Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status and challenges for its development is needed. Using quantitative indicators developed in a collaborative stakeholder process, our study provides a comprehensive overview of the current status of CS in Germany, Austria and Switzerland. Our online survey with 340 responses focused on CS impact through (1) scientific practices, (2) participant learning and empowerment, and (3) socio-political processes. With regard to scientific impact, we found that data quality control is an established component of CS practice, while publication of CS data and results has not yet been achieved by all project coordinators (55%). Key benefits for citizen scientists were the experience of collective impact (“making a difference together with others”) as well as gaining new knowledge. For the citizen scientists’ learning outcomes, different forms of social learning, such as systematic feedback or personal mentoring, were essential. While the majority of respondents attributed an important value to CS for decision-making, only few were confident that CS data were indeed utilized as evidence by decision-makers. Based on these results, we recommend (1) that project coordinators and researchers strengthen scientific impact by fostering data management and publications, (2) that project coordinators and citizen scientists enhance participant impact by promoting social learning opportunities and (3) that project initiators and CS networks foster socio-political impact through early engagement with decision-makers and alignment with ongoing policy processes. In this way, CS can evolve its transformative impact.
This is the first version of the White Paper Citizen Science Strategy 2030 for Germany. This version is out for consultation from 8/8/21-30/9/2021 and will then be revised with received comments and reviews accordingly. Consultation website: www.citizen-science-weissbuch.de
We describe the system design and setup of our digital twin of the socialecological system urban beekeeping, with the aim to support agroecological methods in urban agriculture. The physical space consists of the bee populations, their beekeepers who are part of a beekeeping community, non-beekeepers who consume honey, organisational actors shaping rules and regulations and the environment. The virtual space is a multi-agent model, where autonomous agents can take actions and make decisions in partially observed Markov processes. To tie the physical and the virtual space, we embedded bee hives in an IoT environment and implemented an online documentation tool as a web application, where beekeepers take short notes about their work and observations. Bee hives are equipped with sensors, such as humidity, pressure and temperature sensors and a scale. Additionally, we pull data from the German weather service (Deutscher Wetter Dienst, DWD). In our system architecture, multiple levels on data fusion are performed, beginning with raw data quality estimation and sensor failure detection. On higher levels, states of entities are estimated, such as the health of a bee colony, and assessment made whether a state is normal or to be considered an anomaly. Finally on the highest level, we deal with the desires of our agents, how actions should be chosen in order to achieve or maintain desirable and rewarding world states. We hope to be able to refine our digital twin into a decision support tool for small-scale (bee) farmers and communal political actors that helps to reach desirable world states by predicting and simulating the effects of actions within the complex system of urban beekeeping.
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