relationship between investigator productivity (i.e., scholarly publication) and success (e.g, R01‐Eq funding) during MK to R01‐Eq transition using publicly available datasets and applying our machine learning techniques. The preliminary experiment based on PubMed data and NIH awardees database show that the proposed method is promising, and a number of interesting funding success factors can be located by utilizing statistical tools.

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