There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.
The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.