So, I decided to lay it all out in a (relatively) simple timeline format; as far as I know, this doesn’t exist anywhere else.  You’re welcome, The Internet.  Below are my sources and some notes; where possible, links are to the original recommendations or RFCs:

SGML is an ISO standard from the 80’s.  Unlike the other standards on this list, it’s not open (the ISO sells copies for >$200).  HTML is an “SGML application“, and has been from the beginning. The Wikipedia article has a lot more information on its origins, as does the W3C.

HTML 2.0 and HTML 3.2 , the first two W3C specs, are both pretty straightforward. Also straightforward is XML, which dropped in February 1998. Like HTML, XML is “an application profile…of SGML.”

In December 1999, the HTML 4.01 recommendation came out, followed a month later by XHTML 1.0.  The important thing to note is that both of these are still HTML 4; however, XHTML is “a reformulation of HTML 4 as an XML 1.0 application,” while HTML 4.01 is still plain ol’ SGML.

No one knows yet exactly what HTML5 is going to look like, as it’s still several years off.  However, the W3C tells us that the HTML5, like HTML4, is going to have two different “serializations.”  One will be an XML syntax, and is currently being called XHTML 5 (wait, why not “XHTML 2?”  Hang on, we’ll get there). You might expect that the other serialization would be SGML a la HTML 4.01.  You’d be wrong.

Although HTML is technically SGML, most browsers and authoring tools couldn’t care less about the broader SGML standard; they just implement HTML.  So the W3C’s plan seems to be to ditch the SGML legecy and replace it with “html” (note the lowercase), an entirely new standard…which happens to look pretty much like HTML has always looked.

Whew, we’re almost done.  OK, what about XHTML2?  Despite the name, project was not a “next step;” it was a huge break with the whole HTML/XHTML tradition, an effort to completely remake web markup.  In July, the W3C decided to let it die on the vine and focus on HTML5.  So XHTML 5, with its HTML lineage, will be a more incremental change than XHTML 2 would’ve been.

There you have it.  If I missed anything or got something turned around, let me know.

Phrenology is interesting stuff.  Though phrenologists had nearly everything wrong, modern neuroimaging has demonstrated that one  important part of their core idea was right: many psychological functions really are highly localized in the brain.  And they made a lot of really cool infographics.  Actually, maybe this is pseudoscience in general; palmistry and astrology also make silly data into some neat-looking infovis.  This same exercise would be fun with made-up star charts and palm diagrams.

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Materials:

• Some 1×2 boards (you can pre-sanded ones for about $2 a piece)
• A panel of “tile board,” which you can get from Home Depot or whatever for about 10 bucks.
• some 3″ drywall screws
• some 1 1/2″ drywall screws
• wood glue

Tools:

• Drill with a screwdriver bit
• handsaw (may need it, may not; see below)
• tablesaw or circular saw to cut the tileboard (may need it, may not; see below)

Construction:

1. Decide on the dimensions you want, and figure how many 1×2’s you need (see the diagram above for the general plan).  You may need to be flexible here, depending on the sized of tile board panel you’re able to procure.
2. Get the materials.  If you ask nice, a lot of times the store will cut the tile board for you, or they may have a 2′ x 4′  piece available.  You can probably get them to cut the 1 x 2’s for you, as well.
3. Once you get the materials home, cut anything that still needs cuttin’.
4. Fasten everything together with the appropriate-sized drywall screws (The diagram shows where they go).  I added glue, but you don’t really need it.  Once the frame is done, glue the top on. Done!

So how is it that presentation tool Prezi is suddenly the toast of the town?  The quick sell looks like this:

“Prezi allows anyone who can sketch an idea on a napkin to create and perform stunning non-linear presentations with relations, zooming into details, and adjusting to the time left without the need to skip slides.”

I love how the first phrase suggests that there’s this great mass of napkin-sketching geniuses out there who can’t get their ideas out (until now!).  I mean, I like mind maps, but turning one into an outline is pretty easy.   So the presentations are “non-linear.”  Does that mean the audience can interact with them, zooming in on sub-points of interest?  If it does, let me show you this thing called “hyperlinks.”   And is skipping slides really this tremendous problem?

When it comes down to it, the real selling point of Prezi is just the “stunning” presentation.  Now, perhaps I’m jaded, but “zoom-in/zoom-out” leaves me unstunned.  More importantly, though, this seems a textbook example of chartjunk: a “really great” visual effect that serves only to obscure or distract from real information.  I think (hope) it’ll have the lasting appeal of Powerpoint’s racecar-noise-with-flying-in-bullet-point.

Perhaps I’m missing something (feel free to correct me in the comments) or just being curmudgeonly, but I think Prezi is vastly overhyped.  Powerpoint is bad enough.  Also: I like how the Prezi logo, by mixing case, suggests that the product may in fact be called “Pretzl.”  Ok, now that’s definitely being curmudgeonly.

My FeedVis project–the interactive tagcloud for a group of feeds–has been out for a week now, I’ve been thrilled at the positive response I’ve gotten so far.  One rather glaring problem with the program, though, was that you could only look at the top 50 edublogs.

Not anymore.  After a few late nights, I’ve got a beta system for uploading and analyzing your own sets of feeds.  You just upload your opml, wait a few minutes, and you’re set: FeedVis gives you a custom page that you can bookmark and return to anytime you like; it’ll continue to update every time you visit.  You can also browse visualizations of other people’s feeds.

It’s pretty untested, and I’m sure use will uncover some bugs.  But it’s got potential; I’m excited to see what people think.

Tagclouds have value, but, as I’ve written before, they’ve a number of shortfalls as well.  I’ve just finished my attempt to remedy some of these problems: FeedVis.  It’s an animated tagcloud that lets you compare word frequencies accross different time periods and authors, then check out the posts that used the words.  The demo is using the feeds for Scott McLeod’s Technorati-compiled list of top 50 edublogs, since that’s what got me started about feeds and tagclouds in the first place (although the program will work with any set of feeds).  More details about how it works are on the demo page.

I think what I’m really most excited about is the way this uses animation to let you actually see the words changing from one sample to the next.    Motion is such an important part of the way we see the world, and it’s been underemployed in information visualization, I think (although this changing; Hans Rosling’s TED talks have gotten a lot of buzz, for instance).

The project has been really fun, and a great learning experience; it’s gotten me really pumped about inofVis for learning about online interaction.  I think there is a lot of potential there for ed tech research.  I’m also pretty excited about programming; I started learning in February (with php), and then started javascript a couple months ago.  It’s been a really mind-expanding experience, and I’m looking foward to my next project, probably once I get done with grad school apps.

But why stop there?  Why not a service that analyzes this kind of real-time, viewer-supplied data, selects the most interesting bits, and then displays it?  It would function both as a real-time fact-checker and a window into audience’s reactions.

Lots of people already live-blog these things; it would be easy to get several thousand people to submit their questions and search results to a server, using a standardized interface.  The software then just aggregates, organizes, and presents the results.  Volunteers who try to game the system would be shut out with Digg-style, community-driven user ratings.  If Google would make its real-time query data available, that’d be added, too, significantly broadening the sample’s relevance.

The exciting part comes when this user-created input—both information about facts and people’s more general reaction to what they’re hearing—is presented to the the debate audience and the debaters themselves in real time (via a big display in the venue, for instance.)  For one thing,  the audience and debaters would immediately know of factual errors or half-truths, and have easy access to cited sources.  This would work for relatively picky things, like the pronunciation of someone’s name, but also for more substantive problems in responses; imagine how much answers would improve if the debaters knew they might have to stand next to an 8-foot-high rendering of, “40 million Americans think you just dodged the question.”

But also, the tool could act in more positive ways.  The candidates would immediately know the reactions of a national audience had about what they say–what the audience was interested in, confused about, or skeptical about.  If 80% of people want to hear more about the differences in candidates’ economic plan, they probably will.  You would have a truly participatory, interactive town-hall meeting of sixty million people.   Techniques like this are already beginning to surface in education, with tools like classroom clickers and “google jockying.”  Could they raise the level and relevancy of national politics?

Note: oops, accidentally published this sans links.  fixed now.

But the problems of tag clouds–and their close cousin, word clouds–go deeper, to their usefulness as a visualization method.  These aren’t problems with how the method is used or misused, but with the idea itself.

Moritz Stefaner points out (and presents his own solution for) several problems with the format:

• tag clouds give a great picture of the “big head” of tags: the most frequently used tags that change little over time; they overlook, though, the “long tail”–where many of the interesting tags are located.
• tag clouds don’t show change over time.  Chirag Mehta has created a tag cloud with a time slider, which helps with this.  But as Stefaner points out, animating tag clouds doesn’t work very well, as the changing size of the cloud moves the words around so they’re hard to follow.
• Finally, tag clouds don’t show the relationships between tags (pretty much everyone who criticizes tag clouds mentions this one).

The IBM Many Eyes site has one of the best tag cloud (actually this does word clouds, too) tools I’ve seen, allowing users to get lots of data from each tag while keeping the interface clean and simple.  They make a great point about an inherent limitation of the tool: the size and shape of the words themselves isn’t controlled for.  So, long words seem more dominant than short ones, and words with lots of ascenders and descenders (the vertical strokes of letters like ‘b’ or ‘p’) tend to dominate as well.  This can subtly alter the overall gist that tag clouds are supposed to deliver.

The academic community has noted shortcomings of the technique, as well. Hearst and Rosner (2008) observe that the alphabetical layout of the cloud may lead to a sort of “false clustering” effect, as users misinterpret words because of surrounding tags.  Renninger and Shumar (2007) found that tag cloud quadrants have different rates of recall, a fact which most tag cloud designs ignore.  In fact, their findings suggest that a simple list of tags, ordered by frequency, may deliver a more accurate overall impression than a tag cloud.  Several researchers have sought to improve shortcomings in tag cloud presentation with packing and sorting algorithms that manage whitespace and cluster relevant concepts (Kaser and Lemire, 2007; Seifert, Kump, Kienreich, Granitzer, and Granitzer, 2008).

Now, this isn’t to say that tag clouds have no value; in fact, I think they have great potential. It’s just that we need to know when tag clouds and word clouds are appropriate, know their shortcomings, and (this is the fun part) try to find ways to make them better. Most of the sources cited above have set about doing just that. In my next post, I’ll discuss a few of these “next-generation tag cloud” concepts; in particular, I’ll be examining methods of using word clouds to compare different versions of a text.

In a data-sodden world, the scarce resource is not access, but organization.  Scott adds organizational value both through visualization of PLE concept, and in linking similar work.  The result: a great resource for anyone interested in PLE’s.  Now all we need is a meta-visualization of all the individual efforts together…