Community‐derived solutions to increase citizen science participation

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.

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.

Minimizing Data Waste: Conservation in the Big Data Era

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

The art and science of multi-scale citizen science support

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.

The primary purpose of this study was to examine the influence of volunteer motivation, participation, and citizen science project type on the retention and scientific literacy of 4-H youth volunteers ages 8-19 years participating in science projects. The conceptual model of participation in organized activities (OA) proposed by Bohnert, Fredericks and Randall (2010) was used as a framework for the variables included in the study categorizing them as predictors of participation, participation, program characteristics, or outcomes. A systematic review of volunteer motivations, retention, and scientific literacy in citizen science projects exposed that the literature contains silos of information published in content area specific journals further supporting the need for the Journal of Citizen Science Practice and Theory established in 2016. The review revealed a gap in the literature on motivations of youth citizen science, the factors that influence volunteer retention in citizen science projects, and how to define and measure scientific literacy. This study found two significant differences between 4-H youth participants in 4-H science programs and those in 4-H citizen science program. First, youth in science programs without a citizen science component were more motivated by social functions to volunteer and second, they are more likely to continue to volunteer than their counterparts. Further investigation into the influence of citizen science program characteristics on these variables is needed. This study revealed relationships between engagement and consistency, in addition to consistency and both retention and scientific literacy outcomes. These relationships need to be examined for causation. A new framework for studying youth participation and youth outcomes in citizen science programs is proposed.

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...

<p><strong>Monitoring Svalbard’s environment and cultural heritage through citizen science by expedition cruises</strong></p><p>Michael K. Poulsen1, Lisbeth Iversen2, Ted Cheeseman3, Børge Damsgård4, Verena Meraldi5, Naja Elisabeth Mikkelsen6, Zdenka Sokolíčková7, Kai Sørensen8, Agnieszka Tatarek9, Penelope Wagner10, Stein Sandven2, and Finn Danielsen1</p><p>1NORDECO, 2NERSC, 3PCSC, 4UNIS, 5Hurtigruten, 6GEUS, 7University of Oslo, 8NIVA, 9IOPAN, 10MET Norway</p><p><strong>Why expedition cruise monitoring is important for Svalbard. </strong>The Arctic environment  is changing fast, largely due to increasing temperatures and human activities. The continuous areas of wilderness and the cultural heritage sites in Svalbard need to be managed based on a solid understanding.</p><p>The natural environment of Svalbard is rich compared to other polar regions. Historical remains are plentiful. The Svalbard Environmental Protection Act aims at regulating hunting, fishing, industrial activities, mining, commerce and tourism. Expedition cruises regularly reach otherwise rarely visited places.</p><p><strong>Steps taken to improve environmental monitoring. </strong>A workshop for enhancing the environmental monitoring efforts of expedition cruise ships was held in Longyearbyen in 2019, facilitated by the INTAROS project and the Association of Arctic Expedition Cruise Operators  (https://intaros.nersc.no/content/cruise-expedition-monitoring-workshop) with representatives of cruise operators, citizen science programs, local government and scientists. They agreed on a pilot assessment of monitoring programs during 2019.</p><p><strong>Results show the importance of cruise ship observations. </strong>The provisional findings of the pilot assessment suggest thatexpedition cruises go almost everywhere around Svalbard and gather significant and relevant data on the environment, contributing for example to an improved understanding of thestatus and distribution of wildlife. Observations are often documented with photographs. More than 150 persons contributed observations during 2019 to eBird and Happywhale. iNaturalist, not part of the pilot assessment, also received many contributions. The pilot assessment was unable to establish a useful citizen science program for testing monitoring of cultural remains.</p><p><strong>Conclusions relevant for monitoring and environmental management. </strong>Cruise ships collect environmental data that are valuable for the scientific community and for public decision-makers. The Governor of Svalbard isresponsible for environmental management in Svalbard. Data on the environment and on cultural remains from expedition cruises can be useful for the Governor’s office. Improved communication between citizen science programs and those responsible for environmental management decisions is likely to increase the quantity of relevant information that reaches public decision makers.</p><p><strong>Recommendations for improving the use of cruise ship observations and monitoring.</strong></p><ul><li>1) All cruise expedition ships should be equipped with tablets containing the apps for the same small selection of citizen scienceprograms so that they can easily upload records.</li> <li>2) Evaluation of data that can be created and how such data can contribute to monitoring programs, to ensure that data is made readily available in a form that is useful for institutions responsible for planning and improving environmental management.</li> <li>3) Clear lines of communication between citizen science program participants, citizen science program organizers, the scientific community and decision makers should be further developed.</li> <li>4) Developing expedition cruise monitoring is of high priority in Svalbard, but is also highly relevant to other polar regions.</li> <li>5) Further work is necessary to fully understand the feasibility and potential of coordinated expedition cruise operator based environmental observing in the Arctic.</li> </ul>

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.

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.

We investigated youth participation in three Community and Citizen Science (CCS) programs led by natural history museums in out-of-school settings. Using second generation Activity Theory, we looked at repeated participation over time, collecting and then qualitatively analyzing ethnographic fieldnote observations on focal youth participation and components of the activity systems. We found each program provided multiple and unique access points for youth to participate in environmental science. Further, when facilitators emphasized the scientific goals of the programs clearly and repeatedly, youth participation in the scientific processes of the CCS programs deepened. Access to scientific tools, facilitation in using them, and repeatedly applying them in authentic research, enabled youth to participate in different aspects of CCS, from exploring to submitting biological data. Repeated participation in CCS activities provided the opportunities for youth to try the same type of participation multiple times (intensification), as well as provided the opportunity for youth to try different types of participation (diversification). Our findings suggest that repeated participation in authentic scientific research in CCS contexts fosters youth development of new roles and possible development of environmental science identities.

Citizen science has extensively been reported as a useful tool for scientists gathering large quantities of information (Silvertown et al. 2011; Dickinson et al. 2010; Bonney et al. 2009). Citizen Science has been broadly defined in previous literature. We employ the following for purposes of this paper: partnerships between scientists and the public where data are collected, shared, and analyzed (Jordan et al. 2012). These partnerships result in projects that range in participation capacity, participation design, and project goals. On one hand, there are participatory action research projects (Cooper et al. 2007), initially defined by Caren Cooper as projects initiated by non-scientist members of the public and developed hand-in-hand with scientists or scientists hired by the project as consultants (see also Shirk et al. 2012). On the other hand there are crowdsourced or contributory citizen science projects (Bonney et al. 2009), which allow researchers to gather large amounts of data without any input or participation of the public on project development. These large contributory projects such as eBird (www.eBird.org) and Evolution MegaLab have had success in collecting large-scale data sets from participants for long-term trend analysis in the U.S. and Europe respectively. Crowdsourcing citizen science projects such as FoldIt (www.fold.it) and Galaxy Zoo (www.galaxyzoo.org), have capitalized on large numbers of participants to do analysis that computers are not adept at. Co-created action type citizen science projects represent the middle ground, where partnerships between local citizens and scientists have been successful in preservation and restoration efforts of local waterways in New Jersey and Pennsylvania (e.g., ReClam the Bay (www.reclamthebay.org) and Shermans Creek Conservation (www.shermanscreek.org)). Regardless of the project type, all citizen science programs have participants engaged in authentic scientific research and resulted in successful data collection for scientists. While there is no official count of all the citizen science projects active at any given time, anecdotally there are a large number citizen science projects in ecology. Recent findings on patterns and use of citizen science in research found that the bulk of published citizen science based research was ecological/biological in nature (Follett and Strezov 2015). What has been little studied is the role and impact of the professional scientist who is engaging the citizen scientists. Two areas are of particular interest in this research: (1) who and what disciplines use citizen science and (2) the perceived trust and reliability of citizen science gathered data in academia. Research about the ecologists, and scientists writ-large, engaging participants in their research is limited. It is important to know more about the role and use of citizen science in academia because of its increased use in certain fields not just as a research tool but a method of engagement, and engendering broader support for science within the public sphere. If there is indeed a skewed representation of particular disciplines or individuals, there may be implications for broader citizen science outcomes. Additionally, variation in trust and use, or willingness to use, citizen science or citizen science collected data because of academic practice or identity frames may hinder further gains made in the field of citizen science as * Amanda E. Sorensen amasoren@rutgers.edu

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.