Archived 2015 Data Visualization Work

From PHUSE Wiki
Jump to: navigation, search


Project Title

Data Visualisations - Phase I

Project Team Members

Name Affiliation Role
Michael Rubison Capish Co-Lead
Mary Banach CTSA BERD Co-Lead
Karolyn Kracht Abbvie Co-Lead
Steven Fang Celgene Participant
Bob Friedman Xybion Participant
Prasanna Pointcross Participant
Bereket Tesfaldet FDA Participant
Zachary Skrivanek Eli Lilly Participant
Alastair Clewlow Lundbeck Participant
Pam Howard Icon Participant
Tim Kropp FDA Participant
Kirsten Walther Langendorf SAS Institute Participant

To contact the group, please email Jade Nguyen at, William Frank at, or Crystal Allard at

Affected Stakeholders

For Phase I: Clinical Reviewers and Pharma Physicians

Project Meeting Frequency

The Data Visualizations project team meets on a monthly basis for one (1) hour providing demos of current data visualization/risk based monitoring tools and applications in use.

Data Visualization - A Working Definition

From Wikipedia:

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".[3]

Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.[4]

Well-crafted data visualization helps uncover trends, realize insights, explore sources, and tell stories.[5]

Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.[6]

Free-Form Interactive Exploration: Enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed. This includes an array of visualization options that go beyond those of pie, bar and line charts, including heat and tree maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of it.

The Challenges

Effective Visualization of Clinical data can be challenging for both Pharma and Regulatory Agencies. Though these challenges are generally universal, we have chosen to focus on the unique data visualization difficulties experienced by our team. These include data quality, joining disparate data from separate sources, security and proprietary issues, lack of sufficient tooling, and a general disconnect between the creators, users, and consumers of data.

Our Mission

The Data Visualizations project scope has been revised from the April 2015 new project request submission. As part of the new project objective, persons with specific experience in Data Visualizations and/or risk based monitoring need to be identified prior the review and selection of use cases. The Data Visualizations team will leverage the 2016 PhUSE Computational Science Symposium to acquire persons with the aforementioned experience during the Data Visualizations working group sessions.

Once the Data Visualization project team has identified three (3) uses cases that describe instances in which data visualization was difficult or impossible, the team will answer the following four (4) questions:

1) What can’t be done?

2) Why can’t it be done?

3) What can be done? In other words, what works well?

4) What can’t be done without cumbersome, error-prone, pre-work?

(i.e. What relationships need to exist so that coding doesn’t have to be re-done each time?)