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The Application of Big Data Analytics to Drug Abuse Research (R01)

Post Date

March 9th 2016

Application Due Date

May 7th 2019

Funding Opportunity Number

PA-16-119

CFDA Number(s)

93.279

Funding Instrument Type(s)

Grant

Funding Activity Categories

Education
Health

Eligibility Categories

State Governments
County Governments
City or Township Governments
Special District Governments
Independent School Districts
Public and State Controlled Institutions of Higher Education
Federally Recognized Native American Tribal Governments
Public Housing Authorities or Indian Housing Authorities
Non-Federally Recognized Native American Tribal Organizations
Non-Profits With 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Non-Profits Without 501 (c) (3) Status With The IRS (Except Higher Education Institutions)
Private Institutions of Higher Education
For-Profit Organizations (Except Small Businesses)
Small Businesses
Other

Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.

Funding

  • Award Range:

    $None - $None

Grant Description

The purpose of this FOA is to encourage the application of Big Data analytics to reveal deeper or novel insights into the biological and behavioral processes associated with substance abuse and addiction.NIDA recognizes that to accelerate progress toward understanding how the human brain and behavior is altered by chronic drug use and addiction, it is vital to develop more powerful analytical methods and visualization tools that can help capture the richness of data being generated from genetic, epigenetic, molecular, proteomic, metabolomic, brain-imaging, micro-electrode, behavioral, clinical, social, services, environmental studies as well as data generated from electronic health records.Applications for this FOA should develop and/or utilize computational approaches for analyzing large, complex datasets acquired from drug addiction research.The rapid increase of technologies to acquire unprecedented amounts of neurobiological and behavioral data, and an expanding capacity to store those data, results in great opportunity to bring to bear the power of the computational methods of Big Data analytics on drug abuse and addiction.

Contact Information


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