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Johannes Waschke, Mario Hlawitschka, Kerim Anlas, Vikas Trivedi, Ingo Roeder, Jan Huisken, and Nico Scherf (*) (MPl for Human Cognitive and Brain Sciences, Leipzig, Germany; HTWK Leipzig, Germany; EMBL Barcelona, Spain; EMBL Heidelberg, Germany; TU Dresden, Germany; Morgridge Institute for Research,Madison,USA)
In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data is often a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise package that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data and enriches them with additional features, such as edge bundling or custom axes and generates an interactive web-based visualisation that can be shared offline and online. As shown in [1] linus facilitates the collaborative discovery of patterns in complex trajectory data. Exemplary visualizations are available by scanning the QR codes in Fig.1 directly or by visiting https://imb-dev.gitlab.io/linus-manuscript/. The linus software including source code and documentation is freely available at our repository at https://gitlab.com/imb-dev/linus.