Purpose of review:The field of neurotrauma research faces a reproducibility crisis. In response, research leaders in traumatic brain injury (TBI) and spinal cord injury (SCI) are leveraging data curation and analytics methods to encourage transparency, and improve the rigor and reproducibility. Here we review the current challenges and opportunities that come from efforts to transform neurotrauma's big data to knowledge.Recent Findings: Three parallel movements are driving data-driven-discovery in neurotrauma. First, large multicenter consortia are collecting large quantities of neurotrauma data, refining common data elements (CDEs) that can be used across studies. Investigators are now testing the validity of CDEs in diverse research settings. Second, data sharing initiatives are working to make neurotrauma data findable, accessible, interoperable and reusable (FAIR). These efforts are reflected by recent open data repository projects for preclinical and clinical neurotrauma. Third, machine learning analytics are allowing researchers to uncover novel data-driven-hypotheses and test new therapeutics in multidimensional outcome space. Summary:We are on the threshold of a new era in data collection, curation, and analysis. The next phase of big data in neurotrauma research will require responsible data stewardship, a culture of data-sharing, and the illumination of 'dark data'.

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are ''big'' according to the ''4Vs'' of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data (''dark data'') and ''long-tail data'' (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

Background:Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.Methods:Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.Results:Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery.Conclusions:We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.Funding:NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).