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

Bridget E Hawkins,Carlos Almeida,Jiapei Chen,J Russell Huie,Adam R Ferguson

doi:10.1089/neu.2018.6192

Abstract

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 Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature).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.

Highlights

Traumatic brain injury (TBI) is a prevalent disorder impacting millions of individuals without a widely accepted therapeutic approach
We focus on the principle that organizing and federating numerous small datasets can produce big data that open new opportunities to apply modern machine learning tools for data-driven discovery
In the study by Haefeli and associates, these analyses revealed precise confidence intervals and effect sizes for therapeutic effects using the full set of long-tail data including both previously published[61] and unpublished data, and demonstrated a reliable effect of neurotrophic agent therapy under certain rehabilitation conditions

Summary

Introduction

Traumatic brain injury (TBI) is a prevalent disorder impacting millions of individuals without a widely accepted therapeutic approach. The bootstrapping approach is a tool that depends on modern computers to assess reproducibility: the pooled population of subjects is randomly subsampled many different times with statistical analysis performed separately on each subsample.[60] In the study by Haefeli and associates, these analyses revealed precise confidence intervals and effect sizes for therapeutic effects using the full set of long-tail data including both previously published[61] and unpublished data, and demonstrated a reliable effect of neurotrophic agent therapy under certain rehabilitation conditions Together these early examples from SCI and TBI demonstrate the potential scientific value of long-tail and dark data and provide a rationale for publishing these data. Incentives for dissemination of longtail and dark data include policy guidance, as well as a system of ‘‘carrots’’ and ‘‘sticks.’’

The FAIR Data Principles As Applied to TBI

Big data options for TBI studies

Findings

Concluding Remarks and Overall Benefits of Data Sharing