Department of Biology and Ecology of Fishes, Leibniz Institute of Freshwater Ecology and Inland Fisheries, 12587 Berlin, Germany.
Trends Ecol Evol. 2013 Sep;28(9):541-51. doi: 10.1016/j.tree.2013.06.002. Epub 2013 Jul 13.
The increasing miniaturisation of animal-tracking technology has made it possible to gather exceptionally detailed machine-sensed data on the social dynamics of almost entire populations of individuals, in both terrestrial and aquatic study systems. Here, we review important issues concerning the collection of such data, and their processing and analysis, to identify the most promising approaches in the emerging field of 'reality mining'. Automated technologies can provide data sensing at time intervals small enough to close the gap between social patterns and their underlying processes, providing insights into how social structures arise and change dynamically over different timescales. Especially in conjunction with experimental manipulations, reality mining promises significant advances in basic and applied research on animal social systems.
动物追踪技术的日益微型化使得人们有可能收集到关于陆地和水生研究系统中几乎整个个体群体的社会动态的极其详细的机器感知数据。在这里，我们回顾了收集此类数据及其处理和分析的重要问题，以确定“现实挖掘”这一新兴领域中最有前途的方法。自动化技术可以提供足够小的时间间隔的数据感应，以缩小社会模式与其潜在过程之间的差距，从而深入了解社会结构如何在不同时间尺度上动态出现和变化。特别是结合实验操作，现实挖掘有望在动物社会系统的基础和应用研究方面取得重大进展。
