Traditional file-based neuroimaging data management and integration strategies have shown increasing limitations in accommodating the meteoric growth in both the scale and complexity of today’s neuroimaging data. The sophisticated software and hardware pipelines required in many of today’s neuroimaging studies have produced numerous platform-specific data files that are increasingly difficult to parse, exchange, and understand by the broader research community. Modern neuroimaging studies are hampered by not only the challenge of parsing and managing rigid and diverse file types, but also the lack of a unified interface for systematic data validation, query, manipulation and integration, thereby limiting its ability to handle large datasets. Creating a future-proof, highly scalable, and low-maintenance data storage and dissemination platform is highly desirable for the broad and rapidly growing neuroimaging community. The future of neuroimaging data management must be scalable, searchable, verifiable, and capable of accommodating highly complex hierarchical data generated from complex paradigms involving multi-modal inputs. Inspired by the great success of NoSQL database platforms, we envision that a unified database interface for managing complex neuroimaging data and exchanging human-readable hierarchical data records will be highly suitable to address the urgent needs of next-generation neuroimaging data management. In this project, we aim to solidify a series of easy-to-adopt, easy-to-extend, human-readable JSON (http://json.org) based data file specifications to systematically assist the storage, exchange and integration of existing and emerging neuroimaging datasets. These JSON-encoded universal data files readily enable users to utilize highly scalable and high-performance NoSQL databases, such as CouchDB and MongoDB, to rapidly disseminate large, NIH-funded neuroimaging public datasets, and enable validation and automation. Our group has been a major contributor to JSON-based scientific data storage since 2011. We have published open- source specifications (http://openjdata.org) to standardize the exchange of neuroimaging data for common formats such as NIfTI/GIfTI/SNIRF, building a solid foundation for application-specific adoptions. In this project, we seek to further develop, solidify, and disseminate JSON-based data exchange specifications and NoSQL databases. We have built collaborations to major neuroimaging data analysis stakeholders, such as FreeSurfer, SPM, FieldTrip, HOMER, BrainStorm. At the end of this project, we will be able to 1) develop a set of stable universal file formats that greatly modernize data sharing in neuroimaging applications, easing future maintenance and extension, and 2) provide open-source tools for users to build NoSQL database backends to facilitate integration and automation of public/private databases, enabling query, validation, and scale-up. Success in this project will result in a robust data exchange platform to facilitate convenient data sharing, promote reproducible research, and forge efficient collaborations among a broad neuroimaging community.

This project aims to make a broad impact upon the neuroimaging community by extending, solidifying and disseminating a set of universal JSON-based neuroimaging data exchange format specifications, and readily enable the ability in both local and cloud-based data analysis pipelines to handle large, complex neuroimaging datasets via widely used NoSQL database technology. The success of this project is expected to greatly improve our ability to integrate, manipulate, reuse and disseminate existing public and NIH-funded neuroimaging datasets, accommodate fast-growing neuroimaging data in both size and complexity, and modernize widely used neuroimaging data analysis pipelines to be ready for automation, large-scale data processing, easy data exchange, and backward-compatible future extensions.