Defining community-led monitoring and its role in programme-embedded learning: lessons from the Citizen Science Project in Malawi and South Africa.

INTRODUCTION: Programme Science (PS) and community-led monitoring (CLM) intersect in unexpected and promising ways. This commentary examines a CLM initiative in Malawi and South Africa to highlight the crucial role of CLM in bolstering the PS framework. By leveraging data sources often overlooked by conventional research and evaluation approaches, CLM emerges as a pivotal element in enhancing programme effectiveness. This paper delineates the fundamental principles of CLM, presents programme outcomes derived from CLM methodologies and contextualizes these findings within the broader framework of PS. DISCUSSION: The Citizen Science Project implements CLM continuously at 33 health facilities: 14 in Malawi (eight in Kasungu District and six in Dedza District), and 19 in South Africa (all in the West Rand District), representing a total catchment area of 989,848 people. Monitoring indicators are developed in an iterative process with community groups. The indicators are unique to each country, but both focus on the uptake of health services (quantitative) and barriers to access (qualitative). Monthly clinic records surveys capture 34 indicators in Malawi and 20 in South Africa and are supplemented by qualitative interviews with care recipients and healthcare workers. Qualitative interviews provide additional granularity and help confirm and explain the more macro trends in service coverage as described in quantitative data. The resulting data analysis reveals key themes that help stakeholders and decision-makers to solve problems collaboratively. Noteworthy outcomes include a substantial increase in multi-month dispensing of antiretroviral therapy (ART) during COVID-19 (from 6% to 31%) with a subsequent recovery surpassing of HIV service benchmarks in Malawi post-pandemic. CONCLUSIONS: While quantifying direct impact remains challenging due to the project's design, CLM proves to be a robust methodology that generates credible data and produces impactful outcomes. Its potential extends beyond the health sector, empowering community leadership and fostering interventions aligned with community needs. As CLM continues to evolve, its integration into PS promises to improve relevance, quality and impact across diverse disciplines.

Exploring children and young people's experience of participating in citizen science-A qualitative evidence synthesis.

OBJECTIVE: Citizen science with young people is becoming increasingly popular, and understanding their experience is valuable as it can improve research through improved participant motivation/ retention, alongside greater insight. The participants can benefit through opportunities to improve self-efficacy, learning, communication, and relationships. However, studies that explore young people's experience of participating in citizen science have not been synthesised. METHODS: This qualitative thematic synthesis aimed to combine the literature on young citizen scientists' experience of participating in citizen science research studies. Seven databases, Google Scholar and The Journal of Citizen Science Theory and Practice were searched from 2012 to January 2022 and updated in May 2023. The screening included identifying articles by scanning titles and abstracts and, finally, full texts and selecting the articles using inclusion and exclusion criteria. The study findings were synthesised using inductive thematic synthesis (Prospero registration CRD42022299973). RESULTS: Out of 3856 identified articles, 33 studies focusing on the participant experience were included in the synthesis. These papers were coded inductively. The resulting analytical structures were discussed and finalised. The researchers identified three main themes representing aspects of participant experience: relationships, power and personal growth, and three interwoven connecting themes: communication, self-efficacy and decision-making. An illustration of this would be communication bridging relationships and personal growth. As the citizen scientists' communication skills developed through personal growth, their relationships changed both with the project, with the researchers and with wider stakeholders outside the project such as school staff. CONCLUSION: These findings provide a comprehensive understanding of participant experience and how this can be used to inform future citizen science projects to facilitate a positive participant experience.

A citizen science approach to develop a digital intervention to reduce HIV stigma and promote HIV self-testing among adolescents and young adults: a mixed methods analysis from Kazakhstan.

INTRODUCTION: Kazakhstan has one of the fastest-growing HIV epidemics in the world, with increasing rates among adolescents and young adults (AYA). Innovative strategies are needed to increase HIV testing uptake and decrease HIV stigma among AYA. Citizen science, defined as the active engagement of the general public in scientific research tasks, promotes and facilitates community engagement throughout the research process. This citizen science study used crowdsourcing to engage AYA in Kazakhstan to develop a digital intervention to reduce HIV stigma and promote HIV self-testing. Our objectives in this paper are to describe the approach used, its feasibility and acceptability, and AYA motivations for and lessons learned collaborating on the study. METHODS: From October 2021 to July 2022, in collaboration with a Community Collaborative Research Board and a Youth Advisory Board, we developed an open call requesting multimedia submissions to reduce HIV testing stigma. Eligible submissions were separated by age group (13-19 or 20-29 years) and judged by a panel composed of AYA (n = 23), healthcare professionals (n = 12), and representatives from the local government and non-governmental organizations (n = 17). Each entry was reviewed by at least four judges and ranked on a 5-point scale. The top 20 open call contestants were asked to submit self-recordings sharing their motivation for and experience participating in the contest and lessons learned. Descriptive statistics were calculated for quantitative data. Qualitative data were coded using open coding. RESULTS: We received 96 submissions from 77 youth across Kazakhstan. Roughly, three-quarters (n = 75/96) of entries met judging eligibility criteria. Of the eligible entries, over half (n = 39/75) scored 3.5 or higher on a 5-point scale (70.0%). The most frequent types of entries were video (n = 36/96, 37.5%), image (n = 28/96, 29.2%) and text (n = 24/96, 25.0%). AYA's primary motivations for collaborating on the study included a desire to improve society and help youth. The main challenges included creating content to address complex information using simple language, finding reliable information online and technological limitations. CONCLUSIONS: Crowdsourcing was feasible and highly acceptable among AYA in Kazakhstan. Citizen science approaches hold great promise for addressing the increasingly complex health and social challenges facing communities today.

Revealing biases in insect observations: A comparative analysis between academic and citizen science data.

Citizen Science is a powerful tool for biodiversity research, as it facilitates data recording at large scales that would otherwise be impossible to cover by standard academic research. Despite its benefits, the accuracy of citizen science data remains a subject of concern among scientists, with varying results reported so far. Neither citizen science data nor academic records are immune to biases, which can significantly impact the quality and reliability of observations. Here, using insects in the Iberian Peninsula as a case study, we compare data collected by participatory platforms to those obtained through academic research projects, and assess their taxonomic, spatial, temporal, and environmental biases. Results show a prominent taxonomic bias in both academic and citizen science data, with certain insect orders receiving more attention than others. These taxonomic biases are conserved between different participatory platforms, as well as between groups of users with different levels of contribution performance. The biases captured by leading contributors in participatory platforms mirrored those of sporadic users and academic data. Citizen science data had higher spatial coverage and less spatial clustering than academic data, showing also clearer trends in temporal seasonality. Environmental coverage over time was more stable in citizen science than in academic records. User behaviour, preference, taxonomical expertise, data collection methodologies and external factors may contribute to these biases. This study shows the multifaceted nature of biases present in academic records and citizen science platforms. The insights gained from this analysis emphasize the need for careful consideration of these biases when making use of biodiversity data from different sources. Combining academic and citizen science data enhances our understanding of biodiversity, as their integration offers a more comprehensive perspective than relying solely on either dataset alone, especially since biases in these two types of data are not always the same.

Synchronous citizen science with dogs.

Citizen science approaches have grown in popularity over the years, partly due to their ability to reach a wider audience and produce more generalizable samples. In dogs, these studies, though, have been limited in their controls over materials or experimental protocols, with guardians typically reporting results without researcher supervision. Over two studies, we explored and validated a synchronous citizen science approach. We had dog guardians act as experimenters while being supervised by a researcher over Zoom. In study 1, we demonstrated that synchronous citizen science produced equivalent levels of performance to in-lab designs in a choice task. Consistent with past in-lab research, dogs selected a treat (vs. an empty plate) in a two-alternative forced-choice task. In study 2, we showed that Zoom methods are also appropriate for studies utilizing looking time measures. We explored dogs' looking behaviors when a bag of treats was placed in an unreachable location, and dogs' guardians were either attentive or inattentive while dogs attempted to retrieve the treats. Consistent with past work, dogs in the attentive condition looked at their guardian for longer periods and had a shorter latency to first look than dogs in the inattentive condition. Overall, we have demonstrated that synchronous citizen science studies with dogs are feasible and produce valid results consistent with those found in a typical lab setting.

Fostering citizen-engaged HIV implementation science.

INTRODUCTION: Successful implementation of evidence-based practices depends on contextual factors like stakeholder engagement, the socio-political environment, resource availability, and stakeholders' felt needs and preferences. Nevertheless, inequities in implementation exist and undermine efforts to address HIV in marginalized key populations. Implementation science shows promise in addressing such inequities in the HIV response, but can be limited without meaningful engagement from citizens or communities. DISCUSSION: We define the concept of a citizen-engaged HIV implementation science as one that involves citizens and communities deeply in HIV implementation science activities. In this commentary, we discuss how citizen science approaches can be leveraged to spur equity in HIV implementation science. Drawing on three areas previously defined by Geng and colleagues that serve to drive impactful implementation science in the HIV response, we discuss how citizens can be engaged when considering "whose perspectives?", "what questions are being asked?" and "how are questions asked?". With respect to "whose perspectives?" a citizen-engaged HIV implementation science would leverage participatory methods and tools, such as co-creation, co-production and crowdsourcing approaches, to engage the public in identifying challenges, solve health problems and implement solutions. In terms of "what questions are being asked?", we discuss how efforts are being made to synthesize citizen or community-led approaches with existing implementation science frameworks and approaches. This also means that we ensure communities have a say in interrogating and deconstructing such frameworks and adapting them to local contexts through participatory approaches. Finally, when considering "how are questions asked?", we argue for the development and adoption of broad, guiding principles and frameworks that account for dynamic contexts to promote citizen-engaged research in HIV implementation science. This also means avoiding narrow definitions that limit the creativity, innovation and ground-up wisdom of local citizens. CONCLUSIONS: By involving communities and citizens in the development and growth of HIV implementation science, we can ensure that our implementation approaches remain equitable and committed to bridging divides and ending AIDS as a public health threat. Ultimately, efforts should be made to foster a citizen- and community-engaged HIV implementation science to spur equity in our global HIV response.

Indigenous people doing citizen science to assess water quality using the BMWP in rivers of an arid semi-arid biosphere reserve in Mexico.

Arid and semi-arid areas are among the most threatened ecosystems on the planet. The Tehuacán-Cuicatlán Biosphere Reserve (TCBR), in southeastern Mexico, is an arid and semi-arid area with high biological diversity and human settlements of eight ethnic groups. Two rivers drain the reserve, Río Grande (RG) and Río Salado (RS), which are not subject to water quality monitoring by government agencies; however, measures of water quality of these rivers are needed to focus conservation actions on this resource. This work aimed to test the effectiveness of participatory water quality monitoring with the participation of three actors: Reserve management leaders, local communities, and academics, to monitoring water quality in the rivers of the TCBR. Ninety-two residents were trained to carry out water quality biomonitoring using the Biological Monitoring Working Party (BMWP) index calibrated for the reserve. The BMWP uses macroinvertebrate families to display numerical and categorical water quality scores. Additionally, the Water Quality Index (WQI) was assessed and the Normalized Difference Vegetation Index (NDVI) of the riparian zones was estimated in each study site. The mean WQI scores were 69.24 for RS (no treatment necessary for most crops and necessary treatment for public water supply) and 75.16 for RG (minor purification for crops requiring high-quality water and necessary treatment for public water supply). The BMWP showed five water quality categories (Excellent, Very Good, Good, Fair, and Poor), showing higher water quality scores in the upper portion of the basins and capable of discriminating study sites with lower scores close to human settlements. At one study site, data from participatory monitoring impelled actions taken to address a pollution source and influenced policy focus, reaching the maximum level of participatory-based monitoring. This led to avoid the discharge of wastewater into the river to conserve and protect the water resource. WQI is closely related to BMWP; however, the latter was far more sensitive to detecting areas affected by domestic water discharges. The NDVI presented low values for the TCBR, being lower in RS (the driest area). Although the NDVI showed a weak relationship with BMWP values, areas with higher NDVI values generally achieved higher BMWP values. The results of this study highlight the high sensitivity of the BMWP to detect several water quality conditions in the rivers running through the TCBR when compared to WQI. In addition, the usefulness of biomonitoring using the BMWP index was evident, as well as the importance of the participation of local inhabitants contributing to the knowledge of water quality in biosphere reserves and carrying out timely measures that allow the rivers in these reserves to be maintained in good condition.

The pillars of the sea: strategies to achieve successful marine citizen science programs in the Mediterranean area.

Marine ecosystems are facing a dramatic loss of biodiversity worldwide, together with a widespread collapse of habitats and their functionality. In this context, Marine Citizen Science (MCS) can be a powerful tool to monitor these changes over time. The flowering of very well-structured international projects is strengthening the scientific credibility of MCS data, especially when data are collected after specifically designed training programs and shared in public user-friendly repositories. Here we present a new perspective on the use of MCS in the Mediterranean area, along with the main benefits for the stakeholders (i.e., diving centers, trainers, and policymakers) and the users (i.e., divers), resumed in three pillars: Pillar I - MCS as a tool for the site valorization; Pillar II - MCS as a new career opportunity for graduated students; Pillar III - MCS as a business opportunity for diving centers. In the frame of the Quintuple Helix Approach, for which there is a strong need of a socioecological transition of the society and economy, we show how MCS can be a win-win-win solution for all the actors involved, providing the vision for new and highly qualified job and business opportunities for the diving sector.

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Assessing the scale and ecological impact of derelict and discarded fishing gear across Thailand via the MARsCI citizen science protocol.

South-East Asia is among the least studied regions for the growing issue of marine debris pollution, despite being a major contributor towards global marine debris. In the present study, we provide the preliminary results from the MARsCI project, a survey protocol designed to utilise citizen science to facilitate data collection on the ecological impact of discarded fishing gear (DFG) in Thailand. Over a three-year period, 103 surveys were carried out across Thailand, resulting in impact assessment of 606 pieces of DFG. Our findings indicate corals are regularly impacted by DFG in Thai waters and that isolated marine habitats may be more severely impacted than near-shore sites. We further identify crabs, muricid snails, and demersal fish to be among the most regularly entangled animals. We discuss our findings in the context of earlier work from Thailand, and conduct a critical review of the protocol itself, identifying improvements for future efforts.

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

Citizen science in action: Time-resolved immunofluorescence-based field detection of antibiotics with portable analytical kit.

Citizen scientist-based environmental monitoring and public education are becoming increasingly popular. However, current technologies for antibiotic-based novel contaminant identification are still restricted to laboratory sample collection and analysis due to detection methodologies and apparatus limitations. This study developed a time-resolved immunofluorescence-based simultaneous field-based assay for ciprofloxacin (CIP) and enrofloxacin (ENR) that matches test results to geographic locations. The assay helps the public understand the potential levels of antibiotic exposures in their environments and helps them take appropriate action to reduce risk. The assay was developed using smartphones and social software in addition to rapid testing. The method uses a portable, low-cost analytical kit with a smartphone app to build a field-based detection platform for the detection and analysis of ENR and CIP in water and aquatic products. The methodological evaluation was good, with detection limits of 0.4 ng/mL and 0.5 ng/g for ENR in water and fish, and quantification limits of 1.2 ng/mL and 1.4 ng/g, with recoveries of 89.0 %-101.0 % and 78.0 %-97.0 %. For CIP in water and fish, the limits of detection were 0.3 ng/mL and 0.4 ng/g, the limits of quantification were 0.9 ng/mL and 1.2 ng/g, and the recoveries were 75.0 %-91.0 % and 72.0 %-89.0 %, both with coefficients of variation <15 %. These limits were sufficient to prevent the two antibiotics from crossing over during simultaneous detection. The assay was validated using real samples to assess the effectiveness of the assay platform in field deployments, and the results were consistent with those obtained through liquid chromatography-tandem mass spectrometry (LC-MS) and enzyme-linked immunoassay (ELISA) techniques. In addition, the TRFIA assay process requires less time, uses more portable instruments, and is less complex than traditional methods. This study provides a new scientific, accurate, and rapid detection method for antibiotic detection by citizen scientists, helping scientists to obtain a wider range of data and providing more opportunities to solve scientific problems.

Influence of an extreme event-the COVID-19 pandemic-On establishment of and data collection by a citizen science project.

The recent rising incidence of extreme natural events may significantly influence the implementation of citizen science projects, including the success of outreach strategies and the quality and scope of data collection. The MassMammals Watch and subsidiary MassBears citizen science projects, initiated during the height of the pandemic, recruit volunteers to submit sightings of black bears and other mammals. In this study, we evaluated the methods we employed for engaging and retaining community volunteers during a period of intense social restrictions, and we assessed whether such conditions were associated with spatial biases in our collected data. Newspaper features were more likely to recruit volunteers who engaged with the project multiple times, but social media and internet presence were important for reaching a larger audience. Bear sighting submissions peaked in number and were more likely to be in forested areas during 2020, the height of the pandemic, compared to later years, a pattern which we suggest stems from an increased desire to participate in outdoor activities in light of social distancing measures during that year. Such shifts in patterns of data collection are likely to continue, particularly in response to increasing extreme weather events associated with climate change. Here, we both make recommendations on optimal outreach strategies for others initiating citizen science programs and illustrate the importance of assessing potential biases in data collection imposed by extreme circumstances.

Multispecies deep learning using citizen science data produces more informative plant community models.

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

Collecting and managing in situ banana genetic resources information (Musa spp.) using online resources and citizen science.

The Musa Germplasm Information System (MGIS) stands as a pivotal database for managing global banana genetic resources information. In our latest effort, we have expanded MGIS to incorporate in situ observations. We thus incorporated more than 3000 in situ observations from 133 countries primarily sourced from iNaturalist, GBIF, Flickr, Pl@ntNet, Google Street view and expert curation of the literature. This addition provides a more comprehensive and detailed view of banana diversity and its distribution. Additional graphical interfaces, supported by new Drupal modules, were developed, allowing users to compare banana accessions and explore them based on various filters including taxonomy and geographic location. The integrated maps present a unified view, showcasing both in situ observations and the collecting locations of accessions held in germplasm collections. This enhancement not only broadens the scope of MGIS but also promotes a collaborative and open approach in documenting banana diversity, to allow more effective conservation and use of banana germplasm. Furthermore, this work documents a citizen-science approach that could be relevant for other communities. Database URL: https://www.crop-diversity.org/mgis/musa-in-situ.

Long-term variation in exposure to NO2 concentrations in the city of Naples, Italy: Results of a citizen science project.

The objective of this study is to evaluate long-term changes in the level of exposure to NO2 among the population living in the urban area of Naples (south Italy). This has been achieved by integrating data from the regional reference monitoring network with information collected during the citizen science initiative called 'NO2, NO grazie!' conducted in February 2020 and coordinated by the Non-Governmental Organisation 'Cittadini per l'aria'. This citizen science campaign was based on low-cost passive samplers (Palmes tubes), providing the ability to obtain unprecedented high-resolution NO2 levels. Using a Land Use Random Forest (LURF), we extrapolated the experimental data obtained from the citizen science campaign and evaluated the changes in population exposure from 2013 to 2022 and the uncertainty associated with this assessment was quantified. The results indicate that a large proportion of the inhabitants of Naples are still exposed to high NO2 concentrations, even if strict emission containment measures are enforced. The average levels remain higher than the new interim and air quality targets suggested by the World Health Organisation. The implementation of co-created citizen science projects, where NGO and citizens actively participate alongside scientists, can significantly improve the estimation and the interpretation of official reference data.

Citizen scientists: Unveiling motivations and characteristics influencing initial and sustained participation in an agricultural project.

Citizen science, where non-specialists collaborate with scientists, has surged in popularity. While it offers an innovative approach to research involvement, the domain of agri-environmental research participation, particularly in terms of citizen recruitment and retention, remains relatively unexplored. To investigate how what factors influence initial and sustained participation in an agronomic citizen science project, we performed a large survey during the case-study "Soy in 1000 Gardens". We obtained data on citizens motivations, general values, environmental concern, prior citizen science experience, and knowledge regarding sustainable food consumption and garden management and applied a two-step selection model to correct for potential self-selection bias on our participation outcomes. Initially, citizen scientists appear to be mostly motivated by gaining knowledge, having fun social interactions and environmental concern with regards to the effects on others, while the desire for enhancing or protecting their ego is less prominent. They also display higher knowledge and self-transcending values. Sustained participants however, are significantly older and share a stronger sense of moral obligation than their dropped-out counterparts. Moreover, prior experience seems to positively influence the length of their participation, while higher knowledge has a positive impact on the amount of data contributed. These insights offer strategies for tailored engagement that should emphasize collective impact, align with intrinsic values, and foster a sense of moral duty, with potential to enhance agri-environmental citizen science initiatives' effectiveness in addressing environmental challenges.

Application of citizen science with the nationwide bird census.

Citizen science (CS) is the most effective tool for overcoming the limitations of government and/or professional data collection. To compensate for quantitative limitations of the 'Winter Waterbird Census of Korea', we conducted a total of four bird monitoring via CS from 2021 to 2022. To use CS data alongside national data, we studied CS data quality and improvement utilizing (1) digit-based analysis using Benford's law and (2) comparative analysis with national data. In addition, we performed bird community analysis using CS-specific data, demonstrating the necessity of CS. Neither CS nor the national data adhered to Benford's law. Alpha diversity (number of species and Shannon index) was lower, and total beta diversity was higher for the CS data than national data. Regarding the observed bird community, the number of species per family was similar; however, the number of individuals per family/species differed. We also identified the necessity of CS by confirming the possibility of predicting bird communities using CS-specific data. CS was influenced by various factors, including the perceptions of the survey participants and their level of experience. Therefore, conducting CS after systematic training can facilitate the collection of higher-quality data.

Emerging technologies in citizen science and potential for insect monitoring.

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.