Decoding NAture: Balancing Student Participation and Cutting-Edge Research

The Decoding NAture project is a unique citizen science partnership between the Natural History Museum (NHM, London) and UK schools, established in 2009. It is designed to immerse children into the life of a real scientist... [ view full abstract ]

The Decoding NAture project is a unique citizen science partnership between the Natural History Museum (NHM, London) and UK schools, established in 2009. It is designed to immerse children into the life of a real scientist whilst gathering high quality publishable data. Initially, the project (then called ‘Tree School’) focused on investigating the floral diversity of native trees using DNA barcoding. By design, the project provided an opportunity to be involved in the entire process of data generation during week-long residential courses held at a purpose-designed science education centre.

The project has been successful in many ways, particularly the deep level of involvement for the children and the quality of data produced. However, the time frame required for this approach meant that the rate of data production sufficient for publication conflicted with the rapid progression of DNA barcoding developments.

In this session I will share our lessons learned and show how the evolution of the project over the past six years has overcome these challenges. Utilising the feedback gathered from all involved parties we have been able to achieve the ideal balance between inclusivity and the production of high quality scientific data. We have had successful results, with extensive benefits for students and scientists alike. Engaging the students in all aspects of the process enables them to become real scientists and see the relevance of their work for the environment and current research. The data collected has led to new findings with implications for taxonomy and conservation in the UK.

It is hoped that this approach will be applicable to other citizen science projects, and will encourage discussion about the merits of this concept, especially how to maximise the balance between participant learning and the volume of data generated.