Beyond the horizon: immersive developments for animal ecology research

Visual Computing for Industry, Biomedicine, and Art

Perspective

Karsten Klein¹  Ying Zhang²  Falk Schreiber³  Kamran Safi⁴ 

[1] Department of Computer and Information Science, University of Konstanz, 78464, Konstanz, Germany;Department of Computer and Information Science, University of Konstanz, 78464, Konstanz, Germany;Department of Migration, Max Planck Institute of Animal Behavior, 78315, Radolfzell, Germany;Department of Computer and Information Science, University of Konstanz, 78464, Konstanz, Germany;Faculty of Information Technologies, Monash University, 3145, Melbourne, VIC, Australia;Department of Migration, Max Planck Institute of Animal Behavior, 78315, Radolfzell, Germany;

关键词: Immersive analytics;    Animal ecology;    Collaboration;    Interactive data visualization;   

DOI  :  10.1186/s42492-023-00138-3

 received in 2022-10-28, accepted in 2023-05-19,  发布年份 2023

来源: Springer

【 摘 要 】

More diverse data on animal ecology are now available. This “data deluge” presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual realityand augmented realitydevices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

Fig. 3

42004_2023_911_Article_IEq042.gif

Fig. 6

42004_2023_911_Article_IEq25.gif