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It’s easy to think that the average person can’t do much for science with all of the big expensive facilities and very powerful computers around, but this is not the case and there is certainly space for all of us to get involved. All you need is an internet connection, a brain and a willingness to contribute to the discovery of something amazing and understand more about our universe. Us humans have an amazing ability to recognise patterns. Our brains are designed for it, we evolved this way so we were good at protecting ourselves from danger. Pattern recognition has made a us achieve a remarkable amount and, so far, computers just can’t quite keep up. Watch this space though, computers are getting smarter, faster and better at pattern recognition, but before they do there’s an opportunity for us all to be involved in understanding the universe a bit better through citizen science.
The success and accuracy of citizen science projects depends on the simplicity of the task, a too complex question will decrease accuracy and ultimately harm the credibility of any result. Helpfully, there’s a bunch of citizen science projects grouped together on Zooniverse that are structured around the inherent visual identification and pattern recognition skills we all have. The science is in the construction on the tasks and follow on analysis, citizen science participants are often contributing through their skills in pattern recognition to help classify data that leads to further analysis. Below, we have a closer look at four of the projects. All four of the projects rest on the observation that the combined assessment of many non-experts can often equal or even outperform experts and do outperform computers, if the question being as asked is thought about correctly.
About 10 years ago I logged on to Galaxy Zoo to see what all of the hype was about and quickly found myself immersed in pictures of galaxies from the Sloan Digital Sky Survey. At the time, when presented with a picture of a galaxy, I had to determine if it was elliptical, spiral or two galaxies merging. If it was a spiral galaxy then I had to say which way the arms were going, clockwise or anti-clockwise. It didn’t take long to get through many thousands, I think I did about 80,000 classifications over a six month period and I felt like I really contributed something. It was easy, sitting with my big clunky laptop clicking away through thousands of galaxy photographs and learning about the universe at the same time. You can’t look at that many photos and not get your interest sparked into wanting to learn more. So I did, and I bought a telescope so I could look at galaxies myself!
Galaxy Zoo has gone through a few iterations since I was last on it and now gets participants to classify features of galaxies from Galaxy and Mass Assembly Survey (GAMA) and the Kilo-Degree Survey (KIDS). The sort of work is to classify pictures like the one below, which is from the tutorial on Galaxy Zoo 3D.
The participation in Galaxy Zoo has made some real contributions to science with a large number of published articles and time on Hubble Space Telescope dedicated to further investigating interesting finds. One of the more famous examples was during the early years of Galaxy Zoo when Hanny’s Voorwerp was discovered by a Dutch school teacher, Hanny van Arkel in 2007 (see the featured image above, captured by the Hubble Space Telescope). It turned out to be a rare image of a quasar ionisation echo.
NASA has a couple of spacecraft on heliocentric orbits called STEREO A and B which observe the Sun. Photographs are uploaded to the project and participants are asked to help with detailed analysis of solar wind patterns caused by storms on the Sun. Version one of the project built a big catalog of solar storms and led to the publication of 7 papers. The second version of the project (Solar Storm Watch II) aims to characterise the shape of the storms outer edge. This will help determine the length of time it takes for the storm’s effect to be felt on Earth.
It was a bit tricky for me to draw the shape but the interface was easy to use and tutorial is quite good.
The power of these sorts of crowd sourced work is that multiple participants review the same answer so that an comparison can be made between the different results. In the case of mapping storm fronts, there are likely to be variable tracings between participants but this can be moderated by comparing the collective answer across a wider number, hopefully giving a better result.
Exoplanet Explorers allows participants to review the results from the Kepler mission which examines stars for evidence of exoplanets. Basically the Kepler collected the light from stars to see if there was a dip in brightness as a planet crosses in front of the star. This is measured in a graph (like the one below taken from the Exoplanet Explorers tool). Participants were then asked to identify any dip in brightness in the zoomed in graph where the superimposed blue line represents an identifiable trend. This can be subjective so the trick is to have as many participants as possible so they get the best group answer.
Like Solar Storm Watch, it’s very easy to use and the training is via a very easy to step through tutorial. This project has found a number of planets with the Kepler data. In a period of 48 hours with a team of 10,000 volunteers the project discovered 116 gas giant exoplanets and 68 potential rocky planets in the habitable zone. These aren’t necessarily discoveries until the results are refined through further tests of the data. This is a great example of how these projects add to our understanding of the universe.
The the famous KIC 8462852 aka Tabby’s Star was found with a similar programme: Planet Hunters, and data from Kepler that subsequently pointed it to the astronomers for further study.
The Planet Four: Ridges project aims to identify features where ground water may have seeped to the surface and left mineral deposits that have formed polygonal ridge networks, and may provide evidence of water flow on Mars. There are a number of processes that can cause these patterns (shown below in the photo from the tutorial in the Planet Four Ridges project) including lava flows, meteorite impact effects and water. The polygonal ridge networks create a distinctive pattern that computers struggle to recognise but people have no problem with.
The previous project looked at these ridge patterns in an area of Mars called Arabia Terra; the new project aims to compare those results with classification of similar patterns found in Sinus Meridiani (just south of the equator near Arabia Terra). The aim is to map where these areas are, compared to other attributes, like ancient terrains or minerals associated with the presence of water. Like the other projects, the interface is very easy to use and after a quick tutorial you’ll be up and running.
In conclusion, here are four ways to get involved, in citizen science, to make life easier, there are even apps for your phone so you can contribute to science whilst on the move, but not while you’re driving please.
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