Social data collection and analyses

M. Charpentier, M. Pele, J. Renoult, C. Sueur
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引用次数: 1

Abstract

Sampling accurate and quantitative behavioural data requires the description of fine-grained patterns of social relationships and/or spatial associations, which is highly challenging, especially in natural environments. Although behavioural ecologists have tackled systematic studies on animals’ societies since the nineteenth century, new biologging technologies have the potential to revolutionise the sampling of animals’ social relationships. However, the tremendous quantity of data sampled and the diversity of biologgers (such as proximity loggers) currently available that allow the sampling of a large array of biological and physiological data bring new analytical challenges. The high spatiotemporal resolution of data needed when studying social processes, such as disease or information diffusion, requires new analytical tools, such as social network analyses, developed to analyse large data sets. The quantity and quality of the data now available on a large array of social systems bring undiscovered outputs, consistently opening new and exciting research avenues.
社会数据收集和分析
采样准确和定量的行为数据需要描述社会关系和/或空间关联的细粒度模式,这是极具挑战性的,特别是在自然环境中。尽管行为生态学家从19世纪起就开始系统地研究动物社会,但新的生物学技术有可能彻底改变动物社会关系的采样方式。然而,大量的数据采样和多样性的生物学家(如近距离记录仪),目前允许采样大量的生物和生理数据带来新的分析挑战。研究社会过程(如疾病或信息扩散)所需的高时空分辨率数据需要新的分析工具,例如为分析大型数据集而开发的社会网络分析。目前可获得的大量社会系统数据的数量和质量带来了未被发现的产出,不断开辟新的和令人兴奋的研究途径。
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