Tag Archives: Science

1,000,000 Classifications and 7 Languages

The Milky Way Project has now passed one million classifications since its relaunch a few months ago. The project is currently 75% complete, meaning there are still many, many images left to classify. Which is fine because in fact the project has become truly international lately – with citizen scientists around the world now able to participate in English, Spanish, German, French, Indonesian, Polish and Danish. There are more languages on the way too!

So to celebrate passing our 1,000,000 milestone I thought I’d share the homepage counter in all seven languages:

If you’re interested in helping to translate the Milky Way Project then get in touch with rob@zooniverse.org – and there are man other translatable project too!

New Milky Way Project Poster

I’ve been diving into the bubbles database recently and ended up creating cutouts of all 3,744 large bubbles from the DR1 data release. From there it was an easy enough job to create this new Milky Way Project poster. It uses all 3,744 bubbles at least once (several are used more than once).

MWP Logo Mosaic of Bubbles

I’m currently working on three new Milky Way Project papers and will be blogging about them in the next weeks and months.

We’re Not Cloud Painters

A current hot topic in star-formation science is the study of Infrared Dark Clouds (IRDCs). These are really dense molecular clouds that appear dark even in infrared surveys.

For a while, we hoped that Project IX would be our avenue to exploring IRDCs. However, as you’ll see in this blog post, not every idea become a reality at the Zooniverse – for a good reason. Here’s the story of the IRDC project that never was.


A Bit About IRDCs

IRDCs appear throughout images from far-infrared surveys – there’s a lovely example in the image above. It was not initially known what lay within them. Closer study revealed that they have sizes and masses similar to high-mass star forming molecular clouds. Similarly, the dense cores within IRDCs appear to match the sizes and masses of high-mass prestellar cores – the direct progenitors of stars. IRDCs also seem to be located along the spiral arms of our Galaxy, which is where star formation mostly occurs (Jackson et al, 2008, see image below). A few IRDCs even show evidence that they contain young proto-clusters of stars. In short, evidence seems to suggest that early phase high-mass star formation is occurring within IRDCs.

High-mass star formation is another hot topic in astrophysics. It is not fully understood how stars several or hundreds of times bigger than the Sun form. We know that they do and that they can get really big – but compared to the process of low-mass star formation their origins are a bit of a mystery. IRDCs may hold some answers.


It has been established that there are upward of 10,000 IRDCs visible to us here on Earth (Simon et al. 2006a). For each of these it would be useful to know their size, shape and location. The Spitzer Space Telescope’s Galactic Legacy Midplane Infrared Survey Extraordinaire (GLIMPSE) highlights many of these often beautiful dark clouds and the data is available for use. The question is how do you visually classify this many dark clouds in such a large dataset?

Project IX

This started to sound to me like a potential citizen science problem. Lots of objects to be found, a task that computers find difficult, and a large dataset. We got really excited here at Zooniverse HQ, and began to concoct an idea that would see tens of thousands of volunteers literally painting clouds in space. I developed a prototype HTML5 interface that allowed someone to draw around IRDCs in GLIMPSE data (se image below). Sadly, our enthusiasm wasn’t to last.


We had a prototype interface and we had lots of GLIMPSE data – but Chris was worried. I kept bringing him a new developments and he’d be interested, excited but also cautious. So we sat down to really get to the bottom of this idea. Imagine 1,000 people were to draw around the same IRDC in an image. Each person looks at the image and decides in their own mind where they would say the border between the dark cloud and the surrounding bright emission is. They then draw around the cloud, fairly imperfectly, following roughly that ratio of dark-to-light.

Translating this into a different vocabulary: each person decides on a contrast ratio and tries to follow a fixed-contrast contour around the dense region in the image. We then average those contours to get the group’s decision on the best contrast ratio to use.

Have you spotted the problem? The average contrast ratio achieved by this method is no more right or wrong than any other value. You may as well have taken a computer and told it to draw a contour at a specified contrast ratio across the whole Galaxy. You can derive that ratio via some meaningful number that can be calculated from the data – maybe the extinction or the signal-to-noise. The 2006 paper that identified the Galaxy’s 10,000+ IRDCs already did this – instead of improving upon the existing study, we would have ended up replicating it, only in a slightly different way. We compared our own visually identified clouds to those drawn out by the Simon et al., 2006 algorithm and found the results were very similar. As such the cloud painting project was more-or-less concluded there and then.

Moving On

The Zooniverse has policies on what makes a good citizen science project. These guidelines have been produced following the lessons of Galaxy Zoo and other projects. Chris wrote up a blog post about this the other day. Our one unbreakable rule is that if we ask the public to collaborate on a project, their efforts must produce a meaningful result. We must never waste people’s time.

I’ll be honest, I was a bit gutted. Cloud painting would be possible and it would yield a reasonable result. It would even be fun! However it wouldn’t add anything scientifically useful to what we know about the Galaxy’s IRDCs. Just because a problem can be crowd-sourced doesn’t mean it should be.

Luckily for Project IX, there is a lot more to see in the GLIMPSE data than just IRDCs. So we leave the dark clouds to the machines and in our next post we’ll finally talk about the great science that we can achieve.

If you want to learn more about IRDCs there are lots of papers and talks on the subject out there on the web. If you’d like to read some of the papers here’s a potted history of IRDCs – Simon et al. 2006aRathborne et al., 2006 and 2007Simon et al., 2006bJackson et al, 2008and Chambers et al., 2009.