You’ve asked for it and we listened! Until recently, interactive visualizations on Many Eyes could only be viewed on the site itself. Today we’re launching the ability to embed an interactive visualization from Many Eyes into your own blog, personal webpage or any other page you think makes sense. For example, if I wanted to show beer consumption per capita in the US in an interactive map, I can do so right here in this blog (click the visualization to load the live version).

You can now use visualizations on Many Eyes in much the same way as you use videos from You Tube or images from Flickr. Visualizations can be embedded by clicking the ’share this’ link and copying an html snippet into your site’s HTML code. We provide two preset sizes, but you can set any custom size by modifying width and height parameters in the embedding code.

We are looking forward to seeing how you, our users, use this capability! So go ahead, upload your favorite data, create a visualization and show it on your site or blog. Here are a couple of inspirational samples to get you started:

One of the joys of data analysis is the “aha” feeling when you figure out how different variables interact. Many of the visualizations on Many Eyes are good for inspecting the relationship between numerical variables–but until now there’s been a hole. How do you understand the relation between categories, things like political affiliations or occupations?

To fill this hole, the Matrix Chart was created by our talented intern Lee Byron. (You may already be familiar with his visualizations of music listening on last.fm.) This visualization, shown below, is a flexible and powerful way to make multivariate comparisons. It’s good for data with several non-numeric columns. For example, the matrix chart below shows data on members of the 17th Canadian Parliament, broken down by political party (y axis) and former occupation (x axis).

Here’s a second example, a visualization of NBA jersey colors. Not the most serious visualization on the site, but it does show off color customization, a first for a Many Eyes graphic.

You can read more about the technique here, and we’re collecting examples in a special topic hub, where we’ve put matrix visualizations of some existing data from the site. (One of the nice things about adding a new visualization is that it lets us revisit older data sets with a fresh pair of eyes.) You can leave comments or ideas for new features in the discussion area of the topic hub.

One last note for the technologists in the audience. This is our first experiment with Adobe Flash. We’re interested in your feedback on this as well–we’ll probably be conducting more experiments in the future.

I returned recently from the OECD World Forum on Statistics, Knowledge and Policy in Istanbul. It was invigorating to talk with such an impressive, globe-spanning group. Attendees–who ranged from academic statisticians, to medical researchers, to directors of government ministries–seemed genuinely excited by Many Eyes and its potential to aid policy decisions and educate citizens.

The OECD itself took the lead on using the Many Eyes technology: To coincide with the World Forum, they created an official OECD Topic Hub, with hundreds of datasets from the OECD Factbook 2007. As Enrico Giovannini, Director of Statistics and Chief Statistician of OECD, put it: “Global discussion based on solid statistics allows society to understand its current state and provides a base from which to make progress and innovate. Providing OECD’s 2007 Factbook data to the public offers citizens around the world an opportunity to collectively explore, discuss and tackle the societal challenges of our time.”

Today Many Eyes holds more than 100 visualizations of the OECD data, from oil prices to CO2 emissions. We encourage you to join the topic hub, create new visualizations, and contribute to the dialog around this trove of information. And we hope that other organizations will join the OECD in opening their data to the Many Eyes audience.

New color slider

Our treemaps use color scales to portray numbers–which can be a tricky business. The treemaps make their best guess at the right scale, but sometimes it’s helpful set the color range by hand. Outlying values might “wash out” the rest of the scale, for instance, or for consistency you may want to set several visualizations to use the same color map.

Our color slider lets you make manual adjustments. The colored bar shows the current color map overlaid on the distribution of data values. The numbers indicate the extreme ends of the scale. (If there are values beyond these ends, they are “capped” at the extremes and a ‘+’ or ‘<' will appear next to the values.)
Interaction

Adjust the scale by clicking and dragging the brackets, or simply type directly in the labels next to the scale. Custom values are maintained when the user changes the dimension that is mapped to the color. To restore the initial color map, right-click the color scale.

Sorting

With few exceptions, Many Eyes visualizations have been faithfully showing information in the order it is found in the data set. For example, if a data set about GDP is alphabetically ordered by country name, then the bar for Afghanistan will appear at the beginning, the bar for Zimbabwe at the end. However, as Stephen Few has noted before, sometimes it’s useful to sort items by numerical value (e.g., sorting the bars from lowest-GDP country to highest).For this reason, we’ve added a “sorting widget” to the bar, line, and stack graphs so you can re-order by:

- Labels: alphabetical order (ascending or descending)
- Values: numerical order (ascending or descending)
- Data Order: follows the order of rows in the data set (same order or reversed)

The third sorting option - data order - is often helpful for time series. If your data set has columns that go “backwards” in time (recent to old), toggling the “data order” button will cause your visualization to show time going forward.We hope you enjoy the new features!

Topic hubs* are starting to sizzle on Many Eyes. Users have created dozens of hubs, ranging from music and social networks, to global warming and official OECD data.

User Yevgeny created the Babies topic hub “for anyone trying to see if there is a relationship between the number of births and the lunar cycle…and other such minutia.” Although we have no lunar cycle data yet, users have already started visualizing baby name trends in the Netherlands, Ireland, and Germany. Know of baby name data from other countries? Make sure to contribute to the Babies topic hub!

Ireland aficionado user colm has created the Irish Life hub “to encourage discussions & visualisations of various aspects of Irish life. Any kind of content is welcome - sociological, economical, ecological etc.” The hub is seeing a lot of activity and already counts on a collection of 21 visualizations that span a variety of Irish themes from immigration and general elections to Rugby and James Joyce’s books.

Don’t see a topic hub that interests you? Create your own! Topic hubs are a great way to engage the Many Eyes community into thinking about an issue that is dear to you.

* topic hubs are mini “portal pages” to collect data sets and visualizations on a particular theme.

Since our launch, we’ve been thrilled to see users contribute thousands of data sets and visualizations to Many Eyes. But as we watched the collection grow, we started to realize that we had an embarrassment of riches. With so many things to see and conversations to read, it’s getting to be hard to find your way around the site. One user email put it directly: “Time to organize the visualizations!”

In keeping with the spirit of the site, we’re turning to the collective intelligence of you, our users. Today we’re introducing a suite of features aimed at bringing the Many Eyes community together and letting you contribute to the site structure. We’ll discuss each feature in depth in the coming days, but here are the highlights:

Topic hubs: Now users can create miniature “portal pages” to collect data sets and visualizations on a particular topic or theme.
User profiles: Our old user pages were simply a list of activity. The new ones let you introduce yourself to the other people on the site and send messages to each other.

Ratings: Sometimes we see a great contribution and want to bring it to everyone’s attention. Now you can do that: the “rate it” button that’s underneath each visualization and data set. (If you see a mistake in a data set or misuse of a visualization technique–not that that ever happens –you can give a negative rating.) You can sort visualizations and data sets by rating, so the best ideas float to the top.

Watch lists: If you want to track activity related to a particular set of visualizations or data, use your new watchlist. When you go to your user page, you’ll see a single organized list of all the latest news.

There are many other new features and bug fixes, as well. Take the new site for a spin and tell us what you think!

Information visualization (infovis) has always been something that is done by experts for experts. Think scientists in white lab coats pouring over visualizations of complex data sets in order to further human knowledge. You might also be reminded of economists and financial gurus forever hypnotized by the glow of monitors showing esoteric visualizations of streaming financial data.

But in the last few years there have been a couple of occasions when infovis has dabbed into the public arena to help spark debate and insight in exciting ways. Take, for example, the series of maps of the US that circulated on the Internet and in the media showing “red” and “blue” states during the last presidential election:

As this page from the University of Michigan shows, the first map that circulated showed each state colored in either red or blue depending on whether a majority of voters voted Republican or Democratic. It was a fine representation of the country but one that gave the superficial impression that the “red states” dominated the picture, since they covered far more area than the blue ones. Quickly afterwards, another map came along showing voting patterns in counties instead of states and coloring each county based on percentage of votes in order to represent results more accurately. Finally, a third map distorted the size of counties based on population count (a technique also known as a cartogram), which better represented the high concentration of people in big cities.

In short, the election maps got progressively more sophisticated as people tried to understand voting results. They also illustrated the fact that there are multiple ways of telling the same story. The maps became an essential part of a national debate on politics, a divided country, and what it means to represent complex data.

We believe this kind of collective sensemaking is not an isolated phenomenon, but rather, an exciting example of social data analysis around visualization. This is precisely our intent in having created Many Eyes: to enable people to collectively reason about the trends and patterns they see on the vivid representations of data called visualizations.

We invite you to visit Many Eyes, play with the visualizations, upload data, share your perspective, and get conversations started. Already we are seeing users discuss a plethora of topics ranging from McDonalds nutrition data, to bioinformatics, to the bible.

What’s in your data set?