探索身临其境环境中的动物行为多层网络--一个概念框架。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Integrative Bioinformatics Pub Date : 2024-07-24 DOI:10.1515/jib-2024-0022
Stefan Paul Feyer, Bruno Pinaud, Karsten Klein, Etienne Lein, Falk Schreiber
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引用次数: 0

摘要

动物行为通常被模拟为网络,例如,节点是一个群体中的个体,边代表这个群体中的行为。然后,不同类型的行为或行为类别被模拟为不同但相互连接的网络,形成一个多层网络。最近的发展显示了多层网络在动物行为研究方面的潜力和益处,以及立体三维沉浸式环境在交互式可视化、探索和分析动物行为多层网络方面的潜在益处。然而,迄今为止,动物行为研究主要由二维桌面上的图书馆或软件提供支持。在此,我们探讨了特定领域对(立体)三维环境的要求。基于这些要求,我们提供了在沉浸式环境中可视化、探索和分析动物行为多层网络的概念验证。
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Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework.

Animal behaviour is often modelled as networks, where, for example, the nodes are individuals of a group and the edges represent behaviour within this group. Different types of behaviours or behavioural categories are then modelled as different yet connected networks which form a multilayer network. Recent developments show the potential and benefit of multilayer networks for animal behaviour research as well as the potential benefit of stereoscopic 3D immersive environments for the interactive visualisation, exploration and analysis of animal behaviour multilayer networks. However, so far animal behaviour research is mainly supported by libraries or software on 2D desktops. Here, we explore the domain-specific requirements for (stereoscopic) 3D environments. Based on those requirements, we provide a proof of concept to visualise, explore and analyse animal behaviour multilayer networks in immersive environments.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
期刊最新文献
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