The “noisy labeler problem” in crowdsourced data has attracted great attention in recent years, with important ramifications in citizen science, where non-experts must produce high-quality data. Particularly relevant to citizen science is dynamic task allocation, in which the level of agreement among labelers can be progressively updated through the information-theoretic notion of entropy. Under dynamic task allocation, we hypothesized that providing volunteers with an “I don’t know” option would contribute to enhancing data quality, by introducing further, useful information about the level of agreement among volunteers. We investigated the influence of an “I don’t know” option on the data quality in a citizen science project that entailed classifying the image of a highly polluted canal into “threat” or “no threat” to the environment. Our results show that an “I don’t know” option can enhance accuracy, compared to the case without the option; such an improvement mostly affects the true negative rather than the true positive rate. In an information-theoretic sense, these seemingly meaningless blank votes constitute a meaningful piece of information to help enhance accuracy of data in citizen science.

Advancements in computer‐mediated exercise put forward the feasibility of telerehabilitation, but it remains a challenge to retain patients' engagement in exercises. Building on our previous study demonstrating enhanced engagement in citizen science through social information about others' contributions, we propose a novel framework for effective telerehabilitation that integrates citizen science and social information into physical exercise. We hypothesized that social information about others' contributions would augment engagement in physical activity by encouraging people to invest more effort toward discovery of novel information in a citizen science context. We recruited healthy participants to monitor the environment of a polluted canal by tagging images using a haptic device toward gathering environmental information. Along with the images, we displayed the locations of the tags created by the previous participants. We found that participants increased both the amount and duration of physical activity when presented with a larger number of the previous tags. Further, they increased the diversity of tagged objects by avoiding the locations tagged by the previous participants, thereby generating richer information about the environment. Our results suggest that social information is a viable means to augment engagement in rehabilitation exercise by incentivizing the contribution to scientific activities.

The advent of automated tracking software has significantly reduced the time required to record movement trajectories, thereby facilitating behavioral studies of zebrafish. However, results are substantially influenced by tracking errors, such as loss and misidentification of individuals. In this study, we present the development of an online citizen science platform, Tracking Nemo, to improve data accuracy on swimming trajectories of zebrafish groups. As an online extension of software for tracking the position of zebrafish from video recordings, Tracking Nemo offers volunteers the opportunity to contribute to science by manually correcting tracked trajectory data from their personal computers. Researchers can upload their videos that require human intervention for correcting and validating the data. Citizen scientists can monitor their contributions through a leaderboard system, which is designed to strengthen participant retention and contribution by tapping into intrinsic and extrinsic motivations. Tracking Nemo is expected to help scientists improve data accuracy through the involvement of citizen scientists, who, in turn, engage in an authentic research activity and learn more about the behavior of zebrafish.