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Given the complexity of current health challenges and the enormous opportunity of 21st century biomedicine, diversity in all its forms — among research projects and the researchers themselves — invites innovation. Research is an intensely human endeavor that benefits from different points of view that are formed in part by racial/ethnic heritage, socioeconomic background, sexual/gender identity, physical ability, geographic location, and many other self-identifying features.

We know that diversity increases creativity, innovation, and productivity in the workforce. But we also know that the U.S. scientific workforce is not as diverse as it could be — especially at the level of faculty at the nation’s colleges and universities, where the vast amount of federally-funded biomedical research takes place. Enhancing diversity in the NIH-funded workforce is urgent, given shifting U.S. demographics and the need to draw insights from all corners of America. 

In 2014, NIH launched a landmark initiative to enhance U.S. scientific workforce diversity through a research-based lens. This effort, the Diversity Program Consortium, is using scientifically-driven approaches to understand which recruitment and retention approaches work, and in what context – to help colleges and universities attract and develop future scientists from diverse backgrounds. Another key component of the Diversity Program Consortium is the National Research Mentoring Network aimed at helping highly trained scientists from diverse backgrounds achieve career success. This highly integrated network helps connect junior and senior scientists across the nation through online tools. 

Data Science

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Due to major technology advancements in recent decades, including the Human Genome Project and its resulting data deluge, biomedical research is increasingly reliant on computers both for finding new knowledge and for understanding what it means. NIH is catalyzing this historic research opportunity to use “Big Data” by bringing different types of researchers together and encouraging rapid, open sharing of data and common standards among scientists working with it. One expectation is that data-science advances occurring at the intersection of computer science and biology will enable basic scientists to conduct more experiments using computer models alone. An important NIH focus in data science is developing and testing tools located “in the cloud,” instead of on individual computers, that are accessible to researchers regardless of location and resource availability. Already, NIH researchers have used data-science analyses to make discoveries about extremely complex conditions like diabetes and heart disease, as well as how to improve the development of new drugs for a range of disorders..

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