Elite athletics, especially at the collegiate level, is a valuable industry in the States. The Division-1 American football and Basketball tournaments generate billions in revenue and draw in multi-millions to view their broadcasts. For most of these athletes, 14+ years of their lives have been dedicated to perfecting their minds and bodies to reach the pinnacle of their sport.

The demands placed on these elite athletes can provide a wealth of benefits, including improved cognition, well-being, and even increased integrity of brain tissue structure. However, the negative impacts on brain tissue structure may be just as prevalent but harder to detect. This is most prevalent in collision-sports athletes (eg. American football and ice hockey), where recent research has reported potentially damaging impacts of repetitive head impacts on brain tissue, which may last for years and contribute to later neurodegenerative disorders.

To fully understand the positive and negative effects of participating in athletics, science needs high-quality, openly shared datasets from elite athletes across the sports world broadly in addition to well-matched non-athletes. Despite recent efforts from multi-center concussion/mTBI focused studies, eg trackTBI, ENIGMA, and the CARE Consortium, there is almost no easily accessible data that can be openly used to separate detrimental and beneficial effects of sports. 

Even if there were a plethora of open data available, data is not enough. We need the resources and knowledge to implement useful science with data that is both reliable and reproducible. This is a much larger and more resource-consuming issue than just data availability, as noted by the current reproduction crises in psychology and neuroscience and the increased interest in Big Data analytics.

To address many of these issues, the National Science Foundation and Microsoft have supported developing an innovative cloud-resource called brainlife.io. This resource allows for storing and processing brain data efficiently, reliably, and reproducibly. By connecting large-scale, publicly funded and private computing resources brainlife.io lowers entry barriers to effective brain data analysis. 

We combined this innovative technology with a data collection effort from varsity football players (collision), cross-country runners (non-collision), and age-matched non-athlete students at Indiana University. Our efforts have resulted in nearly 700GB worth of data and derivatives spanning magnetic resonance images, tractography, cortical maps, and structural networks (https://doi.org/10.25663/brainlife.pub.14). The cutting-edge analysis pipelines implemented for this effort and the data have been thoroughly checked to assure the published data’s highest quality. We are excited about this effort and hope that other researchers will use it to accelerate discovery, and understanding of the effects of athletics on brain tissue.