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Initial bibliometric investigation of NIH mentored K to R transition
Author(s) -
Zhang Li,
Yan Xiaoran,
Mabry Patricia L.,
Martinson Brian C.,
Valente Thomas W.,
Lu Wei,
Liu Xiaozhong
Publication year - 2020
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.310
Subject(s) - productivity , work (physics) , computer science , gerontology , psychology , medicine , engineering , economic growth , economics , mechanical engineering
Abstract 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.