Data navigation has few standard experiences. Generally, you browse data in list or table formats. Rows, columns, filters, ascending, descending, bullets, margin, zebra stripes, borders—you know the drill.

Part of the interest behind the Nodal project was to push our understanding of how to browse data. We built it under the simple question, “What if data could be more interactive?”

The D3 JavaScript library and work that Mike Bostock has been doing is truly inspiring. If you don’t know Mike, he splits time at Square, one of the most admirable growth startups in recent years, and the NY Times. He is responsible for many of the interactive graphs you see on nytimes.com every week. My favorite was this one leading up to the election.

Matt, Jesse and I looked through the type of graphs D3 provides out of the box, as Nodal was to be a Hello World experiment. We stumbled upon an example of a node graph that used physics and thought What if these nodes were people? We brainstormed until we landed on the idea of letting GitHub users explore their network graph.

This is not novel, we acknowledge that. It’s nothing new. But what comes next became intriguing to us.

See, when you have hundreds of nodes representing people and connect them based on a relationship, filters become increasingly more powerful.

For example, let’s say I’m looking at a graph of 1,000 StackOverflow users. I want to filter to just those who are considered Python experts. Trivial, I know, but seeing the results in an interactive network graph compared to a table is a fascinatingly different experience because interactivity is richer. Now let’s say I’m looking at that grouping of Python experts. I find a few I want to contact and drag their node over to a side. When I have my group, I simply drag to select them and cast a command via a context menu.

In the end, Nodal is just a simple experiment that isn’t anything too mind-blowing. But it sparked curiosity. What interface innovations can be done to make navigating a data set more intuitive? Is there a framework that can work across any type of data set? Which heuristics are better than others? And so on.

We are thinking of expanding Nodal to different social networks and types of data sets. Each time we anticipate learning something new.