The Office of Behavioral and Social Sciences Research (OBSSR) was created by Congress in 1993 in recognition of the importance of behavioral and social sciences to the NIH mission. Over more than two decades, the OBSSR has been instrumental in advancing and coordinating behavioral and social science research (BSSR) at the NIH.
The OBSSR Strategic Plan 2017–2021 addresses current emerging opportunities and challenges that have the potential to transform behavioral and social sciences health research including: (1) improvement in the flow of basic to applied science through the research-product pipeline, (2) advances in measurement and methodological approaches, and (3) improvements in the dissemination and implementation of social and behavioral interventions.
To address the second priority area, which involves enhancing the research infrastructure and methods in BSSR, the OBSSR and participating Institutes are launching a new Predoctoral Training in Advanced Data Analytics for BSSR Institutional Research Training Program. The vision of the Advanced Data Analytics for BSSR Training Program is to support the development of a cohort of specialized predoctoral candidates who will possess advanced competencies in data science analytics to apply to an increasingly complex landscape of behavioral and social health-related big data.
Recent advances in medical informatics, electronic health records, big data analytics, mobile and wearable technologies, social media and web generated data, geospatial data, administrative data, and new methods to link data have laid the groundwork for a rich biomedical, behavioral, and social research data environment. The voluminous data environment resulting from diverse data sources will require complex analytical skills to derive rigorous scientific knowledge.
The methodology courses in many Ph.D. programs in the behavioral and social sciences have remained essentially unchanged for the last four decades. To prepare candidates for the world of complex data, the core methods course offerings need to be augmented to provide earlier career training exposure to data science and computational approaches applied in other disciplines such as computer science, applied statistics, and engineering. Training programs may be able to most effectively accomplish this by developing highly coordinated inter-departmental program collaborations for their doctoral candidates.
Applicants are being asked to assemble an interdisciplinary team of scientific mentors to design and direct a training program that includes mentors from relevant BSSR disciplines such as psychology, sociology, economics, anthropology, communication studies, or public health as well as experts in computational or data science analysis approaches from relevant disciplines such as engineering, computer science, applied mathematics, statistics, or physics departments. Integration with training in subdisciplines relevant to NIH Institutes (e.g., health psychology, medical anthropology, medical sociology, health economics) is strongly encouraged. Applicant programs should take advantage of opportunities to engage multiple departments within a university or multiple institutions within proximity to maximize training opportunities.
To support the networking opportunities for this new cohort of specialized predoctoral candidates the OBSSR intends to convene and facilitate cross-site exchanges among the investigators and trainees at the awarded sites. The mentors and trainees funded through this funding opportunity announcement will be required to participate in cross-site activities such as periodic training webinars and annual in-person cross-site BSSR Data Analytics Program grantee meetings.
The OBSSR recognizes the importance of scientific stewardship, particularly in developing the scientific talent and skills needed to advance health-related behavioral and social sciences. Training programs that focus on cutting edge quantitative methods to expand behavioral and social scientists’ capabilities will strengthen BSSR’s ability to meet the scientific challenges of the future.
Please visit https://grants.nih.gov/grants/guide/rfa-files/RFA-OD-19-011.html for the full details about this new training grant program announcement.
