Citizen science’s transformative impact on science, citizen empowerment and socio-political processes

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

Improved upper limb function in non-ambulant children with SMA type 2 and 3 during nusinersen treatment: a prospective 3-years SMArtCARE registry study

Background The development and approval of disease modifying treatments have dramatically changed disease progression in patients with spinal muscular atrophy (SMA). Nusinersen was approved in Europe in 2017 for the treatment of SMA patients irrespective of age and disease severity. Most data on therapeutic efficacy are available for the infantile-onset SMA. For patients with SMA type 2 and type 3, there is still a lack of sufficient evidence and long-term experience for nusinersen treatment. Here, we report data from the SMArtCARE registry of non-ambulant children with SMA type 2 and typen 3 under nusinersen treatment with a follow-up period of up to 38 months. Methods SMArtCARE is a disease-specific registry with data on patients with SMA irrespective of age, treatment regime or disease severity. Data are collected during routine patient visits as real-world outcome data. This analysis included all non-ambulant patients with SMA type 2 or 3 below 18 years of age before initiation of treatment. Primary outcomes were changes in motor function evaluated with the Hammersmith Functional Motor Scale Expanded (HFMSE) and the Revised Upper Limb Module (RULM). Results Data from 256 non-ambulant, pediatric patients with SMA were included in the data analysis. Improvements in motor function were more prominent in upper limb: 32.4% of patients experienced clinically meaningful improvements in RULM and 24.6% in HFMSE. 8.6% of patients gained a new motor milestone, whereas no motor milestones were lost. Only 4.3% of patients showed a clinically meaningful worsening in HFMSE and 1.2% in RULM score. Conclusion Our results demonstrate clinically meaningful improvements or stabilization of disease progression in non-ambulant, pediatric patients with SMA under nusinersen treatment. Changes were most evident in upper limb function and were observed continuously over the follow-up period. Our data confirm clinical trial data, while providing longer follow-up, an increased number of treated patients, and a wider range of age and disease severity

Weight, temperature and humidity sensor data of honey bee colonies in Germany, 2019–2022

Humans have kept honeybees as livestock to harvest honey, wax and other products for thousands of years and still continue doing so. Today however, beekeepers in many parts of the world report unprecedented high numbers of colony losses. Sensor data from honey bee colonies can contribute to new insights about development and health factors for honey bee colonies. The data can be incorporated in smart decision support systems and warning tools for beekeepers. In this paper, we present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities. We provide the raw data with a measurement interval of up to 5 s as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony. The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies

Weight, Temperature and Humidity Sensor Data of Honey Bee Colonies in Germany, 2019 - 2022

<p>Files:</p> <p>bob_publication_data.zip<br>Sensor data in 1-minute, 1-hour and 1-day interval, processed and unprocessed version as described in data paper. Inspection data with beekeepers' meta-information collected using web app.</p> <p>bob_raw_data.zip<br>Raw sensor data with original measurement interval, mostly 5 or 10 seconds.</p> <p>bob_code_publication.zip<br>Code we used to prepare the data. We anonymised sections with personal keys and passwords.</p> <p>Abstract:</p> <p><br>We present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities.<br>We provide the raw data with a measurement interval of up to 5 seconds as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony.<br>The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies.</p> <p>Information:</p> <p>The first three years of the citizen science project were funded by the German Ministry for Education and Research (BMBF).</p&gt