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.
%0 Journal Article
%1 Waschke2021-di
%A Waschke, Johannes
%A Hlawitschka, Mario
%A Anlas, Kerim
%A Trivedi, Vikas
%A Roeder, Ingo
%A Huisken, Jan
%A Scherf, Nico
%D 2021
%I Public Library of Science (PLoS)
%J PLoS Comput. Biol.
%K
%N 11
%P e1009503
%T linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser
%V 17
%X 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.
@article{Waschke2021-di,
abstract = {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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Waschke, Johannes and Hlawitschka, Mario and Anlas, Kerim and Trivedi, Vikas and Roeder, Ingo and Huisken, Jan and Scherf, Nico},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/29b773286fcb17fd4874f81a0b3157bb6/scadsfct},
copyright = {http://creativecommons.org/licenses/by/4.0/},
interhash = {0d424a52fb2625490c547513e749dca4},
intrahash = {9b773286fcb17fd4874f81a0b3157bb6},
journal = {PLoS Comput. Biol.},
keywords = {},
language = {en},
month = nov,
number = 11,
pages = {e1009503},
publisher = {Public Library of Science (PLoS)},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser},
volume = 17,
year = 2021
}
