Making the BRAIN Initiative FAIRer

Background: With the advent of the new NIH Data Management and Sharing requirements, institutions are turning to libraries to provide increased data sharing support for their research community. Within neuroscience research, the landscape around data sharing has been developing through the support of the NIH BRAIN Initiative, which in 2017 began funding the development of data archives and standards emerging from the community. A key mechanism for dissemination of these resources were large collaborative grants supporting the study of brain circuits underlying behavior for which a data science core was a required component. However, the bottom up approach to the development of archives and standards left gaps in coverage that could make adherence to the top down mandated FAIR principles difficult.

Description: Insufficient or uninterpretable metadata results in data that cannot be reused. This issue was a primary concern for both the data science core director of one of the collaborative circuit function grants, a librarian, and a postdoctoral researcher in one of the collaborating labs, an experimental neuroscientist who collected behavioral and electrophysiological data. The postdoc had piloted a tool to facilitate the collection of structured, standardized metadata in his lab, and, as part of the work of the data science core, the postdoc and the librarian collaborated on the further development and dissemination of the tool. One of the biggest challenges in capturing experimental metadata is that the collection of complete and understandable metadata during an experiment is very often an unwelcome burden, therefore central to the development of the metadata collection tool was a recognition that if it required a steep learning curve and/or significant disruption of experimental workflows, widespread adoption would be very difficult to achieve. Additional support for tool development and dissemination from a grant supplement awarded by NIH allowed for retaining an outside development firm with expertise in both front and backend development and user experience to maximize the functionality and usability of this tool.

Conclusion: While the ultimate measure of success of the metadata collection tool will be the number of labs in which it is adopted, there will be a number of points of evaluation over the course of tool development. The first milestone will be, through a process of usability testing and continuous quality improvement, achievement of satisfaction of users with the tool within the context of usability testing. During early dissemination efforts, visits to the website will indicate that a broader community is becoming aware of the tool. The number of registered users will be a further indication of interest. Finally, a reflection of success in these early stages will be the number of labs/researchers willing to serve as beta testers.