Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Faculty of Computer Science and Media, Leipzig University of Applied Sciences, Leipzig, Germany.
PLoS Comput Biol. 2021 Nov 1;17(11):e1009503. doi: 10.1371/journal.pcbi.1009503. eCollection 2021 Nov.
In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet 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, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.
在生物学中,我们经常会遇到信息丰富、大规模的轨迹数据,但探索和交流此类数据中的模式可能是一项繁琐的任务。理想情况下,数据应该用一个交互式的可视化界面来包装,以便于创建和测试协作假设。为了解决这些挑战,我们开发了一个工具 linus,它使得探索和共享 3D 轨迹的过程变得像浏览网站一样简单。我们提供了一个 Python 脚本,该脚本可以读取轨迹数据,并用附加功能(如边缘捆绑或自定义坐标轴)丰富它们,并生成一个可以在线共享的交互式基于网络的可视化界面。linus 促进了复杂轨迹数据中模式的协作发现。