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

Authors :
Chou A
Torres-Espín A
Huie JR
Krukowski K
Lee S
Nolan A
Guglielmetti C
Hawkins BE
Chaumeil MM
Manley GT
Beattie MS
Bresnahan JC
Martone ME
Grethe JS
Rosi S
Ferguson AR
Source :
Neurotrauma reports [Neurotrauma Rep] 2022 Apr 05; Vol. 3 (1), pp. 139-157. Date of Electronic Publication: 2022 Apr 05 (Print Publication: 2022).
Publication Year :
2022

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.<br />Competing Interests: No competing financial interests exist.<br /> (© Austin Chou et al., 2022; Published by Mary Ann Liebert, Inc.)

Details

Language :
English
ISSN :
2689-288X
Volume :
3
Issue :
1
Database :
MEDLINE
Journal :
Neurotrauma reports
Publication Type :
Academic Journal
Accession number :
35403104
Full Text :
https://doi.org/10.1089/neur.2021.0061