Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research - Austin Chou; Abel Torres-Esp?n; 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; et al. (Neurotrauma Reports, 2022)
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.