NIDA REI: Academic Research Enhancement Award (AREA) Training a Diverse Data Science Workforce for Addiction Research (R15 Clinical Trial Not Allowed)
Funding Agency:
- National Institutes of Health
The NIH is continuing to make a special effort to stimulate research at educational institutions that provide baccalaureate and/or advanced degrees for a significant number of the Nation's research scientists, but that have not been major recipients of NIH support. Since Fiscal Year (FY) 1985, Congressional appropriations for the NIH have included funds for this initiative, which NIH has implemented through the Academic Research Enhancement Award (AREA) program. This funding opportunity announcement aims to support AREA grants to undergraduate-focused institutions that do not receive substantial funding from the NIH, in order to provide data science for drug addiction research experiences for undergraduate students and enhance the data science for addiction research environment at these institutions. AREA funds are intended to support new and renewal data science for addiction research projects proposed by faculty members of eligible institutions.
The main objective of this NOFO is to train a diverse data science workforce for drug addiction research through the following : (1) provide support for meritorious data science for drug addiction research at undergraduate-focused institutions or institutional components; (2) strengthen the data science for drug addiction research environment at these institutions/components; and (3) give undergraduate students an opportunity to gain significant data science for drug addiction research experience through active involvement in the research. For the purpose of this announcement, an undergraduate-focused institution/component is one in which the undergraduate enrollment is greater than the graduate enrollment. Research projects must focus on leveraging the potential of artificial intelligence and machine learning (AI/ML) to accelerate the pace of biomedical and socio-behavioral innovation in drug addiction research and training a diverse data science workforce in drug addiction research field.
The AREA program will enable qualified scientists to receive support for small-scale data science for drug addiction research projects. It is anticipated that investigators supported under the AREA program will benefit from the opportunity to conduct independent data science for addiction research; that the grantee institution will benefit from a data science for addiction research environment strengthened through AREA grants; and that students at recipient institutions will benefit from exposure to and participation in scientific research in the field of data science for addiction science so that they consider careers in this area. This AREA NOFO emphasizes the engagement and inclusion of undergraduates in research.
The research project must involve undergraduate students, and the research team must be composed primarily of undergraduate students. Student involvement in research may include participation in the design of experiments and controls, collection and analysis of data, execution and troubleshooting of experiments, presenting at meetings, drafting journal articles, collaborative interactions, participation in lab meetings to discuss results and future experiments, etc. The AREA program is a research grant program, not a training or fellowship program, and, as such, applications should not include training plans such as didactic training or non-research activities relating to professional development. Masters and doctoral candidates may be supported on these research projects, but their inclusion should be carefully considered. In all cases, the majority of students conducting research through the award must be undergraduates. Since diversity strengthens the research environment, AREA projects are encouraged to include students from diverse backgrounds, including those from groups underrepresented in the biomedical research workforce (See NOT-OD-20-031). This NOFO does not provide for support of research from Health Professional Schools as defined in Section III.1 regardless of student composition.
An AREA application submitted to this NOFO may include other investigators, such as technicians, collaborators or consultants, or other individuals such as high school students, post baccalaureate participants, graduate students, or postdoctoral fellows. However, involvement of such individuals does not fulfill the goal to engage undergraduate students in eligible environments to research. Due to the multi-disciplinary nature of data science research, the establishment of mutually beneficial partnerships to enhance the participation of researchers from diverse backgrounds and communities experiencing health disparities is expected.
The NIH has recognized the importance of increasing the capacity of experts in computation, data science, and related fields to move into the biomedical and socio-behavioral research space in the NIH Strategic Plan for Data Science. There are currently multiple strategies to enhance the data science workforce at NIH, expand the national research workforce, and engage a broader community in the biomedical and clinical research fields. Rapid increase in the volume of data generated through Electronic Health Records (EHR) and other biomedical and socio-behavioral research presents exciting opportunities for developing data science approaches for biomedical research and improving healthcare. However, there are several challenges that hinder more widespread use of AI/ML technologies, including cost, capability for widespread application, and access to appropriate infrastructure, resources, and training. Additionally, there is a need for a diverse workforce to identify and mitigate the risk of creating and perpetuating harmful biases in its practice, algorithms, and outcomes inadvertently cultivating health disparities and inequities. Another critical issue is that underrepresented groups often lack financial, infrastructure, and training capacity to apply data science methods to research questions of interest to them. In response to some of these challenges in 2023, NIH launched the Science Collaborative for Health disparities and Artificial intelligence bias Reduction(ScHARe), which is a cloud-based social science data platform designed to accelerate research in health disparities, health care outcomes, and artificial intelligence bias mitigation strategies.
There are many opportunities to train a diverse data science workforce who can leverage the potential of AI/ML to accelerate the pace of biomedical and socio-behavioral innovation in drug addiction substance use research and establish mutually beneficial partnerships to enhance the participation of researchers from diverse backgrounds and communities experiencing health disparities. It is critical to provide support for meritorious research, strengthen the research environments and collaborations for successful data science projects and expose undergraduate and/or graduate students in such environments to meritorious research in this area.
Applicants may request up to $300,000 in direct costs for the entire project period of up to 3 years.
January 24, 2024
Division of Neuroscience and Behavior
Susan Wright, PhD
National Institute on Drug Abuse (NIDA)
Telephone: 301-402-6683
Email: susan.wright@nih.gov