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via Free Technology for Teachers by (Mr. Byrne) on 3/9/11

JGraph is a UK company that develops and supports graph visualization software and web services. One of the free services they offer is a diagram creation tool called offers a drag and drop interface for creating diagrams using clip art and pre-drawn shapes. Using the service does not require registration and all of your diagrams can be saved to your local computer in your choice of four formats (xml, png, jpg, or svg).

Applications for Education could be a good tool for students to use to create flowcharts of a process or concept. Students could also use to create mindmaps that use images instead of just words and lines. As registration is not required in order to use the service you can have students using it quickly without losing instructional time to walking students through a registration process.


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via information aesthetics on 03/03/11

Social Compare [] sees a conceptual gap between social networking and visual size comparison. The online tool enables everyone to easily compare the size of all sorts of artefacts, like objects (e.g. iPad vs. iPad 2), persons (e.g. Obama vs. Sarkozy), countries (e.g. USA vs. France) and the like. All visual comparisons can be saved to be shared on social networks or be embedded directly on blog post or website. In addition, size "tables" allow comparison of things that have no visual counterpart, such as Internet browsers, car performance statistics or sports and activities.

Ultimately, Social Compare believes it can become an new social community to create and share easily interactive and collaborative comparison tables. More information also watchable in a short introduction movie below.

See also Sizeasy - Visual Size Comparison


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Find Great Data Visualizations at " is a community site for sharing data visualizations (aka infographics). Anyone can upload their data visualizations to the public gallery. The public gallery is divided into four categories; economy, environment, health, and energy. Visualizations in the gallery can be downloaded, printed, and or embedded into your blog or website. Some of the visualizations in the gallery, such as this one embedded below, are interactive.

National obesity comparison tool <br /><a href="#"><img alt="National obesity comparison tool " src="" height="100%" /></a>

Applications for Education could be a good place to find infographics to use at the start of a research activity. For example, take the infographic embedded above and ask students to research the causes of and impact of high obesity rates on communities.


Let's Debate the Issue of Aesthetics in Data Visualization... on Television: "


BBC television seems to have embraced informing people of the power (and dangers) of infographics. Several months before Hans Rosling's television documentary 'The Joy of Stats', they even took up data visualization and infographics as a subject of intense debate. More specifically, on a episode of News Night, Information is Beautiful author David McCandless dueled with 'Anti Design' initiator Neville Brody, a 'legendary designer who is the original art director of The Face'.

Interestingly, the actual discussion topic quickly focused on the potential misuse of beauty in data visualization, which ultimately might make them 'too mesmerizing, too beguiling, too pretty' (I confess, I had to look the 2nd verb up). Without much consideration, the moderator put up several infographics of one of the two guests and invited the other one to vent some critiques. What started off with a friendly 'Congratulations David! I would like that on my wall!' quickly shifted into an intellectual argument that nailed the work as the epitome of what should not come out after '25 years of Thatcher locking up culture'. While no-one took the trouble of asking what actually should come out instead, the moderator was quick to remark: 'Are you more coffee table graphics?'

So, in short, if you want to see the utter surprise when a talented and acclaimed information designer is so openly criticized on national television, then watch the movie below.

What should David McCandless have answered instead?

Here is David's own take, as he recently mentioned in an interesting interview at Visualising Data: 'I forgot how TV journalism reduces debate down to two opposing polarities: for and against. Which I think for a topic like information design is a lame approach. How can you be against information design? It's just a technique! So I was caught on the hop a bit and felt quite bemused by what was going on. I thought we might have a debate about its potential and its limitations. But no.'.



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via information aesthetics on 05/01/11

(Editor's note: this is a guest post by Enrico Bertini from Fell in Love with Data)

The following post describes not only one of my favorite papers from VisWeek this year, but I have the feeling it will be one of those papers which has the potential to lead to a big impact in the way we will use, and see, information visualization in the future.

Edward Segel and Jeffrey Heer decided to analyze the right thing at the right moment: How Do People Tell Stories through Interactive Visualization? (PDF,

The whole trend behind storytelling with vis is, at least in my opinion, one of the biggest changes we are experiencing in visualization since many years. We can see its effects everywhere: taking inspiration from the NYTimes visualizations and the infographics that appear in several other international journals, many other press outlets and advocacy groups followed the route of data-driven journalism, which is slowly but surely becoming a rather successful visualization subfield of its own. It seems that after several decades of research in visualization being focused on data exploration, academics and practitioners alike are re-discovering what the fathers of Infovis have always been saying: "visualization is a great tool for telling stories".

Method: What the authors did

Edward and Jeff analyzed a large set of interactive visualizations for storytelling and, through an iterative process, identified common patterns and built a classification on top of it. Their paper is very rich and I heartedly suggest you to read it all if you are interested, because it is quite "thick" and I will not be able to convey all the useful details it contains in this single post.

In this post, I rather will focus on the parts of the models I deem useful and personally appreciated the most. I will try to suggest, as usual in these guest posts of mine, how you can use the results in the visualization practice.

Design Space

The paper contains a classification of the design space organized around the following components:

  1. Genres - 7 generes of story telling vis exist.
  2. Visual Narrative Tactics - Visual devices that assist or facilitate the narrative: visual structuring, highlighting, transition guidance
  3. Narrative Structure Tactics - Non-visual mechanisms to assist and facilitate the narrative: ordering, interactivity, and messaging.

For a encompassing picture of these components, the paper offers a big overview table (click on it for a larger version).


Tacit Tutorial and Stimulating Default Views

These 2 patterns refer to how a new visualization is introduced to the reader and are of enormous importance. The problems of gently introducing a person into reading a visualization has been neglected for a long time and and it is great to see some suggested practical solutions here.

Tacit tutorial refers to the strategy used by some to gently introduce users to the interactive functionalities, for instance by starting with guided examples. Stimulating default views refers to the strategy of providing initial views that stimulate curiosity and encourage to explore further.


The identification of several genres can function as a starting point for any new story telling visualization, and help designers decide which style better suites the need of the story. The image on the top of this post is a visual summary taken directly from the article.

Messaging and Interactivity

Messaging and interactivity refer to the way the author sends messages in the visualization and how interaction is provided into the user's hands. They are somewhat in contrast, as the more messaging you provide, the less freedom is given to the user. Messaging can generate clutter, but interactivity can detract from the intended message. The existing tension between the two is one of the major issues and trade-offs an visualization designer has to take into account when developing visualizations for storytelling. So please take a note: you will probably have to introduce messaging and interactivity in your visualization, but you will be better off if you carefully balance them and understand when they support and when they hinder its intended purpose.

Author-driven, Reader-driven and Hybrid Models

Visualizations for storytelling are author-driven when there is no freedom for the user in the process of exploring the data. That is, there is only one clear and predefined path to follow. In reader-driven visualizations the user has, on the contrary, total freedom to follow any path.

Narrative visualizations are in general in between these 2 extreme cases, and the paper describes a series of hybrid models. Again, keeping these models in mind can help in reflecting what is the best way to convey a given message when designing a novel visualization.

martini-glass_s.jpgMartini Glass Structure: it starts with an author-driven approach as a way to introduce the reader to the story and the functionalities and at the end it goives total freedom to expolore the data further.

interactive-slideshow_s.jpgInteractive Slideshow: it follows the traditional slideshow structure but it gives freedom to further explore and interact with the data in the scope of each single slide.

drilldown-story_s.jpgDrill-Down Story: it shows a general theme organized by content, and visually by author, but then it gives total freedom to the reader to choose where to drill down to get more information.

How do you put this into practice?

I gave some suggestions already inline together with the descriptions of the various patterns and parts of the model. But let me summarize.

  1. The table with the design space can help you during the design phase to understand what components you can use in your solution. Choose a genre, a visual narrative tactic, and a narrative structure. You can also think creatively and see if there are useful combinations of these elements that have never appeared before. Alternatively, you can think out-of-the-box, and come up with new solution altogether.
  2. Be careful with the level of interactivity and messaging you provide. This is probably a choice that you have to take early in your visualization project: do you want to give lots of freedom and risk that the message is not conveyed clearly or you prefer to guide the reader but remove the thrill of exploration?
  3. The hybrid models are ready-cooked solutions that you can use right away. Again, these can both serve as a way to find a model that suits your message, or just as a starting point to create new hybrid models.

In any case, I hope you enjoyed this post. As I said, given the complexity of this article the post is necessarily reductive. If you are really interested be sure to give a look to the paper itself.

This post has been written by Enrico Bertini. He is a researcher in the visualization and data analysis group at the University of Konstanz. He regularly posts his ideas, reviews, and experiments in his blog, where he tries to bridge the gap between academia and the real world out there. If you have any doubts or question you can contact him on Twitter at @FILWD.


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via Duarte Blog by Nancy Duarte on 10/15/10

All industries are sick of ineffective and boring presentations. Some are so frustrated by the tool, they're willing to speak up and risk their job. Check out the article in the opinion section on what we can do to end PowerPoint fatigue.


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via Free Technology for Teachers by (Mr. Byrne) on 10/15/10

Over the last two days at ACTEM's annual conference I've shared Google Fusion Tables to great response. Many people commented that they had never heard of it, but really liked it and plan to explore it some more on their own. This post is a follow-up to yesterday's conversations. I originally wrote most of this post last winter.

Google Fusion Tables is a neat spreadsheet application that makes it easy to create visualizations of data sets. Fusion Tables can also be used to create visualizations of data set comparisons. At its most basic level Fusion Tables can be used to visualize existing data sets with one click. At a deeper level, Fusion Tables can be used to compare your own data sets and create visualizations of those comparisons. The types of visualizations available include tables, maps, charts, and graphs. As a Social Studies teacher, I really like the map visualization options.

Applications for Education
For the visual learners in your classroom, Google Fusion Tables could be an excellent tool for showing the various ways that data can be interpreted. Fusion Tables also provides students with a fairly easy way to compare their own data sets.  

Here are some related items that may be of interest to you:
Google for Teachers II - Free Ebook
Free 33 Page Guide - Google for Teachers
12 Resources All Social Studies Teachers Should Try


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Orange Visual Visualisation Tool: "

A few days ago, I came across a drag’n'drop, wire it together visualisation and data analysis tool called Orange.

Here’s a quick run through of some of the basics (at least, a run through of the first few things I tried to do with the tool…)

First off, we need some data. Orange likes TSV (tab separated values) rather than CSV, so I grabbed some TSV from one of the Guardian Datastore spreadheets on Google Docs (use “Save as Text” to get the tab separated value format…)

TSV from google docs

Orange is a canvas based visual programming environment, in which functional blocks are added the the canvas and certain parameters set within the block. Here’s how we get some data into Orange from a TSV file:

Orangie viz tool - import data

The File icon is giving me a warning (no dependent variable) but I’m not sure why…? I’m sure Orange has managed to detect labels and quantities correctly from other files I’ve tried?

Anyway… we can inspect the data by looking at it in a data table widget – just wire one in:

Orange viz tool - data table

The table is sortable by column, and the Report button can be used to save a version of the table. Looking t the data table, we see it has identified columns with missing entries. We can clean these from out data set using the Preprocessing widget:

Orange - data cleaning

If we now wire the output of the Processing widget into the Scatterplot widget, we can generate a variety of scatterplots:

Orange scatterplot

If you want to save a copy of the chart, it’s easy enough to do so. (I can’t get colour palettes to work on my Mac, so I’m stuck with greyscale displays. Also, the blob sizing doesn’t seem very responsive…)

Orange - save a scatterplot

The Report tool allows us to create a report from various bits of the dataflow, including adding information from several widgets to either separate report pages or the same report page.

Orange - report generator

Saving a Report saves all the report pages to a navigable set of HTML pages that resemble the Orange Report viewer.

Here are a couple of other things we can do with the data, this time using a data set that isn’t throwing the “dependent variable missing” error, in particular the distribution of comments in a small Friendfeed network…

So for example, here’s how the number of comments made by members of the network is distributed:

Orange - distribution of values

Alternatively, we may look at the distribution in a more “statistical” way:

Orange - simple distributions

(Remember, we can generate these reports interactively, and then add them to a growing report.)

The survey plot gives us a macroscopic birds eye view over the whole of the data set:

Orange - survey plot

Okay, that’s enough for starters – hopefully you get the idea: wire stuff together and generate visual reports… So why not go and download Orange now?!;-)

There are a whole range of clustering tools, too, which look like they could be interesting…

And I think the platform is extensible, which means there’s a way of adding your own widgets (written in Python, maybe..?)