Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research
Austin Chou, Abel Torres‐Espín, J. Russell Huie, Karen Krukowski, Sangmi Lee, Amber Nolan, Caroline Guglielmetti, Bridget E. Hawkins, Myriam M. Chaumeil, Geoffrey T. Manley, Michael S. Beattie, Jacqueline C. Bresnahan, Maryann E. Martone, Jeffrey S. Grethe, Susanna Rosi, Adam R. Ferguson
Abstract
Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles ( N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.