Case Study: Aurorasaurus

Liz MacDonald, NASA, Aurorasaurus

I am the founder of a project called Aurorasaurus. I am a scientist, a physicist, and this citizen science project was designed to allow people to get a better idea of when they could see the northern lights. The reason we needed this was because there are no products designed for the public to do this right now, and the public is very interested in seeing the aurora, they’re tweeting about it. We wanted to put those observations on a map and do better at informing more people about when this visibility was available to them. It is a rare event that happens in a relatively short time period, but it is also a really large event that happens globally.

We have built a platform for doing that, and we have interdisciplinary projects with Goddard, with a nonprofit called the New Mexico Consortium, and with Penn State University, where Andrea Tapia is interested in how this rare event that we are forecasting and the notifications that we give people might help them build better early warning systems for other types of rare events, disasters that you can’t simulate. So this might be a closed-loop kind of example.

The participants sign up to get a location based alert near them when people actually see something; then they can report that on the website or our apps. We are also getting observations off of Twitter, and you can upload or download these tweets which are related to our topic and find the needle in the haystack that way in terms of finding when somebody has just tweeted, “I just saw the Aurora,” versus, “I just want to see the Aurora.” That is why we need people to help us do that. We also have some really basic gamification with points—you get points for interacting with the website.

This is definitely an emerging project. We have been live for under a year. There was a really large space event of aurora being visible very far south in the Northern Hemisphere on St. Patrick’s Day, so we saw 100% increase in the number of users and collected a lot of data that can be useful as ground truth for the activity of the aurora right now. It’s useful for scientists and for people to help them prove the forecasts that they get.

We resemble the Venn diagram Mary Flanagan showed during her presentation. We have space science, informal science education and human-centered computing, and here we are in the middle. One disappointment, I think, is that you think of the Venn diagram as being equally distributed, and there aren’t that many people in the middle. We are trying to encourage and find people who are interested and get the word out about this and at least in my field, as a space scientist, people are not on Twitter at all. They are not seeing this as a real data source, so we are changing the perception by showing them the data that we have. That is a challenge, but a good challenge.

And as Neil said, there is not a lot of out-ofthe-box for this now. I am very interested in learning more about what people here have been doing. We have a small team and this was a totally different project for us, so the best practices in terms of building software or an app are also a challenge.

There have been a couple of unexpected outcomes. There is certainly advocacy for this very small niche field, and this is a good way to engage with the public. We have also made connections with people around the globe who are already doing this. They are hunting the aurora and photographing it and they are very, very good at it and interested in being connected to others who are doing that as well.

This presentation was a part of the workshop Engaging the Public: Best Practices for Crowdsourcing Across the Disciplines. See the full report here.