Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research

• Published in: Neurotrauma reports 2022-04, Vol.3 (1), p.139-157
• Main Authors: Chou, Austin, Torres-Espín, Abel, Huie, J Russell, Krukowski, Karen, Lee, Sangmi, Nolan, Amber, Guglielmetti, Caroline, Hawkins, Bridget E, Chaumeil, Myriam M, Manley, Geoffrey T, Beattie, Michael S, Bresnahan, Jacqueline C, Martone, Maryann E, Grethe, Jeffrey S, Rosi, Susanna, Ferguson, Adam R
• Format: Article
• Language: English
• Subjects: data sharing; FAIR principles; multi-variate analysis; Open Data Commons; Original; Original Article; principal component analysis; traumatic brain Injury
• ISSN: 2689-288X; 2689-288X
• DOI: 10.1089/neur.2021.0061

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.