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-Inspired by the success of endeavours like the [Human Genome Project](https://www.genome.gov/human-genome-project) and [CERN](https://home.cern/), neuroscientists are increasingly initiating large-scale collaborations. The largest efforts, such as the [International Brain Laboratory](https://www.internationalbrainlab.com/) [@doi.org/10.1016/j.neuron.2017.12.013;@doi.org/10.1016/j.conb.2020.10.020], [The Blue Brain Project](https://www.epfl.ch/research/domains/bluebrain/) and [Human Brain Project](https://www.humanbrainproject.eu) bring together tens to hundreds of researchers across multiple laboratories. These projects have generated scientific insights, large-scale datasets, tools and educational materials, and represent a step-change in scale. Organisationally, they follow a formal collaborative model with open outputs. That is, there are participating laboratories who collaborate together and then make their data, methods and results available. As such, to participate, individuals must join a participating laboratory, initiate a collaboration with the project, or wait for the publication of data and resources. But, what would a more open collaborative structure look like {cite:p}`doi.org/10.1038/539159a`? 
+Inspired by the success of endeavours like the [Human Genome Project](https://www.genome.gov/human-genome-project) and [CERN](https://home.cern/), neuroscientists are increasingly initiating large-scale collaborations. The largest efforts, such as the [International Brain Laboratory](https://www.internationalbrainlab.com/) [@doi.org/10.1016/j.neuron.2017.12.013;@doi.org/10.1016/j.conb.2020.10.020], [The Blue Brain Project](https://www.epfl.ch/research/domains/bluebrain/) and [Human Brain Project](https://www.humanbrainproject.eu) bring together tens to hundreds of researchers across multiple laboratories. These projects have generated scientific insights, large-scale datasets, tools and educational materials, and represent a step-change in scale. Organisationally, they follow a formal collaborative model with open outputs. That is, there are participating laboratories who collaborate together and then make their data, methods and results available. As such, to participate, individuals must join a participating laboratory, initiate a collaboration with the project, or wait for the publication of data and resources. But, could there be advantages to an even more open collaborative structure and what might that look like {cite:p}`doi.org/10.1038/539159a`?
 
 One alternative is bench marking contests, in which participants compete to obtain the best score on a specific task. Bench marking contests have driven progress in fields from computer vision {cite:p}`10.1109/CVPR.2009.5206848` to [protein folding](https://predictioncenter.org/), and have begun to enter neuroscience. For example, in [Brain-Score](https://www.brain-score.org/) [@10.1101/407007;@10.1016/j.neuron.2020.07.040] participants submit models, capable of completing a visual processing task, which are then ranked according to a quantitative metric. As participants can compete both remotely and independently, these contests offer a low barrier to entry. However, defining quantifiable endpoints for neuroscientific questions remains challenging {cite:p}`doi:10.1017/S0140525X22002813`.