I’ve been looking forward to this session by Chris Lintott on Galaxy Zoo

As our ability to get information about the universe has increased we are challenged to deal with larger and larger amounts of data. In astronomy driven by availability of hi-resolution digital imaging etc – whereas 20-30 years ago you could get collections of hundreds of galaxies – now can get collections of millions.

Analysis of galaxy images is about looking at the shape of galaxy. While machine approaches have been developed – they typically have only an 80% accuracy. However humans are very good at this type of task. This used to be a task students would do – but the amount of data far outstripped ability of students to keep up.

In astronomy there is a long tradition of ‘amateurs’ taking part and spotting things that may not be spotted by professionals. However contibutions have generally been around data collection – and then passed to experts for analysis. Galaxy Zoo reverses this – data collection been done and asking public to analyse data.

GalaxyZoo was meant to be a side project – but picked up by media – specifically BBC News website – and sudden burst of publicity got huge boost. However, first thing that happened was server went down – 30,000 emails telling them that the server had gone down. Luckily able to get that back up and running quickly.

After 48 hours were classifying as many galaxies in 1 hour as a student previously doing in a month.

Found that getting many people to do the classification improves accuracy – over professional astronomers. Took away all barriers to participating to get as many people involved as possible. Originally had a ‘test’ for users – but took this away.

The huge side effect is that humans can spot unexpected stuff without being told – much better than machines.

Also built community around people participating – this community now starting to solve problems – e.g. discovery of small green galaxies – started to analyse, recruited programmer to interrogate data and this has eventually resulted in published paper – these objects have been known since 1960s but never analysed. None of the people in the group were scientists.

When they’ve talked to users of the site the overwhelming reason for taking part is that they want to do something useful – want to contribute.

We have responsibility not to waste peoples time – collective manpower on GalaxyZoo 2 was equivalent to employing a single person for 200 years – we cannot take this likely.

Don’t make promises you can’t keep – e.g. don’t offer ‘free response’ that you then can’t actually read – Galaxy Zoo handles this via the online community forums.

Chris describes three strands of engagement with users

• Known knowns
• Unknown unknowns
• Known unknowns

Now JISC funded project to convert information from old ship logs – because has climate data.

Show pages of ships logs –

• key data you should extract (known knowns – that stuff the researchers know they want from the logs like weather reports)
• unexpected things you might spot (unknown uknowns – stuff you might spot in the logs – pictures, unexpected information)
• expected things, but not known how much (known unknowns – events you know will be in there but not how often e.g. encounters with other ships)

These strands are generalisable to many projects

Zooniverse – takes the generalisable stuff from the researchers and provides it – platform for citizen science.

Can no longer rely on media to get message out and drive engagement – “it’s on the internet isn’t it amazing” no longer a story – need to work out how we get the next 300,000 people involved [my first thought – Games – look at Farmville…]