Dataset Information

Size and characteristics of the biomedical research workforce associated with U.S. National Institutes of Health extramural grants.

ABSTRACT: The U.S. National Institutes of Health (NIH) annually invests approximately $22 billion in biomedical research through its extramural grant programs. Since fiscal year (FY) 2010, all persons involved in research during the previous project year have been required to be listed on the annual grant progress report. These new data have enabled the production of the first-ever census of the NIH-funded extramural research workforce. Data were extracted from All Personnel Reports submitted for NIH grants funded in FY 2009, including position title, months of effort, academic degrees obtained, and personal identifiers. Data were de-duplicated to determine a unique person count. Person-years of effort (PYE) on NIH grants were computed. In FY 2009, NIH funded 50,885 grant projects, which created 313,049 full- and part-time positions spanning all job functions involved in biomedical research. These positions were staffed by 247,457 people at 2,604 institutions. These persons devoted 121,465 PYE to NIH grant-supported research. Research project grants each supported 6 full- or part-time positions, on average. Over 20% of positions were occupied by postdoctoral researchers and graduate and undergraduate students. These baseline data were used to project workforce estimates for FYs 2010-2014 and will serve as a foundation for future research.

SUBMITTER: Pool LR

PROVIDER: S-EPMC4750411 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Size and characteristics of the biomedical research workforce associated with U.S. National Institutes of Health extramural grants.

Pool Lindsay R LR Wagner Robin M RM Scott Lindsey L LL RoyChowdhury Deepshikha D Berhane Rediet R Wu Charles C Pearson Katrina K Sutton Jennifer A JA Schaffer Walter T WT

FASEB journal : official publication of the Federation of American Societies for Experimental Biology 20151201 3

The U.S. National Institutes of Health (NIH) annually invests approximately $22 billion in biomedical research through its extramural grant programs. Since fiscal year (FY) 2010, all persons involved in research during the previous project year have been required to be listed on the annual grant progress report. These new data have enabled the production of the first-ever census of the NIH-funded extramural research workforce. Data were extracted from All Personnel Reports submitted for NIH gran ...[more]

PMID: 26625903

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Project description:ImportanceDiversity in the biomedical research workforce is essential for addressing complex health problems. Female investigators and investigators from underrepresented ethnic and racial groups generate novel, impactful, and innovative research, yet they are significantly underrepresented among National Institutes of Health (NIH) investigators.ObjectiveTo examine the gender, ethnic, and racial distribution of super NIH investigators who received 3 or more concurrent NIH grants.Design, setting, and participantsThis cross-sectional study included a national cohort of NIH-funded principal investigators (PIs) from the NIH Information for Management, Planning, Analysis, and Coordination (IMPAC II) database from 1991 to 2020.ExposuresSelf-identified gender, race and ethnicity, annual number of NIH grant receipt, career stage, and highest degree.Main outcomes and measuresDistribution of investigators receiving 3 or more research project grants, referred to as super principal investigators (SPIs), by gender, race, and ethnicity.ResultsAmong 33 896 investigators in fiscal year 2020, 7478 (22.01%) identified as Asian, 623 (1.8%) as Black, 1624 (4.8%) as Hispanic, and 22 107 (65.2%) as White; 21 936 (61.7%) identified as men; and 8695 (35.3%) were early-stage investigators. Between 1991 and 2020, the proportion of SPIs increased 3-fold from 704 (3.7%) to 3942 (11.3%). However, SPI status was unequal across gender, ethnic, and racial groups. Women and Black PIs were significantly underrepresented among SPIs, even after adjusting for career stage and degree, and were 34% and 40% less likely than their male and White colleagues, respectively, to be an SPI. Black women PIs were the least likely to be represented among SPIs and were 71% less likely to attain SPI status than White men PIs (adjusted odds ratio, 0.29; 95% CI, 0.21-0.41).Conclusions and relevanceIn this cross-sectional study of a national cohort of NIH-funded investigators, the gender, ethnic, and racial gaps in receipt of multiple research project grants among NIH investigators was clearly apparent and warrants further investigation and interventions.

Project description:ImportanceDespite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice.ObjectiveTo examine what types of medical AI have the greatest estimated translational impact (ie, ability to lead to development that has measurable value for human health) potential.Design, setting, and participantsIn this cohort study, research grants related to AI awarded between January 1, 1985, and December 31, 2020, were identified from a National Institutes of Health (NIH) award database. The text content for each award was entered into a Natural Language Processing (NLP) clustering algorithm. An NIH database was also used to extract citation data, including the number of citations and approximate potential to translate (APT) score for published articles associated with the granted awards to create proxies for translatability.ExposuresUnsupervised assignment of AI-related research awards to application topics using NLP.Main outcomes and measuresAnnualized citations per $1 million funding (ACOF) and average APT score for award-associated articles, grouped by application topic. The APT score is a machine-learning based metric created by the NIH Office of Portfolio Analysis that quantifies the likelihood of future citation by a clinical article.ResultsA total of 16 629 NIH awards related to AI were included in the analysis, and 75 applications of AI were identified. Total annual funding for AI grew from $17.4 million in 1985 to $1.43 billion in 2020. By average APT, interpersonal communication technologies (0.488; 95% CI, 0.472-0.504) and population genetics (0.463; 95% CI, 0.453-0.472) had the highest translatability; environmental health (ACOF, 1038) and applications focused on the electronic health record (ACOF, 489) also had high translatability. The category of applications related to biochemical analysis was found to have low translatability by both metrics (average APT, 0.393; 95% CI, 0.388-0.398; ACOF, 246).Conclusions and relevanceBased on this study's findings, data on grants from the NIH can apparently be used to identify and characterize medical applications of AI to understand changes in academic productivity, funding support, and potential for translational impact. This method may be extended to characterize other research domains.

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Dataset Information

Size and characteristics of the biomedical research workforce associated with U.S. National Institutes of Health extramural grants.

Publications

Size and characteristics of the biomedical research workforce associated with U.S. National Institutes of Health extramural grants.

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