Data Coordinating Center for the Aging and Alzheimer’s Disease Mouse Brain Atlas (AD-MBA) (U24 Clinical Trial Not Allowed)
National Institutes of Health
Award ceiling
—Award floor
—Posted
Sep 1, 2026Closes
Nov 10, 2026Location eligibility
No specific state restriction found in this listing — check the full opportunity for details.Categories: Health
Eligible applicants: Public and State controlled institutions of higher education, Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education, Small businesses, Native American tribal governments (Federally recognized), Special district governments, County governments, Independent school districts, Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education, Native American tribal organizations (other than Federally recognized tribal governments), City or township governments, Others (see text field entitled "Additional Information on Eligibility" for clarification), State governments, Private institutions of higher education, Public housing authorities/Indian housing authorities, For profit organizations other than small businesses
Description
This Notice of Funding Opportunity (NOFO) invites applications proposing a Data Coordinating Center that integrates and disseminates multiscale mouse brain datasets for the aging and Alzheimer's disease (AD) research community. The Data Coordinating Center will establish the Aging and AD Mouse Brain Atlas (AD-MBA), which will serve as a central, unified research resource for multi-omic and connectomic mouse brain datasets emerging from the NIA AD Multimodal Atlas Projects (AD-MAPs). The Data Coordinating Center will (1) leverage the NIA-supported AD Knowledge Portal data infrastructure to facilitate hosting, deposition, and dissemination of multimodal datasets generated by the AD-MAPs program, (2) facilitate collaborative analyses among the data-contributing teams, (3) develop/optimize and incorporate tools for multiscale integration of multi-omics and connectomics datasets, and (4) create a broadly accessible web-based interface for the visualization and dissemination of integrated multiscale data.