We recently began designing a page to display individual summary data. This page should show a research participant’s scores on potentially dozens of measures, from the ADOS, to medical history data, to IQ. Our goal is to support clinicians conducting a case review.
When thinking about the best way to display a large and diverse array of data points, we recalled something we’d read on Edward Tufte’s blog:

Some useful metaphors for data displays in business monitoring systems include the tables on the sports data page, the weather page, and the financial page of a good newspaper. There, readers quickly look through large data sets in order to examine selected tables of data. The selection of the tables to look at is more in the hands of the viewers and less in the hands of the editors . . .

Millions of people everyday select and read the sports data tables and the weather pages found in newspapers; business executives can also work at that level of resolution. Also the sports and weather (and stock market tables) have a straightforward workaday undesigned quality.

Here’s an example of what he’s referring to: this is a photo of today’s sports page in the New York Times:


Medical Research Database Information Design



The New York Times “scoreboard” has a tremendous amount of data, and it’s well organized so that readers can quickly find what they’re interested in. For example, we’d typically be looking for the Cubs score, and we know to locate it by finding “Baseball” then looking for a box score with “Cubs” in the header. We learn quickly that they lost (surprise!), and then decide not to read any further.
We decided to try an approach to research summary data that is similar. The display should be data-dense, with headers and sub-headers that make it easy to scan. The viewer should be able to quickly get a sense of the available summary data, and then to zoom in on a particular set if desired.
This is still a work in progress, but here’s a look at one of our initial mockups:

Medical Research Database Information Design


We’d love to hear your ideas about effective ways to organize large amounts of summary data.