Automated systems for high-input data collection and data storage have led to exponential growth in the availability of information. Such datasets and the tools applied to them have been referred to as ‘big data’. Starting with a systematic review of the terms ‘informatics, bioinformatics and big data’ in animal health this special issue of AHRR illustrates some big-data applications with papers on how the use of various omics methods may be used to facilitate the development of improved diagnostics, therapeutics, and vaccines for foodborne pathogens in poultry and on how a better understanding of rumen microbiota could lead to improved feed absorption while minimizing methane production. Other papers in this issue cover the use of big data modeling in dairy cattle for more effective disease interventions and machine learning tools for livestock breeding. The final two reviews describe the use of big data in better vector-borne pathogen forecasts with canine seroprevalence maps and modeling approaches to understand the transmission of avian influenza virus. Although a lot of technical and ethical issues remain with the use of big data, these reviews illustrate the tremendous potential that big-data systems have to revolutionize animal health research.

中文翻译：

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动物健康研究中的“大数据”——机遇与挑战

用于高输入数据收集和数据存储的自动化系统已导致信息可用性呈指数级增长。此类数据集和应用于它们的工具被称为“大数据”。从对动物健康中“信息学、生物信息学和大数据”的系统回顾开始，本期 AHRR 特刊通过论文阐述了一些大数据应用，阐述了如何使用各种组学方法来促进改进诊断的开发，家禽食源性病原体的治疗方法和疫苗，以及如何更好地了解瘤胃微生物群可以改善饲料吸收，同时最大限度地减少甲烷产生。本期的其他论文涵盖了在奶牛中使用大数据建模来进行更有效的疾病干预和用于畜牧业的机器学习工具。最后两篇综述描述了大数据在更好的病媒传播病原体预测中的应用，包括犬血清流行率图和建模方法，以了解禽流感病毒的传播。尽管使用大数据仍存在许多技术和伦理问题，但这些评论说明了大数据系统必须彻底改变动物健康研究的巨大潜力。