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.