An official website of the United States government
This data infrastructure serves the broader research community by making BRAIN Initiative data findable, accessible, interoperable, and reusable (FAIR), accelerating the building and testing of theories and models of brain function.
BRAIN Data Archives
The Initiative has established a network of domain-specific data archives serving particular data types. Investigators should submit data to the archive that matches their data type and modality:
| Archive | Data Types | Link |
|---|---|---|
| DANDI | Cellular neurophysiology, electrophysiology, optophysiology, behavioral time-series | dandiarchive.org |
| NeMO | Multi-omic data (transcriptomics, epigenomics, spatial omics) | nemoarchive.org |
| DABI | Human participant intracranial electrophysiology | dabi.loni.usc.edu |
| OpenNeuro | Human neuroimaging (MRI, fMRI) | openneuro.org |
| OpenNeuro PET | Positron emission tomography | openneuropet.github.io |
| NEMAR | Human EEG, MEG, iEEG | nemar.org |
| BIL | Large-scale microscopy brain imaging (confocal, light sheet) | brainimagelibrary.org |
| BossDB | Electron microscopy and X-ray microtomography | bossdb.org |
| EMBER | Multimodal behavioral, neural, and synchronized neural-behavioral data | brain-bbqs.org |
Investigators working within BRAIN Initiative consortia (BICAN, CONNECTS, BBQS) should coordinate data submissions through their consortium’s data coordinating center.
BRAIN Initiative Archives accept data deposition from non-BRAIN funded NIH project. Reach out to the Program Officer listed on your Notice of Award and the BRAIN Initiative Data Programs for more information.
Data Standards
BRAIN-supported data standards promote interoperability and reproducibility across the Initiative. Common standards include:
- NWB (Neurodata Without Borders) for neurophysiology data
- BIDS (Brain Imaging Data Structure) for neuroimaging data
Investigators are strongly encouraged to use these standards when submitting data to BRAIN archives. Archive staff will provide guidance on technical requirements and submission processes.
Data Sharing Expectations
All NIH-funded research that generates scientific data must include a Data Management and Sharing (DMS) plan. Beginning with applications submitted for due dates on or after May 25, 2026, applicants must use the updated NIH DMS Plan format (see NOT-OD-26-046 ).
The BRAIN Initiative strongly encourages applicants to address the following in their DMS plans:
- Archive selection. Identify the specific BRAIN Data Archive(s) that will receive your data submissions, according to data type or modality. Use the table above to determine the appropriate archive for your data.
- Data standards. Specify the open data standards (e.g., NWB, BIDS) that will be used for each dataset.
- Processing level. Describe the expected level of processing for shared data. A useful guideline is to share data at a processing level 2-3 steps removed from the top-line results you might expect to report in a published journal article.
- Submission timeline. All scientific research data must be submitted to the archive. All data reported in publications must be publicly released for sharing by the time of publication. All submitted data must be publicly released for sharing, at the latest, by the end of the award. BRAIN encourages regular, timely data submission to archives, as this helps investigators become familiar with the submission process and establish consistent submission practices.
Justification should be provided in the DMS plan for sharing data via external or institutional repositories instead of established BRAIN Data Archives. Program staff may reach out to applicants during the pre-award process to ensure DMS plans meet Initiative expectations.
For projects focused on tool development, data acquired purely for testing or calibrating novel tools may fall outside the scope of data sharing requirements. However, sharing validation datasets is strongly encouraged to support tool dissemination and adoption.
BRAIN Knowledgebase
The BRAIN Initiative is developing a Knowledgebase framework to enable its data archives and resources to interoperate and scale as a federated system. Rather than building a single centralized platform, the Knowledgebase is intended to unify access to shared standards, APIs, and resources so that data can be discovered, queried, and integrated across archives. This work builds on the Initiative’s existing data infrastructure investments to support sustainability as data volumes grow.
Contact
For questions about BRAIN data sharing, data archives, or informatics funding opportunities:
Eunyoung Kim, Ph.D.
National Institute of Mental Health (NIMH)
Joseph Monaco, Ph.D.
National Institute of Neurological Disorders and Stroke (NINDS)
BRAIN Data Programs
BRAIN-Data-Programs@nih.gov
Brain Research Through Advancing Innovative Neurotechnologies® Initiative