Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information.Upon data acquisition...Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information.Upon data acquisition,one major hurdle is the subsequent interpretation and visualization of the datasets acquired.To address this challenge,VR-Cardiomics is presented,which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets.By implementing the system in two separate immersive environments,fish tank virtual reality(FTVR)and head-mounted display virtual reality(HMD-VR),biologists can interact with the data in novel ways not previously possible,such as visually exploring the gene expression patterns of an organ,and comparing genes based on their 3D expression profiles.Further,a biologist-driven use-case is presented,in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.展开更多
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 holist...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 reality and augmented reality devices,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.展开更多
For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have ...For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks.In both bodies of literature,networks are frequently referred to as being‘large’or‘complex’,yet these terms are relative.From a human-centred,experiment point-of-view,what constitutes‘large’(for example)depends on several factors,such as data complexity,visual complexity,and the technology used.In this paper,we survey the literature on human-centred experiments to understand how,in practice,different features and characteristics of node–link diagrams affect visual complexity.展开更多
基金This project was partly funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-Project-ID 251654672-TRR 161by DFG Center of Excellence 2117“Centre for the Advanced Study of Collective Behaviour”(ID 422037984)M.R.was funded by an NH&MRC/Heart Foundation Career Development Fellowship and by an Australian Research Council Discovery Project DP190102771 Grant.
文摘Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information.Upon data acquisition,one major hurdle is the subsequent interpretation and visualization of the datasets acquired.To address this challenge,VR-Cardiomics is presented,which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets.By implementing the system in two separate immersive environments,fish tank virtual reality(FTVR)and head-mounted display virtual reality(HMD-VR),biologists can interact with the data in novel ways not previously possible,such as visually exploring the gene expression patterns of an organ,and comparing genes based on their 3D expression profiles.Further,a biologist-driven use-case is presented,in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.
文摘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 reality and augmented reality devices,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.
基金This survey began as part of a working group output of the NII Shonan Seminar No.2015-1 Big Graph Drawing:Metrics and Methods,and we would like to thank this seminar series for the role it played in this surveyWe would like to thank Tamara Munzner for her ideas and feedback at this seminar which helped focus the topic of this paper.The second author would like to thank EPSRC First Grant EP/N005724/1+1 种基金The last author would like to thank the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 747985This work was supported by the Australian Research Council Discovery Project grant DP140100077.
文摘For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks.In both bodies of literature,networks are frequently referred to as being‘large’or‘complex’,yet these terms are relative.From a human-centred,experiment point-of-view,what constitutes‘large’(for example)depends on several factors,such as data complexity,visual complexity,and the technology used.In this paper,we survey the literature on human-centred experiments to understand how,in practice,different features and characteristics of node–link diagrams affect visual complexity.
