Citizen Science Conference
Meredith has been busy this past week attending the Citizen Science conference in St Paul, Minnesota. She reports back that it was a fantastically stimulating conference that confirms the high esteem that citizen science has grown within the science community.
The yearly conference sees a diverse group of people from researchers, educators and universities to the likes of NGO’s and museums get together to discuss the use and promotion of citizen science. Although we at Snapshot Serengeti have been aware of its great impact for some time citizen science is now emerging and is recognised as a powerful tool in the advancement of research by many.
Those attending the four day event collaborated by sharing their varied experience and ideas on a variety of topics. The collection and sharing of data and how to impact policy was discussed. There was focus on how to use citizen science as an engaging teaching tool, how to bring citizen science to a wider audience and how to involve citizens more in research. Those attending brought their joint experience and expertise together to discuss how citizen science impact on science could be measured and evaluated. If you want to find out more about the conference then visit this link.
We sometimes forget when working away at classifying our stunning images on Snapshot Serengeti that there is a lot of tech going on that enables us citizen scientists to be of use to the scientists. Meredith gave what’s known as a ‘project slam’ essentially a 5 minute presentation about our work on Snapshot Serengeti and how it has paved the way for helping other cameratrap citizen science projects. A quick look around Zooniverse will show just how many there are now.
The massive amount of data produced over several seasons through Snapshot Serengeti have allowed the development of a robust, tried and tested methodology that smaller projects would have taken years longer to develop. Just contemplate the work that went into developing interfaces, protocols, pipelines and algorithms for taking millions of classifications of untrained volunteers and turning them into a dataset which has been verified to be >97% accurate.
It is awesome to see that something we all find so truly engaging can translate into such serious stuff in the field of science. I think we, the citizen scientists, and the Snapshot team can be rightly proud of our work on this brand new branch of science