Author: Megan PirrungPublisher:
ISBN:
Category :
Languages : en
Pages : 135
Book Description
Sequencing technologies become cheaper and produce vast amounts of data in many biomedical domains, including in the field of microbial ecology. However, the software and analytical tools available to microbial ecologists are struggling to meet the need demanded by these complex datasets. Scientists use visualization to both support data analysis and to communicate results to scientific and public audiences. Biological data analysis software is often written by scientists who have limited background and experience in software engineering and design of interactive tools, which often results in unstable and ineffective products. To address this problem in the domain of microbial ecology we have developed TopiaryExplorer and EMPeror through a user-centered design process and utilized proper software development practices. Both tools provide an intuitive user interface and use informed visual data representations. We demonstrate the effectiveness of these tools through case studies of real analysis workflows. The tools have proven useful for scientific data analysis and communication. To communicate results scientists often rely on complex graphics for static publication, rather than dynamic data exploration tools. It is unclear how effective these types of visualizations are for communicating microbial ecology data to different audiences. To examine this question we designed an experiment for determining efficacy of different visualizations. We surveyed three different microbial ecology data types: abundance, phylogeny and multidimensional. The results from the experiment show that visualization choice has the most significant effect on understanding of microbial ecology data, but microbial ecology experience as well as previous experience with visualization also have significant effects. These results can be used to better inform scientific communication through visualization. The experiment itself provides a framework for conducting similar experiments with different types of underlying data and visual representations.
