The complex and heterogeneous nature of spinal cord injury (SCI) has limited translational bench-to-bedside results. The wide variety of data collected on a single SCI subject, including injury parameters, biochemical, histological, and behavioral outcome measures represent a ‘big data’ problem, calling for modern data science solutions. To facilitate data sharing within and between labs, we have developed the Open Data Commons for Spinal Cord Injury (odc-sci.org) where SCI data is made FAIR (findable, accessible, interoperable and reusable) and provide mechanisms for both data management and open data publication and citation. However, there are instances in which SCI researchers collect sensitive data that needs to remain private, such as datasets designed to meet regulatory approval (e.g. FDA), sensitive intellectual property, and non-human primate studies, among others. We have addressed this need with the development of a Private Data Commons for SCI (PDC-SCI). This private infrastructure is ideal for multi-lab transdisciplinary studies that require a well-organized, scalable data commons for rapid data sharing within a closed, distributed team. Here we demonstrate the VA Gordon Mansfield SCI Consortium as a use case for the PDC-SCI, in which multimodal data from behavior, biomechanics of injury, hospital records, imaging, and histology are integrated, shared, and analyzed to facilitate insights and knowledge discovery.

Support from Craig H. Neilsen Foundation, Wings for Life Foundation, International Spinal Research Trust, Rick Hansen Institute, International Neuroinformatics Coordinating Center (INCF), UCSF-BASIC, the US Department of Veterans Affairs (I01RX002245-01, I01RX002787).