Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder (R01 - Clinical Trials Not Allowed) | RFA DA 20 006

Opportunity Information: Apply for RFA DA 20 006

The National Institutes of Health (NIH) funding opportunity titled "Leveraging Big Data Science to Elucidate the Neural Mechanisms of Addiction and Substance Use Disorder" (Funding Opportunity Number RFA-DA-20-006) is a discretionary grant program using the R01 mechanism, with clinical trials explicitly not allowed. The central aim is to bring data science, computational, and quantitative expertise directly into addiction and substance use disorder research by encouraging investigators to develop and apply innovative methods that can combine and analyze complex datasets that are difficult to interpret using traditional approaches.

This opportunity is focused on "big data" approaches that integrate information across different data types and scales, with the idea that addiction biology and the neural mechanisms underlying substance use disorders can be better understood when diverse data streams are examined together rather than in isolation. The FOA emphasizes the creation or adaptation of novel computational, bioinformatics, statistical, and analytical strategies that enable new kinds of analyses. In practical terms, the program is looking for proposals where the primary contribution is methodological and integrative: approaches that can merge heterogeneous datasets, manage high dimensionality, uncover hidden structure or relationships, and generate insights that were previously inaccessible due to data complexity, fragmentation, or scale.

The expected outcome of funded projects is not merely the production of another dataset, but the development and demonstration of analytical frameworks that can reveal new aspects of addiction biology, especially neural mechanisms. That can include techniques for harmonizing or linking datasets collected at different resolutions (for example, molecular or genomic signals alongside neural circuit measures and behavioral or clinical phenotypes), building models that can account for multi-level influences, or designing computational pipelines that make it possible to test mechanistic hypotheses using integrated evidence. Although the FOA does not list specific required data modalities in the provided text, the intent is clearly to support work that leverages multi-source data integration and advanced analytics to push beyond conventional single-dataset or single-scale interpretations.

Eligibility is broad and spans many organizational types, which signals an interest in drawing talent from a wide range of sectors that include academia, government, nonprofits, and industry. Eligible applicants include state, county, city or township governments; special district governments; independent school districts; public and state-controlled institutions of higher education; private institutions of higher education; federally recognized Native American tribal governments; tribal organizations that are not federally recognized; public housing authorities and Indian housing authorities; nonprofits with or without 501(c)(3) status (as long as they are not institutions of higher education in those categories); for-profit organizations other than small businesses; small businesses; and other unspecified entities. The announcement also explicitly calls out additional eligible applicants such as Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISI), 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 breadth suggests the FOA is designed to attract both domain experts and specialists in data-intensive methods, including teams that may not traditionally lead addiction neuroscience proposals but have strong computational capabilities.

From a logistics and administrative standpoint, the FOA falls under the Health and Education funding activity category and is associated with CFDA number 93.279. The opportunity was created on 2019-07-10, and the original closing date listed is 2019-11-14. An award ceiling is not specified in the provided source text, and the number of expected awards is also not provided, which typically means applicants would need to refer to the full NIH FOA document or related budget guidance for exact limits and expectations.

In summary, this R01 grant opportunity is essentially an invitation for data and computational scientists, as well as interdisciplinary teams, to build and apply next-generation analytic methods for integrating multi-type, multi-scale datasets in order to clarify the brain-based mechanisms of addiction and substance use disorder. The defining features are (1) methodological innovation in computation and analytics, (2) integration of diverse data sources to enable new analyses, (3) a focus on neural mechanisms and addiction biology, and (4) the clear restriction that proposed work must not include 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 (R01 - 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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