@article{ref1,
title="Data dissemination: shortening the long tail of traumatic brain injury dark data",
journal="Journal of neurotrauma",
year="2019",
author="Hawkins, Bridget E. and Huie, J. Russell and Almeida, Carlos and Chen, Jiapei and Ferguson, Adam R.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multispecies 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 is 'big' according to the 4v's of volume, velocity, variety or 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 Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ('dark data') and long-tail data (small, specialty TBI datasets undergirding the published literature).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 papers in both the preclinical 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.<p /> <p>Language: en</p>",
language="en",
issn="0897-7151",
doi="10.1089/neu.2018.6192",
url="http://dx.doi.org/10.1089/neu.2018.6192"
}