PSI ToxSIG Webinar: Beyond the looking glass - Interpreting animal welfare & behaviour by monitoring & assessing mice activity data
Analysing continuously collected locomotive activity data to interpret mice welfare and behaviour.
The field of data visualization is the intersection of data journalism, statistics, graphic design, computer science, neuroscience, and cognitive psychology. We will focus on the latter two fields of data visualization as it relates to data visualization: neuroscience and cognitive psychology.
Date: Wednesday 28th April 2021
Time: 16:00 - 17:00 GMT
Speakers: Zachary Skrivanek, (Lilly)
Who is this event intended for? This event is intended for people who create data visualizations as well as customers who work with data visualization developers to make their data come to life.
What is the benefit of attending? This is a fun event where the audience will participate and become part of an experiment using polls and quick exposure to images (~250 milliseconds) to validate some of the principles taught in this course. This event will benefit data visualization developers and customers by teaching them how to leverage gestalt principles and pre-attentive processing in selecting aesthetics to maximize the effectiveness of their data visualizations.
You can now register for this event. This event is free of charge to both Members and Non-Members of PSI.
To register for the session, please click here.
The visual cortex can recognize certain “targets” and “borders”, based on variations in visual cues such as shape and color, within 250 milliseconds; this is called pre-attentive processing. This is faster than it takes to become conscious of the image. When combining visual cues, conjunctive visual cues, the pre-attentive qualities are generally lost. We will illustrate these concepts through an empirical experiment with the audience. The audience will be expected to participate and identify targets and borders within 250 milliseconds. We will cover what types of visual cues are conducive to pre-attentive processing and how to incorporate these concepts in your data visualizations. Similarly, the study of gestalt principles from psychology, seeing meaning in a purposeful arrangement of design elements, can be leveraged for effective data visualizations. We will also discuss the hierarchies of perception and how this applies to data visualization.
Dr. Skrivanek graduated with a Ph.D. in biostatistics from Ohio State University and a B.S. from Cornell University, where he studied exploratory data analysis under Professor Velleman, a protégé of John W. Tukey, who invented a number of statistical graphics including the box plot. He joined Eli Lilly in 2002 where he contributed to the development of endocrine-related medicines and related biomarkers in early clinical phase drug development. He later transitioned to a product team in late phase clinical development as the lead statistician and developed and successfully implemented an innovative Bayesian adaptive, seamless phase 2/3 study which selected the doses for the entire program utilizing a clinical utility index. Dr. Skrivanek heavily leveraged data visualization to communicate the operating characteristics of the design as well as the results of the study.
He is currently leading an effort to make visual analytics and good data visualization practices in general an integral part of drug development at Eli Lilly and the industry in general. He is involved in a number of external collaborations focused on advancing drug development through visual analytics including co-leading a subproject in PHUSE, on “Interactive Data Visualizations for Decision Making in Submissions”, and contributing to an ASA-DIA working group on interactive safety graphics and an organizing member on a PSI (Statistics in the Pharmaceutical Industry) Special Interest Group (SIG) which hosts a monthly event, “Wonderful Wednesdays” where members are given data visualization challenges that they must solve for the following month and the solutions are critiqued by the panel based on good data visualization principles.