Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R21 - Clinical Trials Not Allowed) | RFA DA 20 007

Opportunity Information: Apply for RFA DA 20 007

The National Institutes of Health announced this discretionary grant opportunity, RFA-DA-20-007, titled "Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R21 - Clinical Trials Not Allowed)." It uses the R21 mechanism, which is generally intended for exploratory, high-risk/high-reward projects that can generate proof-of-concept results, new directions, or early-stage methods rather than fully mature, large-scale research programs. As the title indicates, clinical trials are not allowed under this funding opportunity, so the work should focus on computational, analytical, and data-driven approaches rather than testing clinical interventions in human participants.

The core goal of the program is to bring more data and computational scientists into addiction and substance use disorder research by encouraging proposals that can integrate and analyze complex datasets that differ in type and scale. In practical terms, this means the NIH is looking for teams that can combine information across levels such as molecular or genomic data, neural circuit or brain imaging data, physiological measures, behavioral data, and other relevant sources in ways that traditional analysis pipelines do not handle well. The emphasis is on developing and applying novel computational, bioinformatics, statistical, and analytical methods that make it possible to ask new questions, find patterns that were previously hidden, and generate insights into the biology of addiction that could not be reached through single-dataset or single-modality approaches.

A major theme in the description is innovation in data integration. The opportunity is not just asking applicants to run standard analyses on existing datasets; it is specifically encouraging new frameworks, tools, or strategies that can connect disparate datasets, harmonize them, and support new types of inference. Examples of the kind of technical direction implied by the language include multimodal data fusion, advanced machine learning or network-based modeling, scalable pipelines for very large datasets, methods for handling data heterogeneity and missingness, and approaches that enable interpretable links between neural mechanisms and addiction-related phenotypes. The intended payoff is that these methods will unlock "previously inaccessible insights" and reveal new aspects of addiction biology, particularly as it relates to neural mechanisms and substance use disorder.

In terms of who can apply, eligibility is broad and spans many sectors. Eligible applicants include various levels of government (state, county, city or township, and special district governments), independent school districts, public and state-controlled institutions of higher education, and private institutions of higher education. The opportunity is also open to federally recognized Native American tribal governments, tribal organizations that are not federally recognized, public housing authorities and Indian housing authorities, and a range of nonprofit organizations both with and without 501(c)(3) status. For-profit organizations (other than small businesses) and small businesses are listed as eligible as well, along with an "Others" category that NIH uses to capture additional eligible entity types. The announcement also explicitly highlights additional eligible applicants such as Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISIs), Hispanic-serving Institutions, Historically Black Colleges and Universities (HBCUs), Tribally Controlled Colleges and Universities (TCCUs), faith-based or community-based organizations, eligible federal agencies, regional organizations, U.S. territories or possessions, and even non-U.S. entities (foreign organizations). Taken together, this signals an intent to broaden participation and encourage cross-disciplinary and cross-institutional collaborations, including organizations that may bring unique datasets, community connections, or specialized computational expertise.

From an administrative standpoint, the funding instrument is a grant, the agency is the NIH, and the activity category is listed under education and health, with CFDA number 93.279. The original closing date in the source information is November 14, 2019, and the creation date is July 10, 2019. The award ceiling and expected number of awards are not specified in the provided excerpt, which often means applicants would need to consult the full FOA text for budget limits, project period expectations, and any institute- or program-specific constraints tied to the R21 mechanism.

Overall, this opportunity is best understood as a call for methodologically ambitious, data-centric projects that can connect the dots across complex addiction-related datasets to clarify neural and biological mechanisms. The NIH is explicitly trying to stimulate new analytical capabilities in the field by attracting researchers who specialize in computation, statistics, bioinformatics, and big data science, with the expectation that these tools will open up new lines of discovery in addiction and substance use disorder research without conducting clinical trials.

• The National Institutes of Health in the education, health sector is offering a public funding opportunity titled "Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R21 - Clinical Trials Not Allowed)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.279.
• This funding opportunity was created on 2019-07-10.
• Applicants must submit their applications by 2019-11-14. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.

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