About this Research Topic
In the last decade neuroscience has entered an exponential growth phase with the rapid generation of deep and diverse data to study brain function and disease. Many of these rich data sets are open-access and matched with powerful online tools for analysis. The bottleneck is now shifting from pure data generation to the development of effective ways to explore, mine, and integrate information. This Research Topic aims to be an online extension of the INCF Short Course on Neuroinformatics, Neurogenomics and Brain Disease. In this workshop, six groups of "next-gen" neuroscientists evaluated how key online data resources can be exploited to address important topics at the interface of neuroscience, genetics, and genomics. Inspired by the concept of a “speed coding jamboree”, the groups took part in a “speed neuroinformatics analysis jamboree” over a one-week period and produced draft papers as an initial output. This first phase of intense interactions with colleagues and senior instructors has been followed by a more deliberate review and critique, and cycles of reanalysis and revisions. The post-jamboree revision phase was conducted exclusively using online cloud-based collaborative tools, as group members were based in diverse locations around the world.
By making use of existing public data and requiring only a laptop connected to the internet, the analyses were free from the constraints of traditional “bricks-and-mortar” laboratories—thus opening up access to the science for a wider research community than ever before.
This Research Topic provides a model and benchmark for a new type of workshop—to learn by collaboration. The studies in this Research Topic aim to provide examples of what is possible through collaborative mining of the rich, and steadily growing, public data resources. It is hoped that this project will provide a template and inspiration for further neuroscience data analysis jamborees in the future.
Editorial Office Note: Further examples of manuscripts based on public data can be found in a 2017 Research Topic online, HERE.
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