Big data from small data: data-sharing in the 'long tail' of neuroscience

In this Commentary, Martone and colleagues discuss the potential benefits of sharing small datasets, also called “long-tail” data, in the Neuroscience community. They introduce the pros and cons associated with data sharing, describe the existing attitudes toward such initiative, introduce best practices and offer their views on why and how the field should establish a credit system for sharing “long-tail” data. The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.