Visualization Valorization

Tracking #: 436-1416


Christine ChichesterORCID logo

Responsible editor: 

Tobias Kuhn

Submission Type: 

Position Paper


Scientists from diverse backgrounds are joining the field of data science. This leads to advances in data science being actualized in the context of many different domains. Conclusions from datasets using innovative algorithms is one obvious aspect but advances in data science can take on many different forms such as new methods for data interpretation, new data integration and processing technologies, or as will be the topic of this editorial, data visualization techniques. The parity and complementary relationship between all techniques provide ways to improve discovery and should be treated equally in terms of scientific reward. Here, the specific focus is on life science multi-omics data as an example but most of the remarks can be associated with visualization methods in general. From the perspective that visualization serves as an important method for shaping data science interpretations, this paper sets out some of the difficulties encountered in creating and valorizing new visualization implementations for multi-omics datasets.



  • Reviewed

Data repository URLs: 


Date of Submission: 

Monday, March 27, 2017

Date of Decision: 

Friday, April 21, 2017

Nanopublication URLs:



Solicited Reviews: