Here’s a few short and interesting things I’ve been playing with for a little while. Each of these revolves around the idea of documenting ones own behaviour by laying down data – Kevin Kelly’s notion of the ‘quantified self‘.
I’m very interested in how this personal behavioural data can be used to better improve our own and collective relations and awareness. These also raise issues around the changing nature of privacy and the ways that we ‘produce’ our own identities (or as Michelle Kasprzak described it at Picnic 10 – our ‘forked identities’). I’m interested in this both as someone who spent a fair amount of time in a past-life researching subcultural infrastructure, but also from the perspective of how we use these to do interesting things in museums.
Mappiness is a mobile app from the London School of Economics who are doing a UK research project trying to map ‘happiness’ across the country. Now whilst the research data is only concerned with the UK, the app works internationally and I’ve been using it to ‘track my own happiness’. Not only am I submitting data – I can see my own data which is what I care most about. (If this experiment was being run by someone other than LSE I may not have participated.)
Here’s my happiness, sleepiness and awakeness graphed over the past little while.
For nearly five years now, I’ve been tracking all of my music listening through a commercial service called Last.FM. I started making the extra effort to ensure I tracked at least 95% of everything I had agency in the choices of music I was listening to was tracked once I figured that the aggregate data was actually, for me, incredibly interesting. Being someone who also has a musical alter ego, this data represents the reality of my musical identity, versus the projected music identity. (Next year I’m publishing an entire representation of five years of my listening).
Now Last.FM has had a very active community around its dataset and last week their Playground section launched LastFM’s Gender Plot. This takes your tracked listening and compares it to the aggregate of everyone else’s listening and self-presented demographic data and plots where you fit.
Apparently I have very gender-balanced listening and am a fair bit younger in my tastes than my actual age.
The third is Readermeter (via @lorcand). Readermeter is different in that it presents different ‘measures’ of impact for authors. It visually presents the H and G index of publications (citations-based) along with ‘readership’ data from the Mendeley API. I like this one, because much like Last.FM, this is all about shifting the impact data from being about ‘sales data’ to readership and use data.
Here’s a link to Creative Commons founder Lawrence Lessig’s profile on Readermeter. You can see impact of his different books from both citations and readership. Bear in mind that this data is heavily skewed towards academic-focussed publishing.