Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design

Citizen Science and Monitoring Forest Pests: a Beneficial Alliance?

Purpose of the Review

One of the major threats to tree health, and hence the resilience of forests and their provision of ecosystem services, is new and emerging pests. Therefore, forest health monitoring is of major importance to detect invasive, emerging and native pest outbreaks. This is usually done by foresters and forest health experts, but can also be complemented by citizen scientists. Here, we review the use of citizen science for detection and monitoring, as well as for hypothesis-driven research and evaluation of control measures as part of forest pest surveillance and research. We then examine its limitations and opportunities and make recommendations on the use of citizen science for forest pest monitoring.

Recent Findings

The main opportunities of citizen scientists for forest health are early warning, early detection of new pests, monitoring of impact of outbreaks and scientific research. Each domain has its own limitations, opportunities and recommendations to follow, as well as their own public engagement strategies. The development of new technologies provides many opportunities to involve citizen scientists in forest pest monitoring. To enhance the benefits of citizen scientists' inclusion in monitoring, it is important that they are involved in the cocreation of activities.

Summary

Future monitoring and research may benefit from tailor-made citizen science projects to facilitate successful monitoring by citizen scientists and expand their practice to countries where the forest health sector is less developed. In this sense, citizen scientists can help understand and detect outbreaks of new pests and avoid problems in the future.

The Role of Urban Environments in Promoting Active and Healthy Aging: A Systematic Scoping Review of Citizen Science Approaches

Promoting active and healthy aging in urban spaces requires environments with diverse, age-friendly characteristics. This scoping review investigated the associations between urban characteristics and active and healthy aging as identified by citizen science (CS) and other participatory approaches. Using a systematic scoping review procedure, 23 articles employing a CS or participatory approach (participant age range: 54-98 years) were reviewed. An inductive and deductive thematic analysis was completed to (a) identify local urban barriers and facilitators and (b) map them against the World Health Organization (WHO) Checklist of Essential Features of Age-Friendly Cities. A new Citizen Science Appraisal Tool (CSAT) was developed to evaluate the quality of CS and other participatory approaches included in the reviewed articles. A range of interconnected urban barriers and facilitators was generated by residents across the personal (e.g. perceived safety), environmental (e.g. unmaintained infrastructure), socio-cultural (e.g. cross-cultural activities), economic (e.g. affordable housing) and political (e.g. governmental support to migrant communities) domains. Mapping the barriers and facilitators to the WHO age-friendly checklist underscored the checklist's relevance and elucidated the need to explore barriers for migrant and cross-cultural communities and neighborhood development and alterations. The CSAT demonstrated strengths related to active engagement of residents and study outcomes leading to real-world implications. To advance the potential of CS to enrich our understanding of age-friendly environments, employing co-production to enhance relevance and sustainability of outcomes is an important strategy. Overall, employing CS highlighted the value of systematically capturing the experiences of older adults within studies aimed at promoting active and healthy aging.

Wombat Roadkill Was Not Reduced by a Virtual Fence. Comment on Stannard et al. Can Virtual Fences Reduce Wombat Road Mortalities? Ecol. Eng. 2021, 172, 106414

The roadkill of wildlife is a global problem. Much has been written about deterring wildlife from roads, but, as of yet, there is no empirical support for deterrents based on visual and/or auditory signals. A recent paper entitled 'Can virtual fences reduce wombat road mortalities?'reported the results of a roadkill mitigation trial. The authors installed a 'virtual fence' system produced by iPTE Traffic Solutions Ltd. (Graz, Austria) and evaluated its effectiveness for reducing roadkills of bare-nosed wombats (Vombatus ursinus) in southern Australia. The authors recorded roadkills in a simple Before-After-Control-Impact design but did not conduct any formal statistical analysis. They also measured three contextual variables (vegetation, wombat burrows, and vehicle velocity) but did not link these to the occurrence of roadkills in space and time. The authors concluded that the iPTE virtual fence system was 'minimally effective', yet 'appears promising'. Our analysis of their data, using standard inferential statistics, showed no effect of the virtual fence on roadkills whatsoever. We conclude that the iPTE system was not effective for mitigating the roadkills of bare-nosed wombats.

The Potential Role of School Citizen Science Programs in Infectious Disease Surveillance: A Critical Review

Public involvement in science has allowed researchers to collect large-scale and real-time data and also engage citizens, so researchers are adopting citizen science (CS) in many areas. One promising appeal is student participation in CS school programs. In this literature review, we aimed to investigate which school CS programs exist in the areas of (applied) life sciences and if any projects target infectious disease surveillance. This review’s objectives are to determine success factors in terms of data quality and student engagement. After a comprehensive search in biomedical and social databases, we found 23 projects. None of the projects found focused on infectious disease surveillance, and the majority centered around species biodiversity. While a few projects had issues with data quality, simplifying the protocol or allowing students to resubmit data made the data collected more usable. Overall, students at different educational levels and disciplines were able to collect usable data that was comparable to expert data and had positive learning experiences. In this review, we have identified limitations and gaps in reported CS school projects and provided recommendations for establishing future programs. This review shows the value of using CS in collaboration with traditional research techniques to advance future science and increasingly engage communities.

Citizen Science Data Collection for Integrated Wildlife Population Analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.

Research data management in health and biomedical citizen science: practices and prospects

Background

Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?

Objective

The aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.

Methods

A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.

Results

Health and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review.

Conclusions

There is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.

Working the crowd for forensic research: A review of contributor motivation and recruitment strategies used in crowdsourcing and crowdfunding for scientific research

Crowdsourcing and crowdfunding have been successfully used in a range of scientific disciplines and present opportunities for forensic researchers to draw on the power of large numbers of people to contribute to research projects through participation or by providing an alternative source of funding. This review aimed to examine whether contributors to crowd science and crowdfunding for scientific research are motivated to participate or provide financial support by the same factors, and to examine recruitment strategies in an attempt to identify a potential crowd for forensic researchers to approach. There was found to be limited research into crowdfunding for scientific research that addressed the motivations of contributors or recruitment strategies used, and no conclusions could be made. There is a need to overcome low response rates and high attrition over the lifetime of a crowd science project or crowdfunding appeal. It is necessary to target a large number of people who are interested in the subject studied and who want to make a difference in some way and contribute to science. True crime podcast audiences are proposed as they present large numbers of listeners who are interested in forensic science, criminal investigation or law enforcement. These audiences have been targeted for successful fundraising efforts and invitations to participate in crowd activities previously. They should be considered by forensic researchers who are looking to venture into crowdsourcing or crowdfunding for research projects.

Sustaining Citizen Science beyond an Emergency

This commentary explores lessons learned about aspects of citizen science sustainability, such as open data reuse after a project ends or after the urgency of a disaster. It is framed to be consistent with emerging research about how the 2020 pandemic relates to the sustainable development goals (SDGs). It argues for the importance of open data in citizen science, both in platform design and in citizen science outputs, to support sustainability beyond a funding cycle or emergency. This commentary discusses open datasets developed during the Ebola outbreak response in 2014 and the role of collaborative repositories in enabling uses beyond a single project. How citizen scientists can creatively contribute in ways aligned with humanitarian disaster response aims is explored.

Disparate Environmental Monitoring as a Barrier to the Availability and Accessibility of Open Access Data on the Tidal Thames

Open Access data plays an increasingly important role in discussions of environmental issues. Limited availability or poor quality data can impede citizen participation in environmental dialogue, leading to their voices being undermined. This study assesses the quality of Open Access environmental data and barriers to its accessibility in the Thames Estuary. Data quality is assessed by its ability to track long-term trends in temperature, salinity, turbidity, and dissolved oxygen. The inconsistencies found in the data required analyses and careful interpretation beyond what would be expected of a citizen. The lack of clear documentation and centralized database acted as a major barrier to usability. A set of recommendations are produced for estuarine monitoring, including defining minimum standards for metadata, creating a centralized database for better quality control and accessibility, and developing flexible monitoring protocols that can incorporate new hypotheses and partnerships. The goal of the recommendations is to create monitoring which can encourage better science and wider participation in the natural environment.

Optimizing future biodiversity sampling by citizen scientists

We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are ‘noisy’, with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both ‘hotspots’, which are frequently sampled locations, and ‘coldspots’, which are areas far from recent sampling, suggesting that an optimal sampling regime balances ‘hotspot’ sampling with a spread across the landscape.

Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science

The explosive growth in citizen science combined with a recalcitrance on the part of mainstream science to fully embrace this data collection technique demands a rigorous examination of the factors influencing data quality and project efficacy. Patterns of contributor effort and task performance have been well reviewed in online projects; however, studies of hands-on citizen science are lacking. We used a single hands-on, out-of-doors project-the Coastal Observation and Seabird Survey Team (COASST)-to quantitatively explore the relationships among participant effort, task performance, and social connectedness as a function of the demographic characteristics and interests of participants, placing these results in the context of a meta-analysis of 54 citizen science projects. Although online projects were typified by high (>90%) rates of one-off participation and low retention (<10%) past 1 y, regular COASST participants were highly likely to continue past their first survey (86%), with 54% active 1 y later. Project-wide, task performance was high (88% correct species identifications over the 31,450 carcasses and 163 species found). However, there were distinct demographic differences. Age, birding expertise, and previous citizen science experience had the greatest impact on participant persistence and performance, albeit occasionally in opposite directions. Gender and sociality were relatively inconsequential, although highly gregarious social types, i.e., "nexus people," were extremely influential at recruiting others. Our findings suggest that hands-on citizen science can produce high-quality data especially if participants persist, and that understanding the demographic data of participation could be used to maximize data quality and breadth of participation across the larger societal landscape.