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Visualizing Patterns and Trends in Scientific Literature – What’s next? Chaomei Chen Many of us are interested in visualizing patterns and trends in scientific literature. It can be very exciting and revealing as well as challenging and frustrating. More often than not, a visualized ‘big picture’ of a scientific field invites more questions and more specific needs. Some may want to see more details; others may prefer a birds-eye view. There are quite a few unanswered questions. I’d like to line up a couple of them here. First of all, given any visualization of scientific literature, who would be able to understand what it is about? If there is such a thing as a typical viewer, what would be the viewer’s knowledge structure? The intended audience of the graphical message carried by the Pioneer spacecraft was aliens who would have competent knowledge of physics, at least as the way we understand it. If designers do not spell out their intent, where are the clues?
informationvisualization.typepad.com/sigvis/2005/02/visua...
www.connectedaction.net/2009/03/02/facebook-social-networ...
Here is a good example of an application of Bernie Hogan’s Facebook edgelist extractor. Alan Shussman used it on his own Facebook account and generated the following image: Alan Shussman's personal Facebook egonetwork visualization Alan Shussman's personal Facebook egonetwork visualization Alan used the NetworkX tool and python to build this image of his sub-groups in Facebook. It does work nicely to highlight the life-stage clusters of relationships that mostly stay inward focused, each school or work experience is a set of relationships that mostly link to themselves.
This image created for BU's Deep Vision Display Wall shows a patient's heart (red), an implanted defibrillator (green) and multicolored bands simulating the electric field distribution during a defibrillating shock. Image: Visualization by Raymond Gasser and Daniel Mocanu. Courtesy of the Scientific Computing and Visualization Group, Boston University.
Inspired by the awesome Charting The Beatles project (http://mikemake.com/#72772/Charting-the-Beatles) I made a visualization based on the frequency of initial letters in song titles from the 14 stereo albums.
I is the most frequent, with 32. J is the only letter with exactly one song.
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This shot from below makes the statue take up a majority of the screen space and leaves the viewer with a sense of awe in regards of its power and massiveness.
infosthetics.com/archives/2007/10/skyrails_network_inform...
Notes
a freely available social network visualization system that features a built-in programming language for configuring the visualization attributes of the graph. the added flexibility of the scripting language within Skyrails, accessible through scripting or via menus, allows lay users to change the interface or choose how to represent attributes (i.e. nodes can be bound to planes & spheres based on their attributes). 2 movies demonstrating the smooth dynamic character & interface of the visualization system is available after the break. [link: unsw.edu.au & flickr.com & flickr.com]
flowingdata.com/2008/03/12/17-ways-to-visualize-the-twitt...
and
www.visualcomplexity.com/vc/project_details.cfm?id=594&am...
A way to visualize the Twitter universe. More visualizations on this website.
Akshay Java, from ebiquity group, used the Large Graph Layout (LGL) tool to visualize a large social network on Twitter. The top graph shown here was built using contacts from about 25,000 users. Notice that there is a link connecting two users if either one has the other as a friend and hence it is an undirected graph (of about 250,000 edges). Compare this to the bottom graph that is constructed using only users who are mutually acquainted. i.e. A knows B and also B knows A. As Akshay reveals in his post: "I find that visualizing such large graphs is quite a challenge and to glean meaningful information from it is even more difficult". However, he goes further in explaining that some insights can still be gained from this project. Akshay points out that a number of users seem to be trying to win a popularity contest of some sort, while a number of bloggers and (perhaps fake) celebrity profiles have a huge fan following in Twitter. He also mentions how the two graphs look very different on account of the fact that users with public profiles get a lot of followers whom they might not really know and would hence never add them as an acquaintance. But to really understand what the differences are one would need to look at the community structure and properties of the two graphs. ebiquity group has also explored the Twitter API in other projects [1] [2] in order to get a better understanding of the microblogging trend.
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
from upper-left to bottom right:
start in Safeway parking lot. Slower uphill, then downhill. Slow to make a turn. Slow to pass through intersection, and stop at stop light. Quickly downhill, then slow slightly to move up the bike trail, then stop at Frontseat offices.
The renowned science & technology portal Seed (US) is collaborating with visualizing.org on a collection of outstanding examples of how creatively data can be visually processed to generate really impressive BIG PICTURES.
credit: visualizing.org
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
created by this applet: www.aharef.info/static/htmlgraph/
what the colors mean:
blue: for links (the A tag)
red: for tables (TABLE, TR and TD tags)
green: for the DIV tag
violet: for images (the IMG tag)
yellow: for forms (FORM, INPUT, TEXTAREA, SELECT and OPTION tags)
orange: for linebreaks and blockquotes (BR, P, and BLOCKQUOTE tags)
black: the HTML tag, the root node
gray: all other tags
energy monitoring lamp; if you are using energy efficiently in your home, it fans out, gives more light and is far more attractive. It shrinks if you overuse your appliances and electronic devices.
First Hacks/Hackers Meetup held at Atherton Studio at HPR. Great presentations by Ben Trevino, Jared Kuroiwa and Misa Maruyama.
Julia Kaganskiy (@juliaxgulia) organizes Arts, Culture and Technology meetups in NYC. This event on 27th April 2010 was on Data Mining & Visualization: www.meetup.com/Arts-Culture-and-Technology/calendar/13144...
iSGTW story | Photo courtesy of Argonne National Laboratory.
Scientists Folker Meyer and Elizabeth Glass analyze species and metabolic diversity from soil samples using MG-RAST (Metagenome Rapid Annotation using Subsystem Technology) on an active mural display.