It is critical that applicants follow the Training (T) Instructions in the SF424 (R&R) Application Guide, except where instructed to do otherwise (in this FOA or in a Notice from the NIH Guide for Grants and Contracts). Conformance to all requirements (both in the Application Guide and the FOA) is required and strictly enforced. Applicants must read and follow all application instructions in the Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the Application Guide, follow the program-specific instructions. Applications that do not comply with these instructions may be delayed or not accepted for review.

Part 1. Overview Information
Part 2. Full Text of the Announcement
Section I. Funding Opportunity Description
Section II. Award Information
Section III. Eligibility Information
Section IV. Application and Submission Information
Section V. Application Review Information
Section VI. Award Administration Information
Section VII. Agency Contacts
Section VIII. Other Information

The overall goal of the NIH Ruth L. Kirschstein National Research Service Award (NRSA) program is to help ensure that a diverse pool of highly trained scientists is available in appropriate scientific disciplines to address the Nation's biomedical, behavioral, and clinical research needs. In order to accomplish this goal, NRSA training programs are designed to train individuals to conduct research and to prepare for research careers. More information about NRSA programs may be found at the Ruth L. Kirschstein National Research Service Award (NRSA) website.

Purpose and Background Information

The NRSA program has been the primary means of supporting predoctoral and postdoctoral research training programs since enactment of the NRSA legislation in 1974. Research training activities can be in basic biomedical or clinical sciences, in behavioral or social sciences, in health services research, or in any other discipline relevant to the NIH mission.

Institutional NRSA programs allow the Training Program Director/Principal Investigator (Training PD/PI) to select the trainees and develop a program of coursework, research experiences, and technical and/or professional skills development appropriate for the selected trainees. Each program should provide high-quality research training and offer opportunities in addition to conducting mentored research. The grant offsets the cost of stipends, tuition and fees, and training related expenses, including health insurance, for the appointed trainees in accordance with the approved NIH support levels.

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 (Kaplan, Riley, Mabry, 2014).

The purpose of this Funding Opportunity Announcement (FOA) is to solicit applications for new behavioral and social sciences research (BSSR) predoctoral training programs that focus on innovative computational and/or data science analytic approaches and their incorporation into training for the future BSSR health research workforce. The vision of the Advanced Data Analytics for BSSR training program is to support the development of a cohort of specialized BSSR predoctoral candidates pursuing careers in health-related research who will possess advanced competencies in data science analytics.

Clinical health research is transitioning from a data-limited environment in which data is prospectively obtained to a data-rich environment where data are also collected dynamically from multiple sources and through an increasingly web-interconnected society with a more comprehensive health research infrastructure network. Furthermore, the biomedical scientific community’s increasing emphasis on precision medicine calls on future BSSR scientists to provide an understanding of behavioral and environmental influences within this new data rich frontier. Transdisciplinary complex data research opportunities already are coming to the forefront with NIH initiatives such as the All of Us/Precision Medicine Initiative and the Big Data to Knowledge (BD2K) programs. In addition, a key goal in the NIH Strategic Plan for Data Science is to enhance workforce development for complex biomedical data (https://datascience.nih.gov/sites/default/files/NIH_Strategic_Plan_for_Data_Science_Final_508.pdf). These developments and opportunities necessitate a paradigm shift in the BSSR workforce’s training in data analytics to employ research methodologies that are ably suited to temporally dense and dynamically complex data systems.

Big data in the behavioral and social sciences tends to come from mixed sources (e.g., social media, unstructured text, digital sensors or wearables, administrative databases, high-density census population datasets with geographic detail), they are often generated dynamically over time, and not necessarily designed to produce valid or reliable data for scientific analysis. Applying advanced analytic approaches to these kinds of data provides opportunities to transform the spatiotemporal analysis of demographics, behaviors, social interactions, and economics as they relate to health outcomes. Working with disparate social data streams requires careful attention to structuring, harmonizing and extracting meaningful features from the data, which in turn, requires advanced computational and data science statistical approaches. In this sense, behavioral and social sciences big data is notable less for size than for the complexity that renders conventional analytic methods inadequate. In addition, BSSR data are becoming increasingly interdisciplinary as they are combined and merged with biomedical, genetic, geospatial, and administrative data. Successful collection and rigorous analysis of complex behavioral and social science health data will require: solid scientific critical thinking skills, cross-disciplinary collaborations, new research methods, computational tools and data science approaches such as data mining, pattern recognition, machine learning, computational modeling, causal inference, and a keen awareness of the continuously emerging data methods, challenges, and opportunities.

The methodology courses in many current 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.

While there are some existing opportunities for interdisciplinary methods coursework in postdoctoral training programs, there are far fewer opportunities for aspiring BSSR scientists to get exposure to data science and computational modeling methods during their predoctoral years. Changing the career trajectories for a new cohort of BSSR graduates equipped with data science skills will require programs to start earlier and to integrate exposure to data science approaches within existing predoctoral degree programs. Therefore, this training program aims to entice quantitatively-minded students into BSSR careers, and importantly, to create a cohort of specialized predoctoral trainees who will learn together in coursework and collaborate in teams on big data-focused health research.

The foundational training for these BSSR predoctoral programs should include coursework and training experiences in academia or industry, a multidisciplinary team approach, collaborative research opportunities, and should ensure adequate mentorship in advance computational methods with an emphasis on principles and practices that promote reproducibility of results. It is expected that trainees will acquire core knowledge in three overarching relevant areas in: (1) computer science/informatics, (2) statistics/mathematics, and (3) behavioral or social sciences research in a chosen health domain relevant to the NIH institutes and centers participating in this program announcement. The training should include aspects of computer science/informatics and statistics/mathematics that are directly relevant to behavioral and social sciences research in health. The critical thinking and scientific inquiry skills taught within social sciences discipline programs will provide an important context for properly interpreting findings in the new data-rich environment.

This program is not intended to support training for predoctoral candidates earning degrees in biomedical sciences, physical sciences or informatics disciplines because there are other existing programs that have provided for support for students in those disciplines (e.g., see: https://datascience.nih.gov/bd2k/announcements/training and https://grants.nih.gov/grants/guide/pa-files/par-17-096.html).

To be deemed responsive to this RFA, applicants must propose programs designed for predoctoral trainees enrolled in Ph.D. or equivalent research doctoral degree programs leading to careers in behavioral and social sciences research in health. Trainees should be appointed in the early stages of their graduate program. Because of the great need for comprehensive knowledge and skills across three different areas of science (outlined above), trainees should be appointed for a minimum of 2 years with additional 1-2 years allowed if justified by the program plans or by specific trainee needs. Training programs are encouraged to transition trainees to other support such as individual fellowships (F31) or to research grants when feasible.

The NIH Office of Behavioral and Social Sciences Research (OBSSR) intends to convene and facilitate cross-site exchanges among investigators and trainees at the awarded sites. The T32 training programs 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 T32 Program grantee meetings.

Enhancing workforce diversity. Within the framework of the NIH s longstanding commitment to excellence, attention must be given to recruitment of trainees from racial or ethnic groups underrepresented in the biomedical, behavioral and clinical sciences, individuals with disabilities, and individuals from disadvantaged backgrounds. See SF424 Application Guide for instructions. The applicants to this training program are strongly encouraged to recruit students from diverse backgrounds and foster their successful completion of the graduate program and transition to their next position. Programs are also expected to expose students to appropriate role models as faculty, seminar speakers and leaders.

Each training program will be required to focus on one or more of three broad categories noted below, each of which necessitates training and competencies in advanced computational or data science analytics. Applicants should propose a training program that prepares predoctoral BSSR candidates to acquire skills in computational modeling and/or data science methods and apply them within a domain of BSSR health research of interest to one or more of the participating NIH Institutes. This training program is intended to augment the core methods courses in BSSR Ph.D. programs. Therefore, for applications to be deemed responsive to this FOA the applicants must describe plans and curricula that will offer new courses and practical experience with advanced data analytics and computational modeling approaches specifically designed to handle the kinds of big and complex data described below as applied to health research.

1.Intensive or voluminous longitudinal data.

The rapidly developing areas of mHealth, smartphone and portable sensor technologies, and increasing interconnectedness through web-enabled platforms has made it possible to continuously collect real-time data and perform intensive longitudinal assessments of individuals in their natural environments, oftentimes in an unobtrusive manner. These data collection tools offer new possibilities for detailed surveillance and epidemiology research, but also for etiological research that can inform intervention development research. In this space, personal electronic health record or EHR data reflects a special case of intensive longitudinal data that is increasingly available for use in behavioral research. However, EHR data is often not necessarily collected for research purposes and is often fraught with missing data or other computationally relevant problems, and its use may require a complex understanding of HIPAA or other regulatory rules that affect use reporting.

Intensive data capture technologies offer the promise for prevention and treatment approaches to be studied, optimized, and tailored based in part on real-time data collected from smartphones, wearable bio sensors, electronic health records, personal health records, and other sources of frequent patient reported outcomes. Behavioral and biobehavioral interventions for prevention and treatment are important tools for dealing with drug and alcohol abuse, mental illnesses, HIV/AIDS, Hepatitis C, smoking cessation, weight management, diabetes management, cancer prevention, and to help with monitoring and managing symptoms from acute and chronic conditions such as pain, fatigue, sleep disturbance, impaired cognition, disordered mood, appetite, and any number of additional possible health applications.

The next generation behavioral and social scientists who wish to work with these kinds of voluminous longitudinal datasets will require knowledge of high volume data curation and analysis approaches for handling intensive longitudinal data including methods for verification, replication, validation, harmonization, and computational modeling such as non-frequentist approaches to treatment effect estimation or analytic models for individualized patient decision support and optimized interventions.

2. Internet, commercial, and administrative records data.

Social media platforms, internet data sources, crowdsourcing and citizen science data collections, retail purchasing tracking databases, and many other electronic administrative or commercial records of behavioral and social data are experiencing unprecedented worldwide growth. Digital health care administrative data from patients, providers, and insurers is also becoming more readily available for behavioral and social science inquiry in many areas of health.

Since much of this data is often behavioral, there is a great need for trained behavioral and social scientists to help curate, mine, link, and analyze social media data, web-based text, image and video data, passively collected internet or cellphone data, crowdsourced data, product purchasing data, and health administrative data for the purposes of health research. Some notable examples of heath studies involving web and social media data have included: infectious disease surveillance, monitoring of adverse reactions caused by medications, studying the availability of and interest in various nicotine and tobacco products, tracking trends in alcohol use and problem drinking, monitoring prescription drug abuse, predicting asthma prevalence, real-time monitoring of suicide risk factors, analyzing food consumption patterns, studying seasonal patterns in weight loss, physical activity and other fitness goals. Relevant examples of research involving administrative records, includes: analyzing insurance claims databases to identify patterns of prescription seeking including fraudulent activity such as doctor shopping for prescription opioids, using organizational and clinical performance data to study mental health care service systems outcomes and costs, or using hospital administrative datasets to predict patient risk of readmissions or mortality and identify system level levers for improvement in outcomes.

Computer scientists have developed many innovative approaches for handling complex text, image, video, and networked internet or social media data, and for curating and coding complex administrative datasets. The next generation behavioral and social scientists would benefit from training in these areas. Advances in automated data processing including algorithms from machine learning, text mining, data mining, artificial intelligence or natural language processing, and innovations in computational modeling all present possibilities for utilizing these voluminous data sources for public health monitoring, surveillance, or even as platforms for targeting at risk populations with tailored health messaging and personalized real-time interventions.

3. High-density, large sample or population level agency databases.

Federal and State agencies play a central role in the collection of a wide array of public and administrative data vital statistics on health, transportation, commerce, finance, agriculture, and more. Much of this information is gathered by statistical agencies, but smaller organizations for example, the Consumer Financial Protection Bureau, the Army Corps of Engineers and USAID also gather important information. The quantity of available microdata from sources such as the U.S. Census Bureau, the Center for Disease Control and Prevention (CDC), the Centers for Medicare and Medicaid Services (CMS), and other international statistical agencies and historical sources available for population research is exploding. Complete census enumerations and other high-density samples offer rich geographic detail of the world’s population over many decades, creating unique sources for population health research inquiry. Vast troves of microdata in concert with new computational and data science technologies provide the potential to transform the spatiotemporal analysis of demographic behavior, economic activity, for the purposes of health research. Commercial vendors and university-based research data repositories are also offering researchers access to large troves of U.S. and international datasets that can be merged, linked, and utilized to study population health. Some notable examples include, but are not limited to: the Integrated Public Use Microdata Series (IPUMS) (https://usa.ipums.org/usa/), the CDC health-related datasets, including the National Death Index data (https://www.cdc.gov/nchs/nchs_for_you/researchers.htm), Centers for Medicare & Medicaid Services (CMS) data (https://www.resdac.org/cms-data/request/cms-virtual-research-data-center), the U.S. Bureau of Labor Statistics (BLS) National Longitudinal Surveys datasets (https://www.bls.gov/nls/).

The possible topics for health research inquiry with these kinds of big data are nearly limitless. Examples include: projects that measure costs and benefits or changes in Social Security, Medicare, Medicaid, and other programs, studies that examine key factors such as the impact of population aging, health care cost growth, programmatic changes, and uncertainty on various health and services forecasts, projects that examine the complex causal relationships between race, ethnicity, and socioeconomic status and health and morbidity across the life course, analysis of alternative models for coordinating care delivery in complex health care systems, including accountable care organizations, medical homes, or through behavioral treatment systems of care including estimation of clinical and economic outcomes.

The next generation behavioral and social scientists interested in properly curating, linking, mining and combining these kinds of complex large sample datasets for research purposes will require specific data science and computational training. Because inferential statistics developed for small sample surveys are inappropriate for analyzing entire populations with billions of records behavioral and social sciences, trainees interested in these datasets will require training in innovative computational and mathematical modeling approaches, in techniques for data mining and harmonization, and in methods for dealing with unmeasured heterogeneity.

Primary Organizational Focus of the Training Program: Multiple PDs/PIs are allowed and encouraged.

This funding opportunity announcement requires applicants to assemble an interdisciplinary team of scientific mentors to design and direct a training program. Applications must include mentors from relevant behavioral and social sciences research (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. All programs should aim to provide predoctoral level instruction and practical experience in advanced data analytics relevant to research in domains of health best suited for BSSR inquiry. 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. Ideally the trainees will have at least two main mentors who represent completely different areas of expertise to foster a truly cross-disciplinary training experience (e.g., one mentor would be from a behavioral or social science domain and one mentor would be in a computer science or informatics domain).

NIH strongly encourages institutions with expertise in the three areas discussed above who have not previously received training grants from NIH to apply. NIH also encourages institutions that currently have multiple NIH training grants and who wish now to apply for this training grant program to consider drawing on and taking advantage of existing training activities, through collaborative approaches to expand beyond what their current training programs offer to create a unique, effective data analytics training program and one which can augment the training of people in content areas relevant to NIH institutes. In this regard, proposed training programs may complement other ongoing research training and career development programs at the applicant institution; however, the research training experiences for this new program must be distinct from those currently receiving Federal support or that already exist at the applicant institution. The purpose is to create an entirely new predoctoral training program that is not presently available to BSSR students at the applicant institution. Current P50 Program Directors or applicants at institutions with NIH center grant awards or other programmatic awards such as Clinical and Translational Science Award (CTSA) awards who wish to apply for this program are encouraged to describe how these other awards will be used to provide professional development opportunities or serve as a research hub for these new predoctoral trainees.

Institutional research training grants must be used to support a program of full-time research training. The program may not be used to support studies leading to M.D., D.D.S., or other clinical, health-professional training. Short-term training is not intended. Research training programs solely for short-term research training should not apply to this announcement.

Applicants are strongly encouraged to contact the Scientific/Research Contacts in advance to discuss your application for its overall relevance and responsiveness to this OBSSR led training program and to its specific relevance for the interests of the participating ICs (see Section VII., Agency Contacts). Examples of the training focus for each of the participating ICs includes:

National Cancer Institute (NCI):

The NCI is interested in utilizing computational modeling and/or data science methods to understand the predictors, mediators and moderators of behavior including outcomes such as tobacco use, sedentary behavior, physical activity, sun safety, alcohol use, medication adherence and diet/nutrition. In addition, training in multi-level models that examine the effects of multiple and potentially interacting factors ranging from biology to the built environment and the impact of policy on behavior are encouraged. Training in newly established data collection efforts, including (but not limited to) crowdsourcing and citizen science, wearables, sensors, smartphones and the internet of things are encouraged. Training in techniques to merge and/or link different data sets to answer novel cancer control-related research questions are also a priority.

National Heart, Lung, and Blood Institute (NHLBI):

NHLBI supports programs that provide data science training to behavioral and social science fellows in research areas pertaining to the prevention and treatment of heart, lung, blood, and sleep disorders (HLBS), as well as, the promotion of health in these areas, both domestically and internationally. NHLBI also has interests in research that addresses social determinants of health and health disparities, resilience in HLBS disease, and implementation research of proven-effective evidence-based interventions in clinical, community, or other settings for the prevention and treatment of HLBS. Details of NHLBI’s research priorities are provided in the NHLBI Strategic Visioning Plan. Training should include a comprehensive approach from data collection and data management (including data privacy and security) to advanced data analytical strategies and techniques. These can include, but are not limited to, training in: 1) collection of dynamic, longitudinal data in real-time with wearables, sensors, and smartphones (i.e., the internet of things (IoT)); data collection through information sharing platforms and virtual communities/networks (i.e., social media); crowdsourcing and citizen science; in-depth analysis of existing study databases; 2) data harmonization, integration, and linking of data across studies or diverse data sources; and 3) data mining, data visualization, pattern recognition, simulation modeling and systems science to address the prevention and treatment of HLBS disease. Data analyses from multi-level, adaptive, or other complex technological interventions are also encouraged. Training in analytical strategies to understand the influence and interactions of social determinants of health at multiple levels so as to inform the development of multilevel interventions to reduce inequities in HLBS diseases are encouraged. Research training that examines the effects of multiple and potentially interacting factors ranging from genetics, biology, psychology, and health behaviors to the built environment and HLBS-related health policies, and maintenance of health behaviors over time are also encouraged. Investigators should also consider using existing study data sets made available through the NHLBI Biologic Specimen and Data Repository (BioLINCC) and/or the NIH database of Genotypes and Phenotypes (dbGaP).

National Institute on Aging (NIA):

NIA welcomes applications aligned with NIA’s strategic mission (See: https://www.nia.nih.gov/sites/default/files/2017-07/nia-strategic-directions-2016.pdf) in all areas related to the social and behavioral sciences. Applications addressing topics related to Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) are strongly encouraged.

National Institute on Alcohol Abuse and Alcoholism (NIAAA):

NIAAA seeks to support training in advanced data analysis and computational modeling to improve our understanding of the determinants, mediators, moderators, and health consequences of risky and hazardous drinking and related behaviors and interventions to reduce the associated harms of alcohol consumption. Specific challenges include but are not limited to: (1) modeling the interactions of contextual, environmental, cognitive, and behavioral factors that contribute to risky and hazardous alcohol consumption and related behaviors and characterizing the dynamic processes involved at different temporal scales, from momentary decision-making to life-course patterns and outcomes; (2) developing methods and analytic strategies for gaining insights from novel and complex kinds of data on alcohol-related behaviors, such as may be gathered from wearable technologies, sensors, geospatial data streams, ecological momentary assessments, internet and social media sources, and crowd-sourcing data; and (3) advancing modeling and analysis to incorporate profound heterogeneity across multiple dimensions that affect and characterize alcohol-related behaviors and outcomes, including e.g., individuals and groups, settings, drinking patterns, and complex vulnerabilities in order to identify high-leverage intervention opportunities.

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD):

NICHD supports training programs in research areas relevant to the institute’s scientific objectives. The mission of NICHD is to ensure that every person is born healthy and wanted, that women suffer no harmful effects from reproductive processes, and that all children have the chance to achieve their full potential for healthy and productive lives. The institute also aims to ensure the health, productivity, independence, and well-being of people through optimal rehabilitation. Information on NICHD’s extramural branches and programs can be found at: https://www.nichd.nih.gov/about/org/der/branches. For this RFA, NICHD encourages applications seeking to incorporate advanced data analytics into new training programs within the scope of NICHD s behavioral and social science portfolio. Examples of the types of analytic training of interest to NICHD include, but are not limited to:

Addressing collider variable bias/endogenous selection bias in big data samples,

Computational and/or biologically plausible modeling for child development and population health,

Computational modeling applied to injury biomechanics and pediatric injury prevention,

Computational health science and population dynamics/demography,

Creation and analysis of synthetic data sets to model complex human behavior,

Data harmonization and linkage,

Image analytics for video data,

Integrative data analysis for development science,

Natural language processing to study child development or to improve clinical decision support.

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK):

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) encourages applications that propose training in advanced data analytics and methodologies for key mission areas including diabetes and other endocrine and metabolic diseases; digestive diseases, nutritional disorders, and obesity; and kidney, urologic, and hematologic diseases. Proposed research training activities should have translational potential and include clinically meaningful disease endpoints. Areas of emphasis include but are not limited to training in the design of new methodologies and tools for patient reported outcomes and shared decision making; data analytic approaches for large, complex datasets to understand key behavioral and social drivers and barriers to help optimize and personalize prevention and treatment goals and reduce disparities in service delivery and outcomes; data integration and harmonization methodology to enable integration of novel sources of social and behavioral data with data from studies supported by the NIDDK; novel methods to evaluate data from adaptive and pragmatic clinical trials; and novel methods to identify predictors, mediators/moderators, and other covariates that change over time.

National Institute on Drug Abuse (NIDA):

NIDA is interested in training that supports advanced analytic methods for complex and intensive behavioral data. Such training can include but is not limited to the use of new statistical tools and techniques to model complex behavioral processes; development or enhancement of assessment tools; harmonization and/or integration of data across studies; integration of behavioral data with biological/biometric data; or data analysis from multi-level, adaptive, or other complex intervention trials. NIDA supports the utilization of robust methods to unpack administrative data, geospatial data, electronic health records, social network data, data from personal devices, and data from public sources that can contribute to understanding the development, treatment, and prevention of substance use and substance use disorders. NIDA also has an interest in data analyses relevant to drug prevention and treatment services and high-priority HIV/AIDS research (see https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-137.html). Investigators are encouraged to consider using data sets made available through the National Addiction and HIV Data Archive Program (NAHDAP). NIDA’s research priorities are detailed in the NIDA Strategic Plan for 2016-2020.

National Institute of Mental Health (NIMH):

NIMH encourages doctoral candidates in the social and behavioral sciences to develop the capacity to apply advanced data analytics to mental health research studies in areas that include but are not limited to: 1) reducing disparities in mental health service delivery and outcomes, 2) detecting and responding to severe mental illness and severe emotional disturbances at an early stage, 3) preventing suicide and self-harm and 4) informing new targets for and timing of treatment and services for psychiatric disorders. Examples of large datasets that might be valuable for training purposes include but are not limited to data from: electronic health records (EHRs); public and commercial insurance claims and other administrative databases; automated collection via smartphones, wearables, or sensors; internet or device use (e.g., browser history, social media use and content), social and economic datasets available for population health research; and publicly available datasets from the NIMH Data Archive and other repositories.

National Institute of Nursing Research (NINR):

NINR welcomes applications which focus on nursing science as described in the current strategic plan found at: https://www.ninr.nih.gov/sites/www.ninr.nih.gov/files/NINR_StratPlan2016_reduced.pdf. Of particular interest are applications from schools of nursing. NINR priority areas for this opportunity include: innovative study designs (e.g., pragmatic, adaptive, social network analysis, comparative effectiveness) and analytical techniques (e.g., propensity modeling, data visualization) used to implement cost-effective, sustainable wellness interventions across communities and populations. In addition, innovative methods (e.g., trajectory science, longitudinal case studies, agent based, and computation science) and data sources (e.g., electronic health records, existing longitudinal studies) used to explore characteristics (e.g., physiological, biological, behavioral) and their interrelationships that are associated with maintenance of healthy behaviors over time are encouraged.

National Institute on Minority Health and Health Disparities (NIMHD):

NIMHD is interested in data science training to support the research priorities of the institute that could have significant impact on understanding and addressing minority health and health disparities using simulation modeling and other complex data analysis methods. Minority or health disparity populations of interest are African Americans/Blacks, Hispanics/Latinos, American Indians/Alaska Natives, Asians, Native Hawaiians and Other Pacific Islanders, socioeconomically disadvantaged populations, underserved rural populations, and sexual and gender minority populations.

National Library of Medicine (NLM):

NLM is interested in supporting training for a biomedical informatics/data science workforce prepared to make conceptual and methodological advances to meet unique data challenges in behavioral and social science research, including approaches that foster FAIR (findable, accessible, interoperable, reusable) practices. Areas of interest include but are not limited to training in discovery, digital curation, analytics, modeling, statistical methods, visualization, privacy and next-generation mining approaches for reusable digital data objects.

Career opportunities. The career outcomes of individuals supported by these NRSA training programs include research careers in academia and industry and research-related careers in various sectors, e.g., academic institutions, government agencies, for-profit businesses, and private foundations. The training programs should provide students access to a wide range of structured, career development advising and learning opportunities (e.g., coursework, workshops, discussions, research projects).

Oversight of trainee mentoring and progression. Trainees supported by this program will be expected to have formal individual development plans to ensure that they obtain a Ph.D. degree in a timely manner, and with 1) a publication record that will allow them to progress to outstanding postdoctoral research opportunities, 2) written and oral presentation skills that will facilitate their ability to publish their results as first author, submit competitive grant applications, speak at national meetings and interview for future positions, 3) a working knowledge of various potential career directions that make strong use of the knowledge and skills gained during research training and the steps required to transition successfully to the next stage of their chosen career.

This Funding Opportunity Announcement (FOA) does not allow appointed Trainees to lead an independent clinical trial but it does allow them to obtain research experience in a clinical trial led by a mentor or co-mentor. NIH strongly supports training towards a career in clinically relevant research and so gaining experience in clinical trials under the guidance of a mentor or co-mentor is encouraged.

See Section VIII. Other Informationfor award authorities and regulations.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this FOA.

Higher Education Institutions

• Public/State Controlled Institutions of Higher Education
• Private Institutions of Higher Education

The following types of Higher Education Institutions are always encouraged to apply for NIH support as Public or Private Institutions of Higher Education:

• Hispanic-serving Institutions
• Historically Black Colleges and Universities (HBCUs)
• Tribally Controlled Colleges and Universities (TCCUs)
• Alaska Native and Native Hawaiian Serving Institutions
• Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)

Nonprofits Other Than Institutions of Higher Education

• Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
• Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)

Governments

• Indian/Native American Tribal Governments (Federally Recognized)
• Indian/Native American Tribal Governments (Other than Federally Recognized)
• U.S. Territory or Possession

Other

• Native American Tribal Organizations (other than Federally recognized tribal governments)
• Faith-based or Community-based Organizations

The sponsoring institution must assure support for the proposed program. Appropriate institutional commitment to the program includes the provision of adequate staff, facilities, and educational resources that can contribute to the planned program.

The applicant institution must have a strong and high-quality research program in the area(s) proposed under this FOA and must have the requisite faculty, staff, potential trainees and facilities on site to conduct the proposed institutional program. In many cases, it is anticipated that the proposed program will complement other ongoing career development programs occurring at the applicant institution and that a substantial number of program faculty will have active research projects in which participating scholars may gain relevant experiences consistent with their research interests and goals.

Non-domestic (non-U.S.) Entities (Foreign Institutions) are not eligible to apply.

Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.

Applicant Organizations

Applicant organizations must complete and maintain the following registrations as described in the SF 424 (R&R) Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. The NIH Policy on Late Submission of Grant Applications states that failure to complete registrations in advance of a due date is not a valid reason for a late submission.

• Dun and Bradstreet Universal Numbering System (DUNS) - All registrations require that applicants be issued a DUNS number. After obtaining a DUNS number, applicants can begin both SAM and eRA Commons registrations. The same DUNS number must be used for all registrations, as well as on the grant application.
• System for Award Management (SAM) (formerly CCR) Applicants must complete and maintain an active registration, which requires renewal at least annually. The renewal process may require as much time as the initial registration. SAM registration includes the assignment of a Commercial and Government Entity (CAGE) Code for domestic organizations which have not already been assigned a CAGE Code.
• NATO Commercial and Government Entity (NCAGE) Code Foreign organizations must obtain an NCAGE code (in lieu of a CAGE code) in order to register in SAM.
• eRA Commons - Applicants must have an active DUNS number and SAM registration in order to complete the eRA Commons registration. Organizations can register with the eRA Commons as they are working through their SAM or Grants.gov registration. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application.
• Grants.gov Applicants must have an active DUNS number and SAM registration in order to complete the Grants.gov registration.

Program Directors/Principal Investigators (PD(s)/PI(s))

All PD(s)/PI(s) must have an eRA Commons account. PD(s)/PI(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in eRA Commons.If the PD/PI is also the organizational Signing Official, they must have two distinct eRA Commons accounts, one for each role. Obtaining an eRA Commons account can take up to 2 weeks.

Any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research training program as the Training Program Director/Principal Investigator (Training PD/PI) is invited to work with his/her organization to develop an application for support. Individuals from underrepresented racial and ethnic groups as well as individuals with disabilities are always encouraged to apply for NIH support.

For institutions/organizations proposing multiple PDs/PIs, visit the Multiple Program Director/Principal Investigator Policy and submission details in the Senior/Key Person Profile (Expanded) Component of the SF 424 (R&R) Application Guide.

The PD/PI should be an established investigator in the scientific area in which the application is targeted and capable of providing both administrative and scientific leadership to the development and implementation of the proposed program. The PD/PI will be responsible for the selection and appointment of trainees to the approved research training program, and for the overall direction, management, administration, and evaluation of the program. The PD/PI will be expected to monitor and assess the program and submit all documents and reports as required. The PD/PI has responsibility for the day to day administration of the program and is responsible for appointing members of the Advisory Committee (when applicable), using their recommendations to determine the appropriate allotment of funds.

This FOA does not require cost sharing as defined in the NIH Grants Policy Statement.

Applicant organizations may submit more than one application, provided that each application is programmatically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time. This means that the NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review (see NOT-OD-11-101).

Program faculty should have strong records as researchers, including recent publications and successful competition for research support in the area of the proposed research training program. Program faculty should also have a record of research training, including successful, former trainees who have established productive careers relevant to the NIH mission. Researchers from diverse backgrounds, including racial and ethnic minorities, persons with disabilities, and women are encouraged to participate as mentors.

This funding opportunity announcement requires applicants to assemble an interdisciplinary team of scientific mentors to design and direct a training program. Applications must include mentors from relevant behavioral and social sciences research (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.

Ideally the trainees will have at least two main mentors who represent completely different areas of expertise to foster a truly cross-disciplinary training experience (e.g., one mentor would be from a behavioral or social science domain and one mentor would be in a computer science or informatics domain).

NIH strongly encourages institutions with relevant expertise who have not previously received training grants from NIH to apply. NIH also encourages institutions that currently have multiple NIH training grants and who wish now to apply for this training grant program to consider drawing on and taking advantage of existing training activities, through collaborative approaches to expand beyond what their current training programs offer to create a unique, effective data analytics training program and one which can augment the training of people in content areas relevant to NIH institutes. In this regard, proposed training programs may complement other ongoing research training and career development programs at the applicant institution; however, the research training experiences for this new program must be distinct from those currently receiving Federal support or that already exist at the applicant institution. The purpose is to create an entirely new predoctoral training program that is not presently available to BSSR predoctoral students at the applicant institution. Current P50 or U54 Program Directors or applicants at institutions with NIH center grant awards or other programmatic awards such as Clinical and Translational Science Award (CTSA) awards who wish to apply for this program are encouraged to describe how these other awards will be used to provide professional development opportunities or serve as a research hub for these new predoctoral trainees.

The individual to be trained must be a citizen or a noncitizen national of the United States or have been lawfully admitted for permanent residence at the time of appointment. Additional details on citizenship, training period, and aggregate duration of support are available in the NIH Grants Policy Statement.

The predoctoral trainees must be enrolled in a program leading to a Ph.D. or in an equivalent research doctoral degree program leading to a career in behavioral and social sciences research in health.

This program is not intended to support training for predoctoral candidates earning degrees in biomedical and physical sciences because there are other existing T32 programs that provide for support for students in those disciplines. To be deemed responsive to this RFA, applicants must propose programs designed for predoctoral trainees enrolled in Ph.D. or equivalent research doctoral degree programs leading to careers in behavioral and social sciences research in health.

Trainees should be appointed in the early stages of their graduate program. Because of the great need for comprehensive knowledge and skills trainees should be appointed for a minimum of 2 years with additional 1-2 years allowed as justified by the program plans. Training programs are encouraged to transition trainees to other support such as individual fellowships (F31) or to research grants when feasible.

All trainees are required to pursue their research training full time, normally defined as 40 hours per week, or as specified by the sponsoring institution in accordance with its own policies. Appointments are normally made in 12-month increments, and no trainee may be appointed for less than 9 months during the initial period of appointment, except with prior approval of the NIH awarding unit, or when trainees are appointed to approved, short-term training positions.

Buttons to access the online ASSIST system or to download application forms are available in Part 1 of this FOA. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

It is critical that applicants follow the Training (T) Instructions in the SF424 (R&R) Application Guide except where instructed in this funding opportunity announcement to do otherwise. Conformance to the requirements in the Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

For information on Application Submission and Receipt, visit Frequently Asked Questions Application Guide, Electronic Submission of Grant Applications.

Letter of Intent

Although a letter of intent is not required, is not binding, and does not enter into the review of a subsequent application, the information that it contains allows IC staff to estimate the potential review workload and plan the review.

By the date listed in Part 1. Overview Information, prospective applicants are asked to submit a letter of intent that includes the following information:

• Descriptive title of proposed activity
• Name(s), address(es), and telephone number(s) of the PD(s)/PI(s)
• Names of other key personnel
• Participating institution(s)
• Number and title of this funding opportunity

The letter of intent should be sent to:

Elizabeth Ginexi, Ph.D.

Health Scientist Administrator, Office of Behavioral and Social Sciences Research (OBSSR)

Telephone: 240-594-4574

Email: [email protected]

All page limitations described in the SF424 (R&R) Application Guide and the Table of Page Limits must be followed.

Instructions for Application Submission

The following section supplements the instructions found in the SF424 (R&R) Application Guide and should be used for preparing an application to this FOA.

Follow all instructions provided in the SF424 (R&R) Application Guide.

Follow all instructions provided in the SF424 (R&R) Application.

Follow all instructions provided in the SF424 (R&R) Application, with the following additional modifications:

Project Summary/Abstract. Provide an abstract of the entire application. Include the objectives, rationale and design of the research training program, as well as key activities in the training plan. Indicate the planned duration of appointments, the projected number of trainees including their levels (i.e., year of predoctoral training, and intended trainee outcomes.

Other Attachments. An Advisory Committee is not a required component of a training program. However, if an Advisory Committee is intended, provide a plan for the appointment of an Advisory Committee to monitor progress of the training [career development] program. The composition, roles, responsibilities, and desired expertise of committee members, frequency of committee meetings, and other relevant information should be included. Describe how the Advisory Committee will evaluate the overall effectiveness of the program. Proposed Advisory Committee members should be named in the application if they have been invited to participate at the time the application is submitted.

A plan for training in advanced data analytics or computational methods relevant to behavioral and social sciences research in health, and quantitative skills and literacy, is a required program component, and must be included in the body of the application. For programs requesting funds to develop curricula in these areas, detailed syllabi outlining the format and subject matter content of activities must be included as an attachment to the application. Curriculum development funds will not be provided without a well-conceived plan for training in quantitative computational skills and literacy (curriculum enhancement of existing statistics courses may be insufficient to warrant curriculum development support). Please name your file "Quantitative_Training_Syllabi.pdf."

The filename provided for each Other Attachment will be the name used for the bookmark in the electronic application in eRA Commons.

Follow all instructions provided in the SF424 (R&R) Application.

Follow all instructions provided in the SF424 (R&R) Application.

Follow all instructions provided in the SF424 (R&R) Application Guide, with the following additional modifications:

Follow all instructions provided in the SF424 (R&R) Application Guide with the following additional modifications:

• Include all personnel other than the Training PD(s)/PI(s) in the Other Personnel section, including clerical and administrative staff.
• Include requests for curriculum development funds as part of the Training Related Expenses (up to $20,000 in the first budget year of the project). Include a justification for these funds and itemize how the support will be used for the development and/or enhancement of curricula and activities to meet this FOA's training requirement for advanced data analytics or computational methods relevant to behavioral and social sciences research in health, and quantitative skills and literacy. Include a description of who will be supported by the funds, their qualifications and the level of effort required.

The PHS 398 Research Training Program Plan Form is comprised of the following sections:

• Training Program
• Faculty, Trainees, and Training Record
• Other Training Program Sections
• Appendix Note that the Appendix should only be used in circumstances covered in the NIH policy on appendix materials or if the FOA specifically instructs applicants to do so.

Follow all instructions provided in the SF424 (R&R) Application Guide with the following additional modifications:

Particular attention must be given to the required Training Data Tables for predoctoral training (tables: 1, 2, 3, 4, 5A, 6A, 8A). Applicants should summarize, in the body of the application, key data from the tables that highlight the characteristics of the applicant pool, faculty mentors, the educational and career outcomes of participants, and other factors that contribute to the overall environment of the program.

Training Program

Program Plan

Proposed Training.

The PD/PI should describe program activities intended to develop the working knowledge needed for trainees to select among and prepare for the next step in varied research career options available in the workforce. For example, programs should provide all trainees with instruction and training in oral and written presentation and in skills needed to apply for individual fellowship or grant support.

The training program should be designed to ensure that by the end of the training period, trainees would have received sufficient breadth in knowledge and skills in the areas that complement their undergraduate degree as well as depth in complementary data science or computational modeling areas. Because trainees will enter the program with different knowledge and skill sets, a trainee's program may have to be customized.

Common elements of a successful training program should include the following:

Courses: Describe how the courses will expose trainees to the basic concepts and working knowledge in the scientific areas of advanced data analytics or computational modeling as well as a behavioral or social sciences research discipline. It is incumbent on the applicant to define a set of core concepts that graduating students will master, even if their research projects are highly specialized.

Team Science Approach to Problem Solving: Describe how the trainees will be provided with opportunities to work together in teams or as part of an interdisciplinary research group effort to solve with data analysis challenges. Describe how problem-based learning through a team approach be considered in the design of a core curriculum. Describe how the trainees will learn about rigorous experimental design and transparency to enhance reproducibility of results in teams.

Rotations and External Internships: Rotations are widely recognized as effective means to introduce students to the broadest range of the myriad types of data sets that are a challenge to data science. For example, describe how the training program will offer rotations in computer or in clinical laboratories after trainees have had sufficient course work to gain basic applications of their data science training in relevant areas. As another example, programs can describe experiences in academic, industrial, and other relevant settings that may be provided for trainees to introduce them to a variety of creative approaches to conducting complex data research.

Joint Mentorship: Describe how the program will structure arrangements for joint mentorship of trainees and for peer-to-peer mentoring between more senior trainees and more junior trainees. One way to enhance training and communication among disciplines is for trainees to have mentors from more than one of the interdisciplinary scientific areas (computer science, statistics, and the behavioral or social sciences). Ideally trainees will have at least two main mentors who represent completely different areas of expertise to foster a truly cross-disciplinary training experience (e.g., one mentor would be from a behavioral or social science domain and one mentor would be in a computer science or informatics domain).

Reproducibility of Research Results: Describe how the training program will emphasize practices that promote the reproducibility of results, such as scientific and rigorous design and implementation of experiments, usage of analysis methods based on scientifically sound statistical principles, and the sharing of code, data, protocols, and other information necessary for reproducing research.

Forums for Intellectual Exchanges: It is important for trainees to have opportunities to interact with other trainees and faculty from other data analytic training programs to discuss published articles and research in progress and to interact with visiting scholars. Describe the planned mechanisms for fostering intellectual exchanges such as journal clubs; seminars by students, faculty, and outside speakers; annual retreats; and the annual cross-site BSSR Data Analytics T32 Program grantee meetings.

Individual Development Plans (IDP): Describe the IDP plans for the training program. Each student is encouraged to have an IDP in place at the beginning of their appointment to the program. The IDP should be developed jointly by the trainee and her/his mentors and should be reviewed at a minimum annually (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-13-093.html). It is expected that at some point during the training, appointees should have the opportunity to conduct dissertation research in areas of behavioral and social sciences health research involving advanced data science analytics or computational modeling.

If funds for curriculum development are requested, the applicant must describe the new courses, how they will differ from and build upon existing ones, and how they utilize appropriate technology. The applicant should describe how course materials will be disseminated and shared widely and how they can be used, modified, and updated by others.

Program Administration.

Institutions with existing programs must explain what distinguishes this program from the others already in existence, how their programs will synergize with one another, if applicable, and make it clear that the pool of faculty, potential scholars, and resources are robust enough to support additional programs.

Describe the strengths, leadership and administrative skills, training experience, scientific expertise, and active research of the PD/PI. Relate these strengths to the proposed management of the training program. Describe the planned strategy and administrative structure to be used to oversee and monitor the program. If there are multiple PDs/PIs, then the plan for Program Administration is expected to synergize with the "Multiple PD/PI Leadership Plan" section of the application.

Program Faculty.

The application must include information about the program faculty who will serve as preceptors/mentors and, if relevant, distinguish between faculty members who will serve as primary mentors and those who have other roles. Describe the complementary expertise and experiences of the program faculty as they relate specifically to the programmatic structure and goals of the program. Describe expectations for faculty participation in programmatic activities beyond training within their labs. Describe the involvement of participating mentors in training students to conduct their research with quantitative rigor.

Institutional Environment and Commitment to the Program.

Plan for Instruction in the Responsible Conduct of Research

Individuals are required to comply with the instructions for Plan for Instruction in the Responsible Conduct of Research as provided in the SF424 (R&R) Application Guide.

Appendix

Limited items are allowed in the Appendix. Follow all instructions for the Appendix as described in the SF424 (R&R) Application Guide; any instructions provided here are in addition to theSF424 (R&R) Application Guide instructions.

All instructions in the SF424 (R&R) Application Guide must be followed.

See Part 1. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

Part I. Overview Information contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday, the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies). Applicants must then complete the submission process by tracking the status of the application in the eRA Commons, NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time. If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Policy on Late Application Submission.

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the SF424 (R&R) Application Guide.

This initiative is not subject to intergovernmental review.

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement. The National Research Service Award (NRSA) policies apply to this program. An NRSA appointment may not be held concurrently with another Federally sponsored fellowship, traineeship, or similar Federal award that provides a stipend or otherwise duplicates provisions of the NRSA.

Pre-award costs are allowable only as described in the NIH Grants Policy Statement. Note, however, that pre-award costs are not allowable charges for stipends or tuition/fees on institutional training grants because these costs may not be charged to the grant until a trainee has actually been appointed and the appropriate paperwork submitted to the NIH awarding component. Any additional costs associated with the decision to allow research elective credit for short-term research training are not allowable charges on an institutional training grant.

Applications must be submitted electronically following the instructions described in the SF424 (R&R) Application Guide. Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit Applying Electronically. If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Guidelines for Applicants Experiencing System Issues. For assistance with application submission, contact the Application Submission Contacts in Section VII.

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile Component of the SF424(R&R) Application Package. Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH.

The applicant organization must ensure that the DUNS number it provides on the application is the same number used in the organization’s profile in the eRA Commons and for the System for Award Management (SAM). Additional information may be found in the SF424 (R&R) Application Guide.

See more tips for avoiding common errors.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and responsiveness by components of participating organizations, NIH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

Applicants are required to follow the instructions for post-submission materials, as described in the policy. Any instructions provided here are in addition to the instructions in the policy.

Only the review criteria described below will be considered in the review process. As part of the NIH mission, all applications submitted to the NIH in support of biomedical and behavioral research are evaluated for scientific and technical merit through the NIH peer review system.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood that the proposed training program will prepare individuals for successful, productive scientific research careers and thereby exert a sustained influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the project proposed).

Reviewers will consider each of the review criteria below in the determination of the merit of the training program, and give a separate score for each. When applicable, the reviewers will consider relevant questions in the context of proposed short-term training. An application does not need to be strong in all categories to be judged likely to have major scientific impact.

Training Program and Environment

• Are the research facilities and research environment conducive to preparing trainees for successful careers as behavioral and social scientists?
• Are the objectives, design and direction of the proposed research training program likely to ensure effective training?
• Do the courses, where relevant, and research experiences provide opportunities for trainees to acquire state-of-the-art scientific knowledge, methods, and tools that are relevant to the goals of the training program?
• Does the program provide appropriate inter- or multidisciplinary research training opportunities?
• Is the proposed training program likely to ensure trainees will be well prepared for research-intensive and research-related careers?
• Is the level of institutional commitment to the training program, including administrative and research training support, sufficient to ensure the success of the program?
• Is it clear how the proposed training program is distinguished from other externally funded training programs at the institution?

In addition, for this FOA:

• Does the program or training track prepare trainees to apply novel methodologies and data analytic techniques applicable to behavioral and social sciences Big Data?
• Are innovative approaches used to build on existing programs?
• Does the research environment include large amounts of data of varying types and complexities?
• Curriculum (when applicable): Is the new curriculum novel and innovative? Are the plans to make these curricula widely and openly available adequate? Where applicable, are the training materials in a form that can be modified, updated, and tailored by others? Does the program utilize appropriate technology to enhance, facilitate, and personalize the learning process?

Training Program Director(s)/Principal Investigator(s) (PD(s)/PI(s))

• Does the PD/PI have the scientific background, expertise, and administrative and training experience to provide strong leadership, direction, management, and administration of the proposed research training program?
• Does the PD/PI plan to commit sufficient effort to ensure the program s success?

For applications designating multiple PDs/PIs:

• Is a strong justification provided that the multiple PD/PI leadership approach will benefit the training program and the trainees?
• Is a strong and compelling leadership approach evident, including the designated roles and responsibilities, governance, and organizational structure consistent with and justified by the aims of the training program and the complementary expertise of the PDs/PIs?

Preceptors/Mentors

• Are sufficient numbers of experienced preceptors/mentors with appropriate expertise and funding available to support the number and level of trainees proposed in the application?
• Do the preceptors/mentors have strong records as researchers, including recent publications and successful competition for research support in areas directly related to the proposed research training program?
• Do the preceptors/mentors have strong records of training individuals at the level of trainees proposed in the program? Are appropriate plans in place to ensure that preceptors lacking sufficient research training experience are likely to provide strong and successful mentoring?

Trainees

• Is a recruitment plan proposed with strategies likely to attract well-qualified trainees for the training program?
• Is there a competitive applicant pool of sufficient size and quality, at the proposed predoctoral level to ensure a successful training program?
• Are there well-defined and justified selection and re-appointment criteria as well as retention strategies?

Training Record

• How successful are other past students in similar trainings in completing such programs?
• Has the training program ensured past students are productive in similar training in terms of research accomplishments, publication of research conducted during the training period, and subsequent training appointments and fellowship or career development awards?
• How successful are past students in similar training in achieving productive scientific careers as evidenced by successful competition for research science positions in industry, academia, government or other research venues; grants; receipt of honors, awards, or patents; high-impact publications; promotion to scientific leadership positions; and/or other such measures of success?
• Does the program propose a rigorous evaluation plan to assess the quality and effectiveness of the training? Are effective mechanisms in place for obtaining feedback from current and former trainees?

As applicable for the project proposed, reviewers will evaluate the following additional items while determining scientific and technical merit, and in providing an overall impact score, but will not give separate scores for these items.

Protections for Human Subjects

Generally not applicable. Reviewers should bring any concerns to the attention of the Scientific Review Officer.

Inclusion of Women, Minorities, and Children

Generally not applicable. Reviewers should bring any concerns to the attention of the Scientific Review Officer.

Vertebrate Animals

Generally not applicable. Reviewers should bring any concerns to the attention of the Scientific Review Officer.

Biohazards

Generally not applicable. Reviewers should bring any concerns to the attention of the Scientific Review Officer.

Resubmissions

Not applicable.

Renewals

Not applicable.

Revisions

Not applicable.

As applicable for the project proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

Recruitment Plan to Enhance Diversity

Peer reviewers will separately evaluate the recruitment plan to enhance diversity after the overall score has been determined. Reviewers will examine the strategies to be used in the recruitment of individuals from underrepresented groups. The plan will be rated as ACCEPTABLE or UNACCEPTABLE, and the consensus of the review committee will be included in an administrative note in the summary statement.

Training in the Responsible Conduct of Research

All applications for support under this FOA must include a plan to fulfill NIH requirements for instruction in the Responsible Conduct of Research (RCR). Taking into account the specific characteristics of the training program, the level of trainee experience, and the particular circumstances of the trainees, the reviewers will evaluate the adequacy of the proposed RCR training in relation to the following five required components:

1) Format - Does the plan satisfactorily address the format of instruction, e.g., lectures, coursework and/or real-time discussion groups, including face-to-face interaction? (A plan involving only on-line instruction is not acceptable.);

2) Subject Matter Does the plan include a sufficiently broad selection of subject matter, such as conflict of interest, authorship, data management, human subjects, research misconduct, research ethics?

3) Faculty Participation - Does the plan adequately describe how faculty will participate in the instruction?

4) Duration of Instruction - Does the plan meet the minimum requirements for RCR, i.e., at least eight contact hours of instruction?

5) Frequency of Instruction Does the plan meet the minimum requirements for RCR, i.e., at least once during each career stage and at a frequency of no less than once every four years?

Plans and past record will be rated as ACCEPTABLE or UNACCEPTABLE, and the summary statement will provide the consensus of the review committee.

Select Agent Research

Reviewers will assess the information provided in this section of the application, including (1) the Select Agent(s) to be used in the proposed research, (2) the registration status of all entities where Select Agent(s) will be used, (3) the procedures that will be used to monitor possession use and transfer of Select Agent(s), and (4) plans for appropriate biosafety, biocontainment, and security of the Select Agent(s).

Budget and Period of Support

Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s), convened by the Center for Scientific Review in accordance with NIH peer review policy and procedures, using the stated review criteria. Assignment to a Scientific Review Group will be shown in the eRA Commons.

As part of the scientific peer review, all applications:

• May undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score.
• Will receive a written critique.

Appeals of initial peer review will not be accepted for applications submitted response to this FOA.

Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this FOA. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions:

• Scientific and technical merit of the proposed project as determined by scientific peer review.
• Availability of funds.
• Relevance of the proposed project to program priorities.

After the peer review of the application is completed, the PD/PI will be able to access his or her Summary Statement (written critique) via the eRA Commons. Refer to Part 1 for dates for peer review, advisory council review, and earliest start date

Information regarding the disposition of applications is available in the NIH Grants Policy Statement.

If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the NIH Grants Policy Statement.

A formal notification in the form of a Notice of Award (NoA) will be provided to the applicant organization for successful applications. The NoA signed by the grants management officer is the authorizing document and will be sent via email to the grantee’s business official.

Awardees must comply with any funding restrictions described in Section IV.5. Funding Restrictions. Selection of an application for award is not an authorization to begin performance. Any costs incurred before receipt of the NoA are at the recipient's risk. These costs may be reimbursed only to the extent considered allowable pre-award costs.

Any application awarded in response to this FOA will be subject to terms and conditions found on the Award Conditions and Information for NIH Grants website. This includes any recent legislation and policy applicable to awards that is highlighted on this website.

All NIH grant and cooperative agreement awards include the NIH Grants Policy Statement as part of the NoA. For these terms of award, see the NIH Grants Policy Statement Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General and Part II: Terms and Conditions of NIH Grant Awards, Subpart B: Terms and Conditions for Specific Types of Grants, Grantees, and Activities. More information is provided at Award Conditions and Information for NIH Grants.

Recipients of federal financial assistance (FFA) from HHS must administer their programs in compliance with federal civil rights law. This means that recipients of HHS funds must ensure equal access to their programs without regard to a person’s race, color, national origin, disability, age and, in some circumstances, sex and religion. This includes ensuring your programs are accessible to persons with limited English proficiency. HHS recognizes that research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research.

For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this FOA. HHS provides general guidance to recipients of FFA on meeting their legal obligation to take reasonable steps to provide meaningful access to their programs by persons with limited English proficiency. Please see http://www.hhs.gov/ocr/civilrights/resources/laws/revisedlep.html. The HHS Office for Civil Rights also provides guidance on complying with civil rights laws enforced by HHS. Please see http://www.hhs.gov/ocr/civilrights/understanding/section1557/index.html; and http://www.hhs.gov/ocr/civilrights/understanding/index.html. Recipients of FFA also have specific legal obligations for serving qualified individuals with disabilities. Please see http://www.hhs.gov/ocr/civilrights/understanding/disability/index.html. Please contact the HHS Office for Civil Rights for more information about obligations and prohibitions under federal civil rights laws at http://www.hhs.gov/ocr/office/about/rgn-hqaddresses.html or call 1-800-368-1019 or TDD 1-800-537-7697. Also note it is an HHS Departmental goal to ensure access to quality, culturally competent care, including long-term services and supports, for vulnerable populations. For further guidance on providing culturally and linguistically appropriate services, recipients should review the National Standards for Culturally and Linguistically Appropriate Services in Health and Health Care at http://minorityhealth.hhs.gov/omh/browse.aspx?lvl=2&lvlid=53.

In accordance with the statutory provisions contained in Section 872 of the Duncan Hunter National Defense Authorization Act of Fiscal Year 2009 (Public Law 110-417), NIH awards will be subject to the Federal Awardee Performance and Integrity Information System (FAPIIS) requirements. FAPIIS requires Federal award making officials to review and consider information about an applicant in the designated integrity and performance system (currently FAPIIS) prior to making an award. An applicant, at its option, may review information in the designated integrity and performance systems accessible through FAPIIS and comment on any information about itself that a Federal agency previously entered and is currently in FAPIIS. The Federal awarding agency will consider any comments by the applicant, in addition to other information in FAPIIS, in making a judgement about the applicant’s integrity, business ethics, and record of performance under Federal awards when completing the review of risk posed by applicants as described in 45 CFR Part 75.205 Federal awarding agency review of risk posed by applicants. This provision will apply to all NIH grants and cooperative agreements except fellowships.

Institutional NRSA training grants must be administered in accordance with the current NRSA section of the NIH Grants Policy Statement - Institutional Research Training Grants.

The taxability of stipends is described in the NIH Grants Policy Statement. Policies regarding the Ruth L. Kirschstein-NRSA payback obligation are explained in the NIH Grants Policy Statement.

Awards made primarily for educational purposes are exempted from the PHS invention requirements and thus invention reporting is not required, as described in the NIH Grants Policy Statement.

Not Applicable

When multiple years are involved, awardees will be required to submit the Research Performance Progress Report (RPPR) annually. Continuation support will not be provided until the required forms are submitted and accepted.

Failure by the grantee institution to submit required forms in a timely, complete, and accurate manner may result in an expenditure disallowance or a delay in any continuation funding for the award.

The Federal Funding Accountability and Transparency Act of 2006 (Transparency Act), includes a requirement for awardees of Federal grants to report information about first-tier subawards and executive compensation under Federal assistance awards issued in FY2011 or later. All awardees of applicable NIH grants and cooperative agreements are required to report to the Federal Subaward Reporting System (FSRS) available at www.fsrs.gov on all subawards over $25,000. See the NIH Grants Policy Statement for additional information on this reporting requirement.

• The institution must submit a completed Statement of Appointment (PHS Form 2271) for each trainee appointed or reappointed to the training grant for 8 weeks or more. Grantees must submit the PHS 2271 data electronically using the xTrain system. More information on xTrain is available at xTrain (eRA Commons). An appointment or reappointment may begin any time during the budget period, but not before the budget period start date of the grant year.
• A notarized statement verifying possession of permanent residency documentation must be submitted with the Statement of Appointment (PHS Form 2271). Individuals with a Conditional Permanent Resident status must first meet full (non-conditional) Permanent Residency requirements before receiving support.
• Termination Notice: Within 30 days of the end of the total support period, the institution must submit a Termination Notice (PHS Form 416-7) via xTrain for each trainee appointed for eight weeks or more.

A final RPPR, the expenditure data portion of the Federal Financial Report, and Termination Notices for all Trainees, are required for closeout of an award as described in the NIH Grants Policy Statement. Evaluation results should be included as part of the final RPPR.

In accordance with the regulatory requirements provided at 45 CFR 75.113 and Appendix XII to 45 CFR Part 75, recipients that have currently active Federal grants, cooperative agreements, and procurement contracts from all Federal awarding agencies with a cumulative total value greater than $10,000,000 for any period of time during the period of performance of a Federal award, must report and maintain the currency of information reported in the System for Award Management (SAM) about civil, criminal, and administrative proceedings in connection with the award or performance of a Federal award that reached final disposition within the most recent five-year period. The recipient must also make semiannual disclosures regarding such proceedings. Proceedings information will be made publicly available in the designated integrity and performance system (currently FAPIIS). This is a statutory requirement under section 872 of Public Law 110-417, as amended (41 U.S.C. 2313). As required by section 3010 of Public Law 111-212, all information posted in the designated integrity and performance system on or after April 15, 2011, except past performance reviews required for Federal procurement contracts, will be publicly available. Full reporting requirements and procedures are found in Appendix XII to 45 CFR Part 75 Award Term and Conditions for Recipient Integrity and Performance Matters.

In carrying out its stewardship of human resource-related programs, the NIH may request information essential to an assessment of the effectiveness of this program from databases and from participants themselves. Participants may be contacted after the completion of this award for periodic updates on various aspects of their employment history, publications, support from research grants or contracts, honors and awards, professional activities, and other information helpful in evaluating the impact of the program.

Within 8-10 years of making awards under this program, NIH will assess the program’s overall outcomes, gauge its effectiveness in enhancing diversity, and consider whether there is a continuing need for the program. Upon the completion of this evaluation, NIH will determine whether to (a) continue the program as currently configured, (b) continue the program with modifications, or (c) discontinue the program.

The overall evaluation of the program will be based on metrics that will include, but are not limited to, the following:

For programs involving graduate students:

• Successful completion of a BSSR doctoral degree program
• Subsequent participation in a formal research training or career development program
• Subsequent participation in research or employment in a research field
• Authorship of scientific publications
• Subsequent independent research grant support from NIH or another source

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

To make sure that applications are responsive to the requirements of one of the participating NIH ICs, prior consultation with NIH staff is strongly encouraged.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten on-time submission, and post-submission issues)

Finding Help Online: http://grants.nih.gov/support/ (preferred method of contact)
Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

General Grants Information (Questions regarding application processes and NIH grant resources)
Email: [email protected] (preferred method of contact)
Telephone: 301-945-7573

Grants.gov Customer Support (Questions regarding Grants.gov registration and Workspace)
Contact Center Telephone: 800-518-4726
Email: [email protected]

Elizabeth Ginexi, Ph.D.
NIH Office of Behavioral and Social Sciences Research (OBSSR)
Telephone: 240-594-4574
Email: [email protected]

Susan N. Perkins, Ph.D.
National Cancer Institute (NCI)
Telephone: 240-276-5630
Email: [email protected]

Richard Moser, Ph.D.
National Cancer Institute (NCI)
Telephone: 240-276-6915
Email: [email protected]

Rebecca Campo, Ph.D.
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-594-1047
Email: [email protected]

Partha Bhattacharyya, Ph.D.
National Institute on Aging (NIA)
Telephone: 301-496-3131
Email: [email protected]

Dan Falk, Ph.D.
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Telephone: 301-443-0788
Email: [email protected]

Regina Bures, Ph.D.
National Institute of Child Health and Human Development (NICHD)
Telephone: 301-496-9485
Email: [email protected]

Luke E. Stoeckel, Ph.D.
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Telephone: 301-741-9223
Email: [email protected]

Michele Rankin, Ph.D.
National Institute on Drug Abuse (NIDA)
Telephone: 301-480-3832
Email: [email protected]

Lauren Hill, Ph.D.
National Institute of Mental Health (NIMH)
Telephone: 301-443-2638
Email: [email protected]

David Banks, Ph.D., MPH, MSSW, RN
National Institute of Nursing Research (NINR)
Telephone: 301-496-9558
Email: [email protected]

Beda Jean-Francios, Ph.D.
National Institute on Minority Health and Health Disparities (NIMHD)
Telephone: 301-594-9764
Email: [email protected]

Jane Ye, Ph.D.
National Library of Medicine (NLM)
Telephone: 301-594-4927
Email: [email protected]

Delia Olufokunbi Sam, Ph.D.
Center for Scientific Review (CSR)
Telephone: 301-613-6206
Email: [email protected]

Carol Perry
National Cancer Institute (NCI)
Telephone: 240-276-6282
Email: [email protected]

Bryan Clark, MBA
National Institute of Child Health and Human Development (NICHD)
Telephone: 301-435-6975
Email: [email protected]

Pamela Fleming
National Institute on Drug Abuse (NIDA)
Telephone: 301-480-1159
Email: [email protected]

Tamara Kees
National Institute of Mental Health (NIMH)
Telephone: 301-443-8811
Email: [email protected]

Judy S. Fox
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Telephone: 301-443-4704
Email: [email protected]

Ronald Wertz
National Institute of Nursing Research (NINR)
Telephone: 301-594-2807
Email: [email protected]

Amy Keener
National Library of Medicine (NLM)
Telephone: 301-496-4221
Email: [email protected]

Laurel Kennedy
National Heart, Lung, and Blood Institute (NHLBI)
Telephone: 301-827-4777
Email: [email protected]

Jill Bradshaw
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Telephone: 301-827-1230
Email: [email protected]

Priscilla Grant, JD
National Institute on Minority Health and Health Disparities (NIMHD)
Telephone: 301-594-8412
Email: [email protected]

Jessi Perez
National Institute on Aging (NIA)
Telephone: 301-402-7739
Email: [email protected]

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts. All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement.

Awards are made under the authorization of Section 487 of the Public Health Service Act as amended (42 USC 288) and under Federal Regulations 42 CFR 66.