EDITORIAL Big Data ana

that are generally applied in the literature. Instead, the real issue should be:opposite to what should be done. The pilot responded, but too late, and the plane crashed into the ocean killing all 228 people onboard. Simply, the co-pilot was lulled into compla-cency by dependence on an automated process. In Big Data biology we may have been similarly lulled by the plug and play analyticalOpen Access lysis on autopilot? Moore* Biomedical sciences, especially fields such as genomics, are becoming Big Data fields, driven, to a large extent, simply by the ability to generate enormous data sets. For fields such as biology, where data has traditionally been small, the influx of Big Data, noise and all, has caused a need to rapidly shift research practices. Ways of dealing with these data have led to importing collaborators from statistics, computer science, and physics. However, these new “biologists ” are simply not biologists and classically trained biologists are not “Big Data researchers”. The confluence of these fields unfor-tunately has not led to the improvement of biological understanding via what should be synergy, but to a completely unfortunate shifted landscape of research. The ne