FOA Title: 
BRAIN Initiative: Secondary Analysis and Archiving of BRAIN Initiative Data (R01 Clinical Trial Not Allowed)
Grant Type: 
RFA-MH-20-120
Primary IC: 
NIMH
Release Date: 
Apr 10 2019
Expiration Date: 
Jun 12 2020
AC Source: 
R01
Purpose: 
The BRAIN Initiative and the neuroscience field as a whole is generating massive and diverse research data across different modalities, spatiotemporal scales and species in efforts to advance our understanding of the brain. The data types are being produced through development and application of innovative technologies in high-throughput -omics profiling, optical microscopy, electron microscopy, electrophysiological recording, macroscale neuroimaging, neuromodulation, and others. The BRAIN Initiative has made significant investments in the development of an infrastructure to make data available to the research community in a useful way. This infrastructure includes data archives, data standards, and software for data integration, analysis and machine learning. This Funding Opportunity Announcement (FOA) encourages secondary analysis of the large amounts of existing data related to the BRAIN Initiative. The data do not need to be held in one of the funded BRAIN Initiative data archives, but the data must be held in a data archive that is readily accessible to the research community. Support will be provided for innovative analysis of relevant existing datasets using conventional or novel analytic methods, data science techniques, and machine learning approaches. Support may also be requested to prepare and submit existing data into any of the BRAIN Initiative data archives. Investigators should not underestimate the time and effort that may be necessary to curate or harmonize data. Analyzed data, models and analytical tools generated under this FOA are expected to be deposited into an appropriate data archive. Since the BRAIN Initiative data archives are mostly making the data available to the research community through cloud-based storage, depositing the analyzed data, models and tools are expected to enhance opportunities to create a data sandbox where investigators can easily compare the results of their analysis with those from other research groups.