iSpot: Crowdsourcing effective for gathering biodiversity data

New research, undertaken by the OU, looking into the success of the iSpot model has found that the structure of the platform's social network to be a key feature. iSpot combines learning technology with crowdsourcing, enabling beginners to connect with experts, and leading to plant and wildlife species being identified accurately.

Over 94% of observations submitted to iSpot receive some sort of identification with more than half being named within an hour. Using a unique process based on an iSpot user's 'reputation', the platform motivates iSpotters to verify species and rewards them for doing so. In 57% of such cases the reputation system improved the accuracy of the determination.

The research concluded that by effectively connecting users in this way they are able overcome the social as well as geographic barriers that prevent the sharing of knowledge.

Janice Ansine, Citizen Science Project Manager at The Open University and iSpot manager said: "Being able to correctly identify plant and wildlife is a key skill to furthering our understanding of biodiversity, but sadly one that tends to be neglected in formal education at all levels. This research shows that a social network such as iSpot is an incredibly effective tool in not only connecting nature lovers from across the globe, but also in capturing invaluable biodiversity data and insight."