PROJECT SUMMARY Neuroscience research has historically relied on observing tightly controlled behaviors in siloed laboratory experiments, constraining our understanding of the neural bases of complex behaviors observed in naturalistic settings. With ongoing advances in unobtrusive sensing technology, artificial intelligence, and machine learning (AI/ML), and availability of computing power, the field of neuroscience has been afforded an opportunity to make large-scale discoveries hitherto unimaginable. For this to be realized, however, it is crucial to facilitate secondary analyses that cut across individual datasets, allowing for research that transcends individual project designs. Such a goal cannot be achieved without a data archive that provides a compelling technical solution for storing and curating datasets, and that provides close integration with analytical resources that require minimal technical expertise to be leveraged. Here, we propose the Ecosystem for Multi-modal Brain-behavior Experimentation and Research (EMBER), a data archive specifically tailored to serve the unique needs of the Brain Behavior Quantification and Synchronization (BBQS) research community, which will be at the forefront of advancing neurobehavioral knowledge in coming years. At the heart of EMBER is a scalable, hybrid data archive which will house and manage multimodal and multi-species data collected by diverse research groups. Crucially, our hybrid architecture will not only automatically execute the optimal storage scheme for different modalities of data, leveraging existing BRAIN Informatics resources, but also achieve dual objectives of ensuring the security of behavioral and environmental data — which may include Protected Health Information (PHI) and Personally Identifiable Information (PII) — as well as expediting querying and data access not only within BBQS datasets but also with other BRAIN resources. Different cadres of EMBER users, such as BBQS data generators, analysts, as well as the broader neuroscience community will be able to ingest, curate, and instigate discovery from data using a user-friendly portal that will streamline highly technical data harmonization and synchronization steps. In particular, development, testing, and deployment of analysis tools will be supported by cloud-based sandboxes that are seamlessly integrated with ML/AI resources developed by the BBQS Data Coordination and Artificial Intelligence Center (DCAIC). Integral to EMBER’s success will be acceptance in the community as the gold-standard engine for discovery, providing utility beyond being simply a passive, program-mandated data archive. Throughout its lifecycle, we will nurture bidirectional collaboration with the data generators, analysts, as well as the broader neuroscience research community to introduce and maintain tools for sharing, querying, and analyzing data. We anticipate that EMBER and associated data resources will maximize the BBQS program’s potential t...