Understanding the Drivers and Constraints to Participation in Citizen Science Programs in Uganda

Citizen science facilitates cost‐effective ecological data collection at much larger scales than would otherwise be feasible. This is particularly useful for the study of highly migratory species with broad distributions, such as billfishes. Participants in citizen science benefit from an increase in scientific literacy, a sense of satisfaction and enhanced understanding. However, there are common challenges involved in citizen science projects, including the recruitment and long‐term retention of participants. Applying knowledge about participant motivations and concerns is needed to overcome these barriers. We conducted an anonymous online survey of 153 game fishers from across Australia, who were largely recruited through game fishing clubs. The survey investigated their perspectives on participating in citizen science on billfish, including their motivations and concerns. Overall, those surveyed were highly motivated to participate in billfish citizen science programmes and reported few barriers to their engagement in research. Alongside wanting to contribute to billfish research and management, game fishers were motivated to participate to counteractive potential negative perceptions of the sport. However, approximately one third of respondents had not participated in research. Therefore, opportunities for further recruitment exist as potential participants almost certainly exceed current participants. Impediments to participation included a lack of communication about opportunities and outcomes of citizen science research. The survey highlighted a need to strengthen citizen science programmes to ensure participant retention and recruitment through targeted engagement and collaboration across organisations, which includes harnessing technology. Improved communication about the purpose and outcomes of research is key. We anticipate that our findings and recommendations are applicable to broader citizen science programmes, particularly those involving recreational fishers or a specialised pool of highly motivated participants. Great opportunity exists for researchers, fisheries managers and fishing organisations to work together to expand citizen science programmes that strategically improve our knowledge of the biology and stocks of billfish and other recreationally important fish species. Read the free Plain Language Summary for this article on the Journal blog.

The quality of data produced by citizen science (CS) programs has been called into question by academic scientists, governments, and corporations. Their doubts arise because they perceive CS groups as intruding on the rightful opportunities of standard science and industry organizations, because of a normal skepticism of novel approaches, and because of a lack of understanding of how CS produces data. I propose a three-pronged strategy to overcome these objections and improve trust in CS data. Develop methods for CS programs to advertise their efforts in data quality control and quality assurance (QCQA). As a first step the PPSR core could incorporate a field that would allow programs to point to webpages that document the QAQC practices of each program. It is my experience that many programs think carefully about data quality, but the CS community currently lacks an established protocol to share this information. Define and implement best practices for generating biodiversity data using different methods. Wiggins et al. 2011 published a list of approaches that can be used for QCQA in CS projects but how these approaches should be implemented has not been systematically investigated. Measure and report data quality. If one takes the point of view that citizen science is akin to a new category of scientific instruments, then the ideas of instrument measurement and calibration can be applied CS. Scientists are well aware that any instrument needs to be calibrated before its efficacy can be established. However, because CS is new approach, the specific procedures needed for different kinds of programs are just now being worked out for the first time. Develop methods for CS programs to advertise their efforts in data quality control and quality assurance (QCQA). As a first step the PPSR core could incorporate a field that would allow programs to point to webpages that document the QAQC practices of each program. It is my experience that many programs think carefully about data quality, but the CS community currently lacks an established protocol to share this information. Define and implement best practices for generating biodiversity data using different methods. Wiggins et al. 2011 published a list of approaches that can be used for QCQA in CS projects but how these approaches should be implemented has not been systematically investigated. Measure and report data quality. If one takes the point of view that citizen science is akin to a new category of scientific instruments, then the ideas of instrument measurement and calibration can be applied CS. Scientists are well aware that any instrument needs to be calibrated before its efficacy can be established. However, because CS is new approach, the specific procedures needed for different kinds of programs are just now being worked out for the first time. The strategy outlined above faces some specific challenges. Citizen science biodiversity programs must address two important problems that standard scientific entities encounter when sampling and monitoring biodiversity. The first is correctly identifying species. For citizens this can be a problem because they often do not have the training and background of scientist teams. Likewise, it may be difficult for CS projects to manage updating and maintaining the taxonomies of the species being investigated. A second set of challenges is the diverse kinds of biodiversity data collected by CS programs. For instances, Notes from Nature decodes that labels of museum specimens, Snapshot Serengeti identifies species of large mammals from camera trap photographs, iNaturalist collections images of species and then has a crowdsource identification processs, while eBird collects observations of birds that are immediately filtered with computer algorithms for review by the observer and if, subsequently flagged, reviewed by a local expert. Each of these programs likely requires a different set of best practices and methods to measure data quality.

Research on citizen science programmes has highlighted that they can foster science content and knowledge gain, enhance pro‐environmental behaviour and cultivate civic action among participants. Especially in the case of place‐based citizen science, which requires hands‐on repeated activity in an out‐of‐door setting through a scientific lens, evidence suggests that some of these outcomes may be linked to the unique people–place relationships and interactions afforded by such programmes.Even still, studies that empirically examine the influence of place on citizen science participant and programme outcomes are scant. This is due, in part, to the methodological challenges involved in interrogating complex aspects of a person's sense of place—aspects like place attachment—the emotional bonds between people and place.Here, an adapted three‐dimensional model of place attachment is proposed as a theoretical framework from which place‐based citizen science experiences and outcomes might be empirically examined in depth. The model, which posits personal, social and natural environment dimensions of place attachment is contextualized with research findings from the US‐based Coastal Observation and Seabird Survey Team (COASST) citizen science programme.Data from COASST suggest that participants do exhibit place attachment in all three dimensions of attachment, categorized within seven unique constructs, although questions remain regarding the unique intensity, make‐up (shape) and scale (spatial, social and nature‐science) of individual‐level attachment along the three central dimensions. Critically, more research is needed to investigate whether the unique place attachment ‘profile’ of participants is a function of personal, social or programmatic variables pre‐ and post‐programme participation.To encourage further scholarship on potential links between the experiences, exposures and programme components of place‐based citizen science and the place attachment profiles of participants, this paper includes a brief review of the research opportunities presented by the adapted three‐dimensional place attachment model discussed.Advancing this line of inquiry is an important component of broader efforts to understand how sense of place is altered via place‐based citizen science and whether or not that is linked to specific programme outputs or participant outcomes in science knowledge, ecological understanding and civic engagement.A freePlain Language Summarycan be found within the Supporting Information of this article.

Citizen science (CS) programs often question what motivates their volunteers and how volunteer participation can be sustained. Using a case study of citizen scientist volunteers (CSVs) who monitor water quality in Texas, I apply here a novel approach—the Dispositional-Organizational Interactions Framework (DOIF)—that provides a nuanced understanding of CSVs. The DOIF allows for consideration of how dispositional variables, such as sociodemographic characteristics and motivations for participation, may relate to organizational variables (e.g., program efficacy, results, and recognition); both overarching variables relate to indicators of commitment. The purpose of this study is to examine interactions among different aspects of a CS program and CSVs—observations that can improve CSV satisfaction and possibly retention. In a community geography partnership, volunteers of a statewide CS program were surveyed (n = 327). Results of exploratory factor analyses and a series of nonparametric tests indicate the DOIF offers insights into five major motivational factors; it uncovers between-group differences in how CSVs value organizational variables and indicate a commitment to volunteerism. This study contributes to the broader literature by incorporating the role of the organization in assessments of motivations through the creation of a novel framework and through empirical findings. The paper considers implications of results for CS programs and practice, then concludes with suggestions for future research.

Minimizing Data Waste: Conservation in the Big Data Era

Simple SummaryCitizen science offers an excellent opportunity to engage the public in scientific data collection, educational opportunities, and applied management. However, the practicalities of developing a citizen science program, from generating ideas to developing tools, implementing programming, and evaluating outcomes, are complex and challenging. To address challenges and provide a foundation for practitioners, scientists, and the public, the Government of Alberta developed a set of citizen science principles. Here, we use these principles as an evaluative framework to assess the outcomes of the GrizzTracker program, which was developed to help inform provincial species-at-risk recovery efforts. While the program experienced some successes, we identified challenges, including skepticism from the scientific community about the utility of citizen science and a lack of program leadership, staff capacity, and funding needs for long-term implementation. Reflecting on the principles, we provide policy recommendations that future citizen science programs can consider.Citizen science offers an excellent opportunity to engage the public in scientific data collection, educational opportunities, and applied management. However, the practicalities of developing and implementing citizen science programming are often more complex than considered. Some challenges to effective citizen science include scientists’ skepticism about the ability of public participants to rigorously collect quality data; a lack of clarity on or confidence in the utility of data; scientists’ hesitancy in engaging the public in projects; limited financial commitments; and challenges associated with the temporal and geographic scales of projects. To address these challenges, and provide a foundation upon which practitioners, scientists, and the public can credibly engage in citizen science, the Government of Alberta developed a set of citizen science principles. These principles offer a framework for planning, designing, implementing, and evaluating citizen science projects that extend beyond Alberta. Here, we present a case study using these principles to evaluate GrizzTracker, a citizen science program developed to help inform provincial species-at-risk recovery efforts. While we found that GrizzTracker applied each of the six principles in some way, including successful public engagement, strengthened relationships, and raising public awareness about northwest Alberta’s grizzly bears, we also identified a number of challenges. These included ongoing skepticism from the traditional scientific community about the utility of citizen science and governance challenges related to program leadership, staff capacity, and funding. By using the principles as a guideline, we provide policy recommendations for future citizen science efforts, including considerations for program design, implementation, and evaluation.

14 INVITED Early phase drug development in the Children's Oncology Group

The art and science of multi-scale citizen science support

Data quality (DQ) is a major concern in citizen science (CS) programs and is often raised as an issue among critics of the CS approach. We examined CS programs and reviewed the kinds of data they produce to inform CS communities of strategies of DQ control. From our review of the literature and our experiences with CS, we identified seven primary types of data contributions. Citizens can carry instrument packages, invent or modify algorithms, sort and classify physical objects, sort and classify digital objects, collect physical objects, collect digital objects, and report observations. We found that data types were not constrained by subject domains, a CS program may use multiple types, and DQ requirements and evaluation strategies vary according to the data types. These types are useful for identifying structural similarities among programs across subject domains. We conclude that blanket criticism of the CS data quality is no longer appropriate. In addition to the details of specific programs and variability among individuals, discussions can fruitfully focus on the data types in a program and the specific methods being used for DQ control as dictated or appropriate for the type. Programs can reduce doubts about their DQ by becoming more explicit in communicating their data management practices.

Building a More Scientifically Informed Community in the Delaware River Basin David W. Bressler, John K. Jackson, Matthew J. Ehrhart, and David B. Arscott Citizen Science (CS) programs inherently broaden societal science literacy by providing experiential scientific learning opportunities to a diverse cross-section of the public. Here we describe an expanding CS program that supports more than 50 nonprofit organizations in the Delaware River Basin (DRB). The motivation for this effort has been generated by investment from the William Penn Foundation to create the Delaware River Watershed Initiative (DRWI), a multi-year effort to support organizations working to protect and restore stream health in the DRB. In direct support of this initiative, the Stroud Water Research Center is facilitating CS efforts to improve the capacity of watershed groups to conduct scientific investigations associated with DRWI watershed protection and restoration projects, as well as to build general knowledge on the ecology of their watersheds and the broader DRB. This project benefits from cooperative efforts among a wide variety of citizen scientists, as well as professional scientists and environmental planners. Participants in these CS activities have diverse backgrounds ranging from volunteers with minimal or no formal training in science to retired Ph.D.-level scientists. There are full-time and part-time environmental professionals who volunteer in their spare time, college and high school students, teachers and professors, and many other individuals from a wide variety of science and non-science backgrounds. Some volunteers work multiple days per week carrying out or assisting the goals of the DRWI, while others put in a few hours per month—all helping to build valuable datasets on water quality and related outcomes of restoration and land protection. Through their engagement, these citizen scientists gain personal knowledge and experience that can inform the greater community and influence local environmental policy. Citizen Science depends on the experience and expertise of the individuals involved. In our case, professional scientists, environmental planners, and even environmental regulators help to frame monitoring approaches and guide groups and individuals on collecting samples, doing field measurements, analyzing data, and researching policy. Our vision of success is a collaborative environment that supports watershed groups and their citizen scientists in asking and answering their own ecological questions about local streams and rivers, and in translating this knowledge and experience into regional policies and practices that result in healthier streams and, subsequently, cleaner drinking water for future generations. Volunteers contributing to this initiative are not exclusively collecting data to feed into a single large study; nonetheless, combined across tributaries, this effort is also building an increasingly comprehensive and publicly accessible dataset for the whole DRB. Citizen Science enables certain things that conventional science does not. We are supporting CS programs to not only generate robust data sets but also to build a scientifically informed community in the DRB. Citizen Science is no different than ordinary science in that it follows the same [End Page 24] processes of developing and testing hypotheses (i.e., asking questions, making predictions, and coming up with ways to answer the questions), Quality Assurance (QA) and Quality Control (QC) (i.e., making plans to ensure data accuracy [QA] and then confirming data accuracy [QC] via specific data replication protocols), and summarizing and communicating results (i.e., preparing data summaries, reports, etc.). Citizen Science is different from ordinary science, however, in that it involves a far greater diversity of individuals with wide-ranging backgrounds and skills. From certain professional science perspectives, this variation among individuals may be considered a hindrance to the science. However, with improvements in technology and with people more often changing careers and increasing volunteer involvement during these transitions, in spare time, and in retirement, there continue to be more opportunities to build large viable datasets with new and unconventional CS methods. Perhaps most importantly, as societal and cultural pursuits are increasingly directed toward improving the environmental awareness and science-knowledge of the general population, CS not only presents opportunities to build useful datasets but also to make strides in building a scientifically informed community, which is rarely a goal in conventional science endeavors. Ideally, this building of science literacy then leads to communities making better environmental decisions...

Citizen science programs offer the public opportunities to be involved in hands-on scientific data collection and study. Despite a history of incorporating citizen science in the K-12 classroom, little research has focused on projects that are appealing to both K-12 instructors and higher education faculty. Our study investigates initial barriers and motivations for participation in citizen science at multiple education levels using a winter, stream ecology citizen science project. Our findings indicate a growing interest in citizen science programs in winter, as they provide opportunities for collaboration, encourage flexible data collection, and offer tangible results for participants to reflect on their local ecological habitats.

Citizen science programs provide opportunities for students to help professional scientists while fostering science achievement and motivation. Instruments which measure the effects of this type of programs on student motivational beliefs are limited. The purpose of this study was to describe the process of examining the reliability and validity of The Citizen Science Self-Efficacy Scale (CSSES) designed to measure the effectiveness of citizen science programs on student self-efficacy for scientific observation skills. Fifteen (n =15) field experts and 248 (n = 248) eighth grade students participated in three studies. The results suggest that the psychometric properties of this scale are sufficient. Implications for the development and utility of self-efficacy scales in a variety of citizen science contexts are discussed. The aim of the present study is twofold: (a) to establish the psychometric properties of a scale developed to measure student self-efficacy beliefs for scientific observations in citizen science programs and (b) to describe the process in the validation of a self-efficacy scale to support researchers who want to create their own scales for similar citizen science programs. Three studies were conducted to develop the Citizen Science Scale (CSSES) and evaluate its psychometric properties. The purpose of the CSSES was to develop a measure suitable for analysis within a social cognitive career framework and informal natural science contexts. The findings in the present study found that the measure had an acceptable unitary factorial structure and high internal reliability of .89 for the CSSES. The purpose of the Citizen Science Self-Efficacy Scale (CSSES) is to assess individual’s beliefs about their capabilities for scientific observational skills. This scale is applicable to measuring individual’s self-efficacy in outdoor learning contexts (e.g., horseshoe crab citizen science context). Given that self-efficacy is a strong predictor of academic achievement and motivation, self-efficacy scales like the CSSES may provide a way for stakeholders involved in outdoor education to measure student gains and to substantiate program effectiveness. From a methods standpoint, the contribution of this work is to serve as a guide of how to develop a self-efficacy scale.

How do marine and coastal citizen science experiences foster environmental engagement?

Purpose The purpose of this paper is to provide a conceptual framework for using citizen science – defined as a data collection method through which non-professionals engage in contributing to authentic scientific inquiry – within the expedition cruise industry to contribute significantly to the collection of environmental data from hard-to-access Arctic areas. Design/methodology/approach The authors review trends in Arctic expedition cruise tourism and current needs in Arctic research and monitoring, and clarify where the expedition cruise tourism industry could have the most impact by providing data to the scientific community. The authors also compare the regulatory context in the Antarctic to that in the Arctic and discuss how these differences could affect the widespread use of citizen science. At last, the authors describe some general principles for designing citizen science programs to be successful on board, and highlight several existing programs that are being recognized for their contributions to a greater scientific understanding of the Arctic. Findings The authors find that citizen science data from the expedition cruise industry are underutilized as a tool for monitoring Arctic change. Numerous examples illustrate how citizen science programs on-board expedition ships can successfully collect robust scientific data and contribute to enhancing the knowledge and stewardship capacity of cruise passengers. Inclusion of citizen science data from the expedition cruise industry should be considered a critical part of international Arctic observing networks and systems. Social implications Active participation in Arctic citizen science by tourists on expedition cruise ships has many potential benefits beyond the collection of high quality data, from increasing passengers’ knowledge and understanding of the Arctic while on board, to affecting their attitudes and behaviors after they return home. Originality/value The potential for tourism to contribute to Arctic observing systems has been discussed previously in the scientific literature; the authors narrow the focus to citizen science programs in the expedition cruise industry, and provide concrete examples, in the hope that this will streamline acceptance and implementation of these ideas by researchers and tourism practitioners.

Most citizen science research inherently separates the observer (citizen science participant) from the observation (e.g. data point), placing artificial boundaries around what matters and how it comes to matter. We apply three elements of the philosophical framework of agential realism to reveal a more complex picture of how data arise within citizen science programmes, and its meaning to both the practice of science and the citizen science participant: ‘intra‐action’ (all entities have agency and are entangled with one another); ‘material becoming’ (what comes to matter); and ‘responsibility’ (accountability for what comes to matter and what is excluded from mattering). We draw on a case study of FrogID—an Australia‐wide citizen science program focused on calling frogs, with over 42,000 participants and over 1 million frog records. We conducted semi‐structured interviews with 30 FrogID users, completing two rounds of thematic and relational coding. Our findings reveal that as a consequence of their recording behaviours, FrogID participants become increasingly entangled with the nocturnal environment, with sound and with their own self. Expanding and reciprocal relationships and experiences shape the nature and frequency of their recordings. Second, meaning influences what comes to matter (i.e. what is recorded and submitted) for FrogID participants. We reveal meaning related to feedback (recognition and thus reciprocity), others (social networks and participation with family and friends) and the self (physical and mental well‐being and identity formation/becoming). These different forms of meaning influenced engagement with app use. Third, participants communicated responsibilities related to their involvement in citizen science, including responsibilities to create knowledge (e.g. longitudinal data collection), to conserve (e.g. actively conserving frog, formally committing areas to conservation) and to educate self and others (e.g. skills and competencies required for environmental action). Synthesis and applications: By recognizing a more comprehensive set of intra‐actions, beyond the observer and the observation, agential realism can reveal when, why and how citizen science observations are made; what observations come to matter and why; and how people can create a more just world. Agential realism can shape how citizen science participation, retention and biodiversity data generation are founded. We propose three opportunities for citizen science programs based on these findings. Read the free Plain Language Summary for this article on the Journal blog.