How exploring Python can level up your data visualization

Data visualization, i.e., the graphical representation of data, is a vital skill for every scientist to develop – aiding with the interpretation of data and providing an accessible way to communicate these data with others. In the scientific world, data visualization is used to produce eye-catching figures to share results with peers and the wider community. While these visualizations are achievable using no coding, they can be restricted by the dataset size, plotting style and overall cost of the software. Learning to code solves many of these issues and while the learning curve remains a barrier to use, programming is becoming a must-have skill in many fields. Python is one of the world’s most popular programming languages and is at the forefront of data analysis and visualization, producing clear, engaging and reproducible figures in all manner of styles. As biological datasets increase in size and number, the reproducibility and flexibility of Python result in an invaluable tool for scientific data visualization. This article will introduce the use of Python for data visualization and provide some guidance on how to get started.

Narrative Scientific Data Visualization in an Immersive Environment

Motivation Narrative visualization for scientific data explorations can help users better understand the domain knowledge, because narrative visualizations often present a sequence of facts and observations linked together by a unifying theme or argument. Narrative visualization in immersive environments can provide users with an intuitive experience to interactively explore the scientific data, because immersive environments provide a brand new strategy for interactive scientific data visualization and exploration. However, it is challenging to develop narrative scientific visualization in immersive environments. In this paper, we propose an immersive narrative visualization tool to create and customize scientific data explorations for ordinary users with little knowledge about programming on scientific visualization, They are allowed to define POIs (point of interests) conveniently by the handler of an immersive device. Results Automatic exploration animations with narrative annotations can be generated by the gradual transitions between consecutive POI pairs. Besides, interactive slicing can be also controlled by device handler. Evaluations including user study and case study are designed and conducted to show the usability and effectiveness of the proposed tool. Availability Related information can be accessed at: https://dabigtou.github.io/richenliu/