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I'd like to propose an experiment. If you ever publish a photograph on Flickr that features, or is otherwise related to, a ³ÉÈËÂÛ̳ TV or radio programme you might think about machine tagging* it with the programme's unique identifier. First find the programme's unique PID (that's the 8 character ID you find in /programmes or iPlayer URLs; the "b00lj1nc" in /programmes/b00lj1nc), then add a Flickr tag that looks like this...

bbc:programme=b00lj1nc

That's it, the photo is now machine tagged. Machines can now discover that refers to this programme. This is a rather trivial example; my radio tuned to Any Questions on ³ÉÈËÂÛ̳ Radio 4. If you click through and check the tags on Flickr you'll see something that looks like the machine tag above.

So it might be that you took the picture because you really enjoyed listening to something, but more interesting would be a location of a show, or pictures from when you were in the audience, or maybe you've taken a picture of something or someone that was featured in a programme...

Statue of the Venezuelan revolutionary, Francisco de Miranda, as featured on In Our Time

By machine-tagging these photos you will be helping machines connect things together and create new paths for people to find both ³ÉÈËÂÛ̳ programmes and your photos. Sites like , , , and already support machine tags and are often connected to Flickr. Some people are even . An event/place/book page can then point to the on Flickr and the photo page can point back to the event/place/. And, of course, it's not just photos that you can tag like this, you could machine tag your or your social bookmarks or even . As an example, the ³ÉÈËÂÛ̳ could use machine tags to point to your photos from our programme pages or use them to illustrate a blog post.

All these machine tags will be helping to connect and link different pools of data on the web which are currently unconnected. And that's what the web should be about - linking things.

Last week Radio Labs and RAD held a joint mini-hackday to explore visualising some of our music data. We had several multi-disciplinary teams building quick prototypes and hacks, what I called "sketching with data, designing with code". Here are some snapshots of the results...

Album covers from the charts are laid out as a .

Filtering the charts by weather conditions.

The most played bands for each ³ÉÈËÂÛ̳ music show.

Using to render a wall of ³ÉÈËÂÛ̳ shows.

Mapping the bands from ³ÉÈËÂÛ̳ Introducing.

A of lyrics from the charts.

Graphing when a band released records (that's my own MacBook Air - no, the ³ÉÈËÂÛ̳ doesn't give us these!)

We're picking this work up again now to take the best of these ideas and try to build something great. More soon.