Traumatic brain injury (TBI) is a major worldwide public health problem. Despite the extensive preclinical research dedicated to deciphering the underlying pathophysiology and developing therapeutics, successful translation from bench to bedside remains elusive. This is largely due to the heterogeneity of clinical TBI and corresponding overwhelming variety of preclinical studies aiming to recapitulate TBI biology. We introduce the Open Data Commons for Traumatic Brain Injury (ODC-TBI.org): a novel community-driven web platform and repository developed for data publication and citation with persistent digital object identifiers (DOIs), data element harmonization, and FAIR data sharing. ODC-TBI specifically enables data sharing at the level of individual subjects which empowers data reuse for big data and machine learning analytics. We demonstrate two use cases with data aggregated from the ODC-TBI. First, we performed descriptive analytics of subject-level data from 12 publications across diverse preclinical TBI modalities (N = 1250 subjects). Second, we implemented a multidimensional analytic workflow with unsupervised machine learning (principal component analysis) to identify persistent inflammatory patterns across three preclinical TBI experiments. The analysis ultimately improved the sensitivity for detecting biological effects while mitigating the spuriousness of multiple univariate comparisons. Altogether, ODC-TBI will address the demand for data publication from scientific funders and publishers, improve reproducibility of preclinical TBI research, produce new scientific work products for researchers in the form of citable datasets, and expand avenues for novel research and collaboration.