Why develop standards? 

With major brain initiatives across Asia, North America, and Europe committing significant resources to large-scale, multi-faceted efforts to understand the nervous system, we are likely entering a golden age for neuroscience. At the same time, neuroscience, like many domains in biomedicine, is undergoing a reproducibility crisis, where small, underpowered studies, problems in experimental design and analysis, and lack of routine data sharing lead to difficulty in relying on published results. 

Common to both the large brain projects and individual investigator led research is the recognition that neuroscience as a whole needs to converge towards a more open and collaborative enterprise, with neuroscientists around the globe committed to open sharing of data and tools. The Declaration of Intent of the International Brain Initiative, an alliance of large national brain projects, states: “Researchers working on brain initiatives from around the world recognise that they are engaged in an effort so large and complex that even with the unprecedented efforts and resources from public and private enterprise, no single initiative will be able to tackle the challenge to fully understand the brain”. 

Effective resource sharing means not just that data, processing methods, workflows and tools are made available, but that they are made available in a way that ensures that published findings can be reproduced.

Of equal importance, in the age of machine learning and artificial intelligence, data should be published with integration and reuse in mind, so they can be interpreted in new ways, and leveraged so that new knowledge can be extracted. For that to happen, neuroscience as a discipline needs to adopt the FAIR principles [LINK], ensuring that the results of science are Findable, Accessible, Interoperable and Reusable, to both humans and machines.