Many researchers have recruited members of the public to help them collect data (crowdsourcing) or conduct experiments (citizen science). These types of studies provide researchers with unique and rich datasets while also engaging the public in the process of scientific discovery. However, such studies also present many data analysis challenges. This project is developing statistical strategies to help guide researchers who are interested in engaging in these types of projects.
Explaining the science
The data generated from crowdsourced or citizen science experiments must be analysed carefully. Data processing and quality control poses major challenges. For instance, different citizen scientists might have different levels of reliability, and taking this into account should help researchers model the level of uncertainty in their conclusions. In addition, researchers must consider the privacy of the participants and issues around informed consent.
- Analyse a series of case studies in which researchers collected data from primary school students across the United Kingdom. Specifically, investigation will be conducted into what steps were taken to design the experiments, and process and interpret the data. The steps taken towards preserving the privacy of the students and schools will also be assessed.
- Develop a set of guidelines that describe the best practices around performing and analysing crowdsourced and citizen science experiments.
- Design a new citizen science or crowdsourced science experiment, with the goal of educating the public about ‘big data’ and ‘data science’, while providing useful data sets for Turing Researchers.
As a consequence of this research, this works aims to promote higher standards of data analysis of crowdsourced and citizen science data.
Crowdsourced and citizen science is an excellent way to teach the public about the process of scientific inquiry, while developing unique sources of data for researchers. This type of research could benefit organisations such as the BBC who host many crowdsourced and citizen science experiments, including many in schools.
Other large-scale crowdsourced science experiments run by universities, charities, and government departments provide important data about topics such as biodiversity, air/water quality, and astronomy, that would have been very difficult to collect otherwise.
March 2018: Project received seed funding from The Alan Turing Institute.