{"id":828,"date":"2010-06-09T11:16:36","date_gmt":"2010-06-09T10:16:36","guid":{"rendered":"http:\/\/www.meanboyfriend.com\/overdue_ideas\/?p=828"},"modified":"2010-06-09T11:16:36","modified_gmt":"2010-06-09T10:16:36","slug":"sort-working-the-crowd-galaxy-zoo-the-rise-of-the-citizen-scientist","status":"publish","type":"post","link":"http:\/\/www.meanboyfriend.com\/overdue_ideas\/2010\/06\/sort-working-the-crowd-galaxy-zoo-the-rise-of-the-citizen-scientist\/","title":{"rendered":"SORT – Working the crowd: Galaxy Zoo & the rise of the citizen scientist"},"content":{"rendered":"

I’ve been looking forward to this session by Chris Lintott on\u00a0Galaxy Zoo<\/a><\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

After 48 hours were classifying as many galaxies in 1 hour as a student previously doing in a month.<\/p>\n

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.<\/p>\n

The huge side effect is that humans can spot unexpected stuff without being told – much better than machines.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Chris describes three strands of engagement with users<\/p>\n