Scripted Diagrams Getting Easier

A quick heads-up on an another tool (diagrammr) that makes it easy to create network/graph diagrams like this:

Just type in a description of the graph and the diagram will be generated at the same time [video]:

[Infoskills note to self: when making a screencast with Jing, after clicking in a text box area, remember to move the mouse cursor out of the way...]

(Regular readers will know I’ve been this sort of thing for some time; for example, see Scripting Charts WIth GraphViz – Hierarchies; and a Question of Attitude, Writing Diagrams, RESTful Image Generation – When Text Just Won’t Do or Visual Gadgets: Scripting Diagrams).)

As well as creating diagrams, Diagrammr allows you to embed them, providing an image/PNG URI for your diagram; you can also edit the image (that is, edit the script that generates the image) after the fact via a shareable URI.

The URI for the editor page can be generated from the image URI, though, so without the ability to set a password on the editor page when you first crate a new image, this means that any time you embed a Diagrammr image, someone else could go and edit the image?

In an educational context, tools like this make it much easier for students to create their own diagrams (typing in a graph description is far quicker than trying to lay it out by hand in a drawing package). As you script the diagram, your attention is focussed on the local structural components/relations that define the graph, whilst at the same time the automatically generated diagram visualises the overall structure and brings alive its complexity at the network level.

(I’m not sure how the graph layouts are generated – maybe using Graphviz on the server to generate the image and return it to the browser? If so, an improved version of diagrammr might be able to return the compiled xdot version of the graph back to an interactive canviz component running in the browser?)

If you’re working in an insitutional VLE context, where the powers that be are still trying to retain control of everything, the Canviz component might offer one solution – an HTML 5 canvas library for displaying ‘compiled’ Graphviz network descriptions.

Although I haven’t tried it out, there is apparently a recipe for integrating Graphviz with Drupal (Graphviz Filter) and a suggestion for including Canviz into the mix (GraphMapping Framework (graphviz_api + graphviz_fields + graphviz_views + graphviz_filter) – has this been implemented yet by anyone, I wonder?). I’ve no idea if anyone has tried to do something similar in a Moodle environment…

UK HEI Boxee Channel

A week or so ago, Liz Azyan posted a list of UK HEI Youtube channels. Although not quite as polished as @liamgh et al’s OU Boxee app, I piucked up on a couple of suggestions Liam made over a pint last night about simply subscribing to an RSS feed in Boxee to roll my own UK HEI Youtube Boxee channel thing…

So here are the institutional channels:

and here’s a peek inside one of them:

This lets me watch the most recently uploaded videos to all (?) the UK HEIs’ most recent uploads to their Youtube channels, organised by institution via a lean back TV interface.

(You might be able to submenu the institutional channels/streams according to playlists they have specified, as well as tidying up things like icons/logos, maybe, but this was a 10 minute hack, rather than a half hour hack, ok?!;-)

Here’s the recipe…

1. Grab the table from Liz’s web page and create a feed from it:

2. Generate the feed URIs for the most recent uploads to each channel (in the form required by Boxe – e.g. rss://gdata.youtube.com/feeds/base/users/abertayTV/uploads?alt=rss&v=2&orderby=published):

Filter out stuff that isn’t a feed and complete the pipe:

We can now grab the RSS feed from the pipe in the normal way and subscribe to it via a personal account on the Boxee website.

If you now launch the Boxee app, select:

- Video:

- Internet

- Video Feeds (My Feeds)

- the UK HEI Youtube Videos Channel

And from there, you should be able to browse – and play – the recent uploads to all the UK HEI Youtube channels that Liz has listed.

Not that I had a niggle with my Boxe player – I could hear the audio but not see the video for any of the Youtube videos when I tried to play them. If anyone else tries out this channel and gts the same problem, please let me know and I;ll see if it’s a feed problem. Otherwise, I’ll assume it’s a local glitch…

Here’s the RSS feed URI again: “UK HEI Youtube Channels on Boxee” RSS feed

PS out of interest, if I had bid to do this as a #jiscri project, how much should I have asked for?
- planning: 10 mins chatting with Liam over a pint ysterday;
- design: <5 mins looking up Youtube API/URI patterns
- implementation:: <5 mins creating Yahoo pipe
- configuration: <5 mins subscribing to the pipe feed in Boxee
- testing: <5 mins seeing if it worked in Boxee (which it doesn’t, properly, but I’m blaming that on a local problem and trustung that it does actually work… err…?!;-)
Okay, so all told it was maybe a sub-20 minute hack rather than 5 minute one?
- documentation: (i.e. blog post) 30-45 mins, incl grabbing screenshots.

And I’m on holiday today…

A Quick Peek at the IWMW2009 Twitter Network

This is just a quickie following up Preliminary Thoughts on Visualising the OpenEd09 Twitter Network, Where Next With The Hashtagging Twitterers List? and Who’s Tweeting Our Hashtag? with a couple of images showing some of the ‘Twitter action’ going on around IWMW2009. (I’ll post the how to when I’m not on holiday…the recipe is a little different to the approach I used in the previous posts…;-)

First up, a graph of who sent a tweet to whom whilst using the #iwmw2009 hashtag. The arrows go FROM the person sending the tweet TO the person they sent it to (I think…):

Secondly, a graph of the RT/retweet network. The arrows go FROM a person who RTd a post TO the person who sent the original tweet (so if A RTd B, the arrow goes FROM A TO B).

Note that if a link goes from A TO B TO C, it doesn’t necessarily mean that A RTd B’s RT of tweet from C. It just means that at some point A RTd B, and at some point B RTd C. This sort of graph might help to identify amplification networks? So for example, we see iwmw and briankelly both being amplified/RTd quite a bit, as well as jaducms. Were folks from the latter engaged in a little bit of social media PR, I wonder?!;-)

Okay – that’s enough of a teaser for now; back to being on holiday… :-)

PS I actually tweeted a links to a couple of similar graphs fro opned09 ystrday – but there was a big oops and those graphs were actually wrong, so I’ve deleted them; I’ll repost them via a blog post in a few days…

Drag and Drop Ordered Links in Delicious?

One of the things I’ve been interested in for some time is how to use social bookmarking services like delicious as a database for ordered link collections (for example, Ordered Lists of Links from delicious Using Yahoo Pipes).

I don’t have time to try this right now, so here’s a quick holding post about a plan for the future:

- pinch the code used for this jquery demo: “Dynamic Drag’n Drop With jQuery And PHP” which shows how to update a database with the new list order using “an Ajax request in the backend”…

- populate the list using the delicious api from a (logged in) users’ specified tags;

- identify the position of each item in the list using a special machinetag tag. Rewrite the jquery demo to use a delicious API call in place of the demo database update function. I’m guessing that this would mean using the https://api.del.icio.us/v1/posts/add? call, and just copying everything from the previous version of the bookmark apart from the item number machine tag?

- cobble together a Javascript script to pull a tagged list from delicious, as JSON, that includes ordered list machine tags and display the list, appropriately ordered; also provide a ‘reverse order’ switch ;-)

- cobble together a simple web service using a minimal PHP script that will grab a tagged list from delicious, with machine tags, and then display it as an ordered RSS list (also provide a ‘reverse order’ switch in the URI).

If anyone builds these before I do, please post a link to the demo :-)

Split Screen Screenshots

Some time ago, I posted a quick hack about how to capture “split screen” screenshots, such as the one below, that shows a BBC News video embedded in a Guardian online news story:

This utility can be handy when you want to capture something in a single screenshot from the top and the bottom of a long web page, but don’t necessarily want all the stuff in between.

Anyway, the hack was included in the middle of a longer web page, so here’s a reposting of it…

On a server somewhere, place the following PHP script:

<html>
<head>
<title></title>
</head>

<frameset rows="30%, 70%">
      <frame src="<?php echo $_GET['url']; ?>">
      <frame src="<?php echo $_GET['url']; ?>">
</frameset>

The bookmarklet simply uses the current page URI as an argument in a call to the above page:

javascript:window.location=
“http://localhost/splitscreen.php?url=encodeURIComponent(window.location.href);

Here’s the bookmarklet in action:

(I was going to pop up a version of the script to http://ouseful.open.ac.uk, but for some reason I can’t get in to upload anything there just at the moment:-(

Why Blog….?

When you are a Bear of Very Little Brain, and you Think of Things, you find sometimes that a Thing which seemed very Thingish inside you is quite different when it gets out into the open and has other people looking at it.

A.A. Milne

Preliminary Thoughts on Visualising the OpenEd09 Twitter Network

The wealth of activity on the #opened09 Twitterstream keeps stealing my attention, and has got me thinking about how we might start to analyse the structure of the network around the hashtag, in part so we can understand information flow through that part of the open education network better.

So what’s come to mind? Here are some early sketches that I’m using as a foil to see what sort of questions they bring to my mind about the structure of the network. Note that this is a ‘first principles’/grass roots/bare bones approach – I’m not going to use any formal social network analysis techniques because I don’t know anything about them (yet….;-) But by starting with some doodles, the questions that arise may lead me to appreciating why the formal SNA tools and approaches are so useful in practical, easy to understand, human terms, and thus provide me with the motivation to learn how to use them…

First up, is there any value in understanding the structure of the network of people who are twittering with the #opened09 hashtag? Partly because of rate limiting on the Twitter API, last night I grabbed a sample of the most recent #opened09 hashtagged tweets, and then filtered them down to the most active twitterers over that period (people using the hashtag more than three times in the sample, I think it turned out to be). Then I pulled down their follower lists from the Twitter API, and constructed a graph of who followed who in that set. Here’s the Graphviz plot of that graph:

So what questions does this bring to mind? Well, off the top of my head:

- who is the most connected person in this graph? Does that tell us anything useful? Would we expect the event organiser to be the most connected?
- is there anyone in the network who isn’t very connected to other people? Why? Are they a different ’sort’ of user? Are they new to the network?
- does the connectedness of people within the graph change over the course of the event? (I think that the Twitter API returns a list of followers in reverse chronological order; so we could approximate the growth by comparing the above graph with one that ignored the most recent 50(?!) followers of each person?
- are all Twitterers equal? Should we treat users who only ever use the hashtag as part of an RT differently when constructing this graph?
- is there any value in representing the number of followers each person has within the above graph? Or the number of people they follower? Or some function of the two? What about the number of times they used the hashtag in the sample period (or the number of times they RT the hashtag, or a function of the two) – should that be reflected too?

What’s the POINT of asking these questions? How about this – as individuals, can we identify members of the community who we don’t know? (IE is this sort of graph a good basis as a friend recommender? Would a big poster of this sort of graph be a good thing to post in the event coffee area? What would people look for in it if we did?)

Okay, next up: there’s always talk of things like twitter being used to amplify an event to ‘virtual participants’. How big might that audience be? And who comprises it? Are there people not at the event who effectively amplify it further?

How about a plot of the simple reach of the twitters, as a treemap?

Hmm… I’m not sure about that… Just because you have a big following doesn’t mean it’s a big relevant or interested following? (The number of people RTing the hashtag in your audience might?)

instead, how about this sort of graphic to help frame some questions:

This one shows, for the most active opened09 hashtag twitterers, the people who follow more than 12 (maybe?; or more than 11?) of them. The named individuals are heavy opened09 twitterers, the numbers are the Twiitter IDs of the people who are seeing the event amplified to them. (Not that these people may also have tweeted the hashtag, only not so heavily. Maybe I need a stop list that removes people from this ‘amplification graph’ who have used the hashtag? That way, we can identify ‘leaves’ on the #opened09 tree – that is, people who received some number of #opened09 tweets but who never used the hashtag?)

So what questions does it bring to mind:

- are there people receiving large numbers of opened09 tweets who are unknown to the community?
- do the opened twitterers fall into cliques or reasonably well clustered groups around sets of followers who aren’t tweeting? (Would a cluster analysis be an interesting thing to do here?)
- if we lower the sampling threshold that specifies the minimum number of heavy twitterers that a ‘listener’ is following, how does the size of the listening audience grow? Is this interesting? Does the numbr of people that a listener follows influence how likely they are to see opened09 tweets. (eg if i follow 20 opned09 heavy twitterers, and only 50 people in all, my traffic may be domintad by opened09 folk; if i follow 500, or 1000, or 2000, then that traffic is likely to be diluted?)

And the POINT? Can we get a feeling for the audience the event is being amplified to? Are there members of that audience who seem to be a member of the community but aren’t really known to the community? Can we find the lurkers and pull them in with a personal invite (and is this even ethical?)

Just by the by, looking at RT networks could also be interesting – that is, looking at patterns of RTing across the network. maybe a graph showing people who RTd hashtagged tweets, as well as the path back to the original tweet? (This brings to mind some of @mediaczar’s work looking at Twitter in a PR context – which is exaclty what event amplification is, right?)

So having got some questions in mind (if you have more, please add them as comments below), I’ve got some sort of rationale for having a look at some formal graph theory and social network analysis stuff. This looks like it could be a good place to start: M.E.J. Nwman – The mathematics of networks [PDF].

[UPDATE: I guess the heuristic I have in mind with respect to the charts and SNA is this: are there features from the visualisation that jump out at me that the SNA tools can also pick out?]

Browser Use In Higher Education – A Brake on Innovation?

A couple of weeks ago, I set up a quick poll to try to get a feel for what sort of browsers were being used as the default browser on computers in UK universities. I provided a little bit of rationale in a post on the IWMW blog (What’s the default browser on your network?) but the main reason was to get a feel for what sorts of browser any #jiscri apps might have to contend with if we wanted our JISCRI project apps to ever get used in that default user context (which is the point, right?!;-)

Anyway, here are the results, such as they are:

Hmmm…. (See also this Quirks Mode post on why “IE6 will continue to exist when IE7 has all but disappeared”.)

After a bit of consultation with @josswinn, I’ve popped up a more elaborate questionnaire here: JISCRI “What do we have to cope with browser wise” survey, so if you’ve a couple of minutes to spare, we’d appreciate you filling it in… (or not – what do I care?!;-)

If you want to create your own reports of the data, feel free (for a quick how to on that, see: Creating Your Own Results Charts for Surveys Created with Google Forms)

PS note that this is not an ‘official’ JISCRI survey, nor is it posted as part of a JISCRI project. I just thought it might be useful, that’s all… ;-)

A Couple More Thoughts on Library Openness…

Getting blog links in to the OUseful.info blog has been getting harder over the last few weeks, but my post on Open Educational Resources and the University Library Website (which I’d tweeted as “Are academic libraries conspiring against OERs?”) generated a couple that I thought I’d comment on here.

First up, Stephen Downes suggested: “Is the university library actively subverting the movement toward opn [sic] educational resources? One could argue that it has significant incentive to do so. … We cannot, I argue, expect support for open educational resources from institutions dedicated to their elimination.”

The thing that first came to my mind when I read that was that it’s the vendors of systems into libraries who have a commercial stake in directing attention towards their systems, particularly in the journals/ebooks area. (I’m not sure what the library catalogue vendors sell other than their pretty much unusable OPAC systems?) On second breath, it then struck me that the people in the library who hold the budgets to pay for these expensive systems also have a stake in retaining them, under the assumption that the bigger the budget you hold/spend, the more, I dunno, important you are?

I haven’t had a look round the library websites to see how obvious the open repository searches are, but I’d be willing to bet that they still aren’t as prominent as the bought in systems that direct attention towards subscription journal content. (Maybe that’s right? Maybe the prominence should be proportional to the amount of money spent on the resource compared with allocating it in proportion to website traffic (cf. OU Library Home Page – Normalised Click Density). But it is worth remembering that the research repository projects are often run under the auspices of the archiving role of the library.)

The next point that came to mind was clarified for me by David Davies in OER and library websites, time for integration:

[H]istorically it’s not been libraries that worried about those kinds of educational resources [i.e. OERs]. While libraries were cataloguing books and journals, other parts of the central institutional services were managing learning objects, multimedia resources, e-learning content, whatever you want to call the stuff. These resources were locked up in WebCT or some other VLE/LMS and were discoverable there, at least in theory. Teachers and their students knew, and still know, where to look for books & journals and where to look for other kinds of learning resources.

This suggests that teaching material type stuff is to be found through the VLE or CMS. But I’m not sure that’s right? Locally suggested resources are almost definitely linked to from those environments, but do those environments also offer wider search facilities over external teaching materials? Why shouldn’t that sort of material be the sort of material that you’d expect to find in using academic library search tools? (See also: ACRLog: The Question They Forgot To Ask: “But why are we only considering the role of the academic library as gateway, archive and buyer? I would argue [we need] to add a new dimension for faculty to consider – the academic library’s role as learning center and instruction partner [rather than focusing] on the acadmic [sic] library’s traditional role as collector, organizer and gateway provider. … I would argue that an equally essential part of the academic library’s digital transformation is the shift from the gateway role to the teaching and learning role in a much more aggressive way that integrates the library into the digital learning environment that has become many faculty’s preferred method of delivering their educational content“; okay, so here we gt into invisible library territory – the library providing services in other locations. But it’s still a library service… What I think we need to do is tease apart library services and where those services are accessed. It may be that we don’t want OERs to be discoverable through the library website, but then we need to ask exactly what sort of proposition the library website is offering?)

There’s another thing I’d like to pick up from David’s post – the phrase: “While libraries were…”; because while they were (doing whatever? conspiring with the publishers against academic authors’ rights?, or conspiring with the publishers to pass off sponsored publications of as academic journals?;-), the web arrived, websearch arrived, Google arrived, Google Books and Google Scholar arrived, arXiv already was there (but did the library build on it?), OERs started gaining funding, if not traction (yet), Library Thing arrived, Creative Commons arrived, print-on-demand has almost arrived (Amazon has a POD capability, I think?), all manner of how to video tutorial sites arrived (and their aggregators – like How Do I?;-), custom search engines arrived, and so on…

Ho hum, I hadn’t intended that to b so much of a rant… Sigh… maybe I need to go and look at JISC’s Library of the Future debate to see how to think about libraries in a more considered manner?;-)

Where Next With The Hashtagging Twitterers List?

This post is a holding position, so it’s probably gonna be even more cryptic than usual…

In Who’s Tweeting Our Hashtag?, I described a recipe for generating a list of people who had been tweeting, twittering or whatever, using a particular hashtag.

So what’s next on my to do list with this info?

Well, first of all I thought it’d be interesting to try to plot a graph of connections between the followers of everyone on the list, to see how large the hashtag audience might be.

Using a list of about 60 or so twitterers, captured yesterday, I called the Twitter API http://twitter.com/followers/ids/USERNAME.xml function for each one to pull down an XML list of all each of their followers by ID number, and topped it up with the user info (http://twitter.com/users/show/USERNAME.xml) for each person on the original list; this info meant I could in turn spot the ID for each of the hashtagging twitterers amongst the followers lists.

It’s easy enough to map transform these lists into the dot format that can be plotted by GraphViz, but the 10,000 edges or so that the list generated from the followers lists was too much for my version of GraphViz to cope with.

So instead, I thought I’d just try to plot a subgraph, such as the graph of people who were following a minimum specified number of people in the original hashtag twittering list. So for example, the graph of people who were following at least five of the the people who’d used the particular hashtag.

I hacked a piece of code to do this, but it’s far from ideal and I’m not totally convinced it works properly… Ideally what I want is simple (efficient) utility that will accept a .dot file and prune it, removing nodes that are less than a specified degree. (If you know of such a tool, please post a link to it in the comments:-)

Here’s the first graph I managed to plot:

If my code is working, an edge points to a person if at that person is following at least, err, lots of the other people [that is: lots of other people who used the hashtag]. So under the assumption that the code is working, this graph shows one person at the centre of the graph who is following lots of people who have tweeted the hashtag. Any guesses who that person might be? People who have edges directed towards them in this sort of plot are people who are heavily following the people using a particular hashtag. If you’re a conference organiser, I’m guessing that you’d probably want to appear in this sort of graph?

(If the code isn’t working, I’m not sure what the hell it is doing, or what the graph shows?!;-)

One other thing I thought I’d look at was the people who are following lots of people on the hashtagging list who haven’t themselves used the hashtag. These are the people to whom the event is being heavily amplified.

So for example, here we have a chart that is constructed as follows. The hashtag twitterers list is constructed from a sample of the most recent 500 opened09 hashtagged tweets around about the time stamp of this post and contains people who are in that list at least 3 times.

The edges on the chart are directed towards people who are not on the hashtag list but who are following more than 13 of the people who are on the list.

Hmmmm… anyway, that’s more than enough confusion for now… I’m going to try not to tinker with this any more for a bit, becuase a holiday beckons and this could turn into a mindf**k project… However, when I do return to it, I think I’m going to have a go at attacking it with a graph/network toolkit, such as NetworkX, and see if I can do a proper bit of network analysis on the resulting graphs.