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Participatory science and amateur participation in scientific data collection and work has been common for hundreds of years, but has become a more formalised field of practice in recent decades. The inclusion and reliance on informally trained members of the public in scientific endeavours has especially helped connect natural history collections to the general public. In recent decades, the term used to describe these participants — citizen scientists — was intended to unite formal and informal scientists as global citizens working towards a common goal. However, the term 'citizen' today has negative connotations for many members of the public and can have a polarising effect on certain individuals. Given that the nature of participatory science is to be inclusive and inviting, it is time to change this terminology. The term 'community' science has been suggested as an alternative by some practitioners and programmes. This self-awareness within the scientific community is important, but lacks impact without input from the community members potentially participating in these programmes. We addressed this knowledge gap by posing the question of term preference to groups of volunteers who have attended participatory science activities from the Field Museum of Natural History (Chicago, Illinois, USA) and the Natural History Museum of Los Angeles County (Los Angeles, California, USA) from 2019 to 2023. A majority of respondents showed a clear preference for the term 'community' over 'citizen' science. This was especially true for younger individuals and those who belong to ethnic groups other than White. This information can impact which terms are used for specific programme populations and supports community involvement in selecting terminology and in project design. We advise stopping use of the term 'citizen' in all participatory science programmes and adopting terminology that is most appropriate depending on region, research, audience and activity. Moreover, participant populations should be solicited to hear their voices. 

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups. 

The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on COVID-19 case prediction tends to focus on performance improvements, masking relevant insights about when mobility data does not help, and more importantly, why, so that it can adequately inform local decision making. In this article, we carry out a systematic analysis to reveal the conditions under which human mobility data provides (or not) an enhancement over individual regional COVID-19 case prediction models that do not use mobility as a source of information. Our analysis—focused on U.S. county-based COVID-19 case prediction models—shows that (1) at most, 60% of counties improve their performance after adding mobility data; (2) the performance improvements are modest, with median correlation improvements of approximately 0.13; (3) improvements were lower for counties with higher Black, Hispanic, and other non-White populations as well as low-income and rural populations, pointing to potential bias in the mobility data negatively impacting predictive performance; and (4) different mobility datasets, predictive models, and training approaches bring about diverse performance improvements.