Expanding involvement of the public in citizen science projects can benefit both volunteers and professional scientists alike. Recently, citizen science has come into focus as an important data source for reporting and monitoring United Nations Sustainable Development Goals (SDGs). Since bees play an essential role in the pollination ecosystem service, citizen science projects involving them have a high potential for attaining SDGs. By performing a systematic review of citizen science studies on bees, we assessed how these studies could contribute towards SDG reporting and monitoring, and also verified compliance with citizen science principles. Eighty eight studies published from 1992 to 2020 were collected. SDG 15 (Life on Land) and SDG 17 (Partnerships) were the most outstanding, potentially contributing to targets related to biodiversity protection, restoration and sustainable use, capacity building and establishing multi stakeholder partnerships. SDG 2 (Zero Hunger), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities) were also addressed. Studies were found to produce new knowledge, apply methods to improve data quality, and invest in open access publishing. Notably, volunteer participation was mainly restricted to data collection. Further challenges include extending these initiatives to developing countries, where only a few citizen science projects are underway.

Although the quality of citizen science (CS) data is often a concern, evidence for high-quality CS data increases in the scientific literature. This study aimed to assess the data reliability of a structured CS protocol for monitoring stingless bees’ flight activity. We tested (1) data accuracy for replication among volunteers and for expert validation and (2) precision, comparing dispersion between citizen scientists and expert data. Two distinct activity dimensions were considered: (a) perception of flight activity and (b) flight activity counts (entrances, exits, and pollen load). No significant differences were found among groups regarding entrances and exits. However, replicator citizen scientists presented a higher chance of perceiving pollen than original data collectors and experts, likely a false positive. For those videos in which there was an agreement about pollen presence, the effective pollen counts were similar (with higher dispersion for citizen scientists), indicating the reliability of CS-collected data. The quality of the videos, a potential source of variance, did not influence the results. Increasing practical training could be an alternative to improve pollen data quality. Our study shows that CS provides reliable data for monitoring bee activity and highlights the relevance of a multi-dimensional approach for assessing CS data quality.

In citizen science, training and evaluation are important steps in improving the pedagogical effectiveness of projects. However, evaluating learning outcomes is complex and usually requires multidimensional analysis. In this work, we analyze the influence of the profile of citizen scientists (i.e., gender, age, level of education, educational background, prior experience in beekeeping, and level of contact with nature) on their learning, considering multiple dimensions (i.e., knowledge of the nature of science, self-efficacy, knowledge of scientific process and content, interests, values and priorities, and motivations). Citizen scientists participated in a training course that focused on meliponiculture and citizen science, and they performed a contributory citizen science protocol. The evaluation was based on pre- and post-course questionnaires, (reaching 886 respondents). Learning was observed in all dimensions and, depending on the dimension, profile variables, such as gender, educational level, educational background, and prior experience in meliponiculture were influential. Participants demonstrated high levels of nature connectedness, strong personal norms, pro-environmental behavior(al intention), and high levels of trust in science. The main motivations of the participants were to learn, assist in conservation, and contribute to the production of scientific knowledge about bees. Our findings offer insight into the subsequent steps and future training courses for the #cidadãoasf citizen science project, but they could also be beneficial to other initiatives, indicating the importance of the evaluation of volunteer profiles to guide improvements in a project’s quality.

Para mensurar a qualidade do biodiesel no Brasil, vários parâmetros são estabelecidos. Alguns deles como viscosidade, índice de iodo, número de cetano, densidade são importantes pois caracterizam funções importantes de como o biodiesel irá reagir no motores. Neste estudo, foram usadas Redes Neurais Artificiais (RNAs) para predizer o índice de viscosidade do biodiesel. Para este fim foram utilizadas 13 compostos de esteres deácidos graxos como entrada para as RNAs, com vários algoritmos de convergência do tipo feedforward tendo como sáıda das redes os índices de viscosidade.