Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data

• Published in: Journal of neurotrauma Vol. 37; no. 22; pp. 2414 - 2423
• Main Authors: Hawkins, Bridget E, Huie, J Russell, Almeida, Carlos, Chen, Jiapei, Ferguson, Adam R
• Format: Journal Article
• Language: English
• Published: United States Mary Ann Liebert, Inc 15.11.2020; Mary Ann Liebert, Inc., publishers
• Subjects: Big Data; Brain research; Data collection; Data dictionaries; Datasets; Gene expression; Genomics; Learning algorithms; Machine learning; Neuroimaging; Original; Reviews; Social networks; Translation; Traumatic brain injury; Velocity; United States > US; analytics; traumatic brain injury; FAIR principles; big data; data sharing
• ISSN: 0897-7151; 1557-9042
• DOI: 10.1089/neu.2018.6192

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 , tracing its origins in Internet technology as data that are "big" according to the "4Vs" of , , 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 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 Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature).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.