Q-tools The list below attempts to define a set of “Q-tools” that may be used to generate, sort, classify and perform operations on information. This is not intended to be an exhaustive list, but more of a starting point for discussion. I have also added some alternative names for each Q-tool. PrismA prism is a question that divides information into smaller groups. The purpose of a prism is to break down information into categories or subgroups. An example might be “What are the parts of this system?” Prisms are used extensively in scientific inquiry. They are also used in organization design to map the departments and sub-departments of a company. An example question used in this activity might be “What roles are required to deliver this functionality?” To create a prism, define a question that can be used to divide a unit of information into its constituent parts. Alternative names: Divider, separator, splitter, brancher.
with TwitterFriends you can ... * find out the hidden network of Twitter contacts that are really relevant for you. * visualize the network of your relevant contacts and their contacts * see who of your Twitter friends are online this very moment * read some stats about your Twitter account * take a look at the most conversational Twitterers or those who are posting the most links To see your relevant network and some stats about your tweeting behavior compared to other Twitter users, just enter your (or another) Twitter username: * Darren Rowse of Problogger Blog Tips wrote a nice review on TwitTip and calls TwitterFriends a "great Twitter statistics tool". Thanks, Darren! * Jason Annas even created a video explaining TwitterFriends. I think this is a great introduction to the tool, but see for yourself:
Here’s a visualization concept I came up with a while back to look at the way search engines and word-of-mouth affects hit frequency on the iBiblio web-traffic log. iBiblio consists of around 420 sites. Each one of the circles you see represents one of the websites. The size of each pie slice inside grows with respect to the number of hits by individual search engines (see the legend for which ones). The size of the circle grows with respect to the overall number of hits by people other than search engines. Hits are counted by number of unique incoming IP addresses per day. Links get drawn between cliques of websites where more than 1/4th of the unique IP addresses are the same on that day, meaning, more or less, that those sites often share traffic. The total amount of data was around 10TB and the visualization took about a day to process into a static animation. The original is meant to run on a wall-sized (16′x9′) or on our specialized visualization dome.