Citizen science, training, data quality and interoperability More and more people are interested in participating in citizen-science projects, and the technology is becoming more accessible. Data quality is essential for citizen-science projects. Without quantified-quality data, the results of citizen science projects cannot be trusted. There are several challenges to ensuring data quality in citizen-science projects, such as participant motivation and training, data-entry errors, and environmental factors. These challenges can be addressed by using innovative technologies, such as artificial intelligence, and by developing better training methods. Mobile devices are becoming increasingly powerful and sophisticated, and they are making it easier for participants to collect data anywhere and anytime. Artificial intelligence is being used to develop new tools that can automatically analyse data and identify patterns. This makes it easier to identify and correct data errors. Online communities are providing a space for citizen scientists to connect with each other, share data, and learn from each other. This is helping to improve the quality of data collected by citizen-science projects. Citizen-science projects are increasingly aware of the importance of data ethics. This is leading to the development of new standards and guidelines for collecting and using citizen science data.