# Open Social Scholarship Annotated Bibliography/Citizen Science
## Category Overview

Citizen Science refers to research that is partially or wholly conducted by nonscientists, typically by volunteers who receive the training necessary to collect and interpret data for a target research investigation. In recent years, citizen science projects have become much more prominent, especially in the social sciences. Authors argue that this is due to the advancement of technology that allows the collection and transfer of data by nonprofessionals, as well as the recent demand of funding agencies to seek the public’s approval of scientific research endeavors, since taxpayer dollars often fund these initiatives. The Cornell Lab of Ornithology (CLO) is one of the central organizations that has been practicing citizen science for over twenty years; in this category, CLO researchers provide a model for setting up a successful citizen science project (Bonney et al. 2009). Some of the central issues brought to light in this category revolve around the question of data reliability, which often depends on the clarity of instructions, the type of training, and the level of motivation of participants. Prestopnik and Crowston propose one way of increasing this motivation through a gaming model that includes a crowdsourcing element. The authors propose that gaming may encourage a more engaged practice and accurate data (2011). Research also addresses the educational role of citizen science for individual participants and the necessary steps that project leaders must take to ensure reaching these goals, as well as the benefits of integrating citizen science in undergraduate curricula (Jordan, Balard, and Phillips 2012; Oberhauser and LeBuhn 2012). Authors especially encourage making data, results, and their interpretation available for the public in open access (Bonney et al. 2009; Gallo and Waitt 2011). The authors in this category unanimously agree that if done properly, citizen science can go a long way in educating the public, supporting scientific research, and improving the environment more generally.

## Annotations

Bonney, Rick, Caren B. Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V. Rosenberg, and Jennifer Shirk. 2009. “Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy.” BioScience 59 (11): 977–84. https://doi:.org/10.1111/j.0735-2751.2005.00242.x.
    Bonney et al. provide a model for citizen science based on the past two decades of the Cornell Lab of Ornithology (CLO) citizen science projects, an organization that is deeply engaged with environmental studies projects and has had thousands of participants gather tens of millions of observations each year. The authors assert that citizen science is especially useful in projects that require the gathering of vast amounts of data over the span of many years. They outline the various steps of their model, which involve choosing a scientific question and forming a science-based interdisciplinary team comprising scientists, educators, evaluators, and technologists to lead the project and develop the necessary standards and materials to carry it out. This is followed by recruiting and training citizen participants in the required skills for gathering the appropriate data. This data is immediately displayed to the public in open access, after which it is analyzed and interpreted. These results are then disseminated and the outcomes are measured. The authors emphasize the necessity to innovate current data management, scientific analysis, and educational research practices in order to accommodate the growing scope and level of citizen science.
Gallo, Travis, and Damon Waitt. 2011. “Creating a Successful Citizen Science Model to Detect and Report Invasive Species.” BioScience 61 (6): 459–65. https://doi:.org/10.1525/bio.2011.61.6.8.
    Gallo and Waitt describe a citizen science program—the Invaders of Texas—that relies on local volunteers to gather data on invasive species in certain parts of Texas. This data is uploaded into a public database and serves as a point of reference for policymakers, scientists, and resource managers to make various decisions about weed distribution and to be aware of the scope of invasive species at a given time. The volunteers in the citizen science program receive proper training in order to provide detailed information about the target weed, such as its physical attributes, the GPS coordinates of where it was spotted, time of observation, level of damage to the crop, and other information. This information is on the Invaders of Texas database, which is an open source web application with an embedded Google Maps interface that supports exportation in a variety of formats. The authors conclude that making such projects a more common and collaborative endeavor could benefit the ecosystem as a whole.
Jordan, Rebecca C., Heidi L. Ballard, and Tina B. Phillips. 2012. “Key Issues and New Approaches for Evaluating Citizen-Science Learning Outcomes.” Frontiers in Ecology and the Environment 10 (6): 307–9. https://doi:.org/10.1890/110280.
    Jordan, Ballard, and Phillips call attention to the educational role of citizen science projects and focus on its importance in developing ecological literacy on the individual, communal, and program level. The authors are concerned with whether citizen science projects carry out the educational goals they set forth. They argue that team members ought to develop an evaluation plan to trace whether project activities allow these learning goals to be achieved, whether the goals are clearly defined, and what the concrete measures of success for both of these points are. The balance between carrying out tasks and achieving learning goals should primarily be calculated according to the length of the volunteer’s participation and the difficulty level of the tasks they carry out. The authors suggest that a more comprehensive approach—one that would take into consideration the wider scope of impact ranging from the individual to the community level—should be implemented. They point out that various types of citizen science projects have resulted in positive outcomes for the community, including “increased social capital, community capacity, and trust between scientists, managers, and the public” (309).
Mayer, Amy. 2010. “Phenology and Citizen Science.” BioScience 60 (3): 172–75. https://doi:.org/10.1525/bio.2010.60.3.3.
    Mayer argues that phenology—the relationship between annual events and seasonal changes, such as observing the bloom of flowers—lends itself to citizen science when many people record these observations. Mayer addresses the data quality issue and states that evidence from various studies show that clear and straightforward instructions result in reliable data from volunteers. She discusses various recent and long-term phenology projects, such as the National Ecological Observatory Network (NEON) and Feedwatcher, with a specific focus on how both projects address the challenge of sustaining ongoing citizen observations. This is an issue since in phenology, it is long-term observations over years that add real value to a project. Another issue is that such long-term research is not compatible with traditional funding agencies, since they often give out shorter term grants than such research requires.
Newman, Greg, Andrea Wiggins, Alycia Crall, Eric Graham, Sarah Newman, and Kevin Crowston. 2012. “The Future of Citizen Science: Emerging Technologies and Shifting Paradigms.” Frontiers in Ecology and the Environment 10 (6): 298–304.
    Newman et al. speculate about the future of citizen science in conjunction with rapidly evolving technologies. They propose suggestions for project managers to integrate technology in a way that would help their research appeal to a wider audience. The authors describe how the various steps of citizen science projects, including “gathering teams/resources/partners, defining research questions, collecting and managing data, analyzing and interpreting data, disseminating results, and evaluating program success and program outcomes” (299) may change in the future. They foresee a future in which technology could allow volunteers to have more agency and responsibility in science projects, and argue that this could eventually balance out the hierarchy between scientists and volunteers into more of a partnership. Newman et al. focus on wireless sensor networks that may help link the laboratory to the environment and help volunteers collect, analyze, and interpret data. They recommend that project managers encourage the use of open data and open source software, and utilize technology in a way that would increase motivation, retention, and ethnic diversity.
Oberhauser, Karen, and Gretchen LeBuhn. 2012. “Insects and Plants: Engaging Undergraduates in Authentic Research through Citizen Science.” Frontiers in Ecology and the Environment 10 (6): 318–20. https://doi:.org/10.1890/110274.
    Oberhauser and LeBuhn point to the various benefits of including undergraduate students in citizen science and advocate for increased citizen science hands-on training during undergraduate years. They argue that the type of learning that citizen science invokes is valuable since it is an inquiry-based practice that encourages students to pose questions, gather and interpret data, and draw conclusions. The authors focus on two citizen science projects, including the Monarch Larva Monitoring Project (MLMP) and the Great Sunflower Project (GSP) in which the students participate in data collections, data analysis, and in creating independent or group research projects. The authors are the project managers of these initiatives and provide a number of examples of how students behave and learn in such environments. Students working on these projects range from volunteers to paid assistants, and are engaged in the midst of the scientific process rather than merely performing tedious tasks. OberHauser and LeBuhn believe that the three areas of undergraduate studies that would most benefit from citizen science are data collection, class projects, and research opportunities.
Phillips, Tina, Janis Dickinson, Caren B. Cooper, and Rick Bonney. 2007. “Citizen Science as a Tool for Conservation in Residential Ecosystems.” Ecology and Society 12 (2): 11. http://www.ecologyandsociety.org/vol12/iss2/art11/.
    Phillips et al. address the role that citizen science plays in implementing conservation strategies in residential lands for positive impacts on biodiversity. The authors argue that the value suburban and urban residential lands can contribute to our understanding of the ecosystems is only beginning to be acknowledged. They propose a framework for using citizen science in order to gather data that may help gain insight into conservation studies in this newly emerging field. The volunteers gather data, often over long periods of time, based on their training and the research questions set by the science-led team interested in observing certain occurrences, such as watershed-based monitoring. They base their framework on the citizen science model developed at the Cornell Lab of Ornithology that is outlined in this section of the bibliography (see Bonney et al.) and adapt it to their own research question. Phillips et al. conclude that using citizen science in conservation research in residential areas can help not only in tackling scientific questions, but also in implementing and monitoring various management strategies at a large scale, which can eventually result in long-term improvements in the environment.
Prestopnik, Nathan, and Kevin Crowston. 2011. "Gaming for (Citizen) Science: Exploring Motivation and Data Quality in the Context of Crowdsourced Science through the Design and Evaluation of a Social-Computational System." e-Science Workshops (eScienceW), 2011 IEEE Seventh International Conference, 28-33. http://doi.org/10.1109/eScienceW.2011.14.
    Prestopnik and Crowston discuss the role of gaming in improving the present citizen science model, especially in terms of increasing motivation and data quality. This is done by presenting Citizen Sort, a social-computational system that functions as a game for crowdsourced science. According to the authors, the major challenges of the study include measuring abstract concepts such as the level of motivation of the users, whereas more tangible attributes of measurement are related to the quality and completeness of the dataset. The main task of Citizen Sort, when launched, will be to identify whether introducing a gaming element into citizen scholarship will result in a more engaged practice (instead of the somewhat tedious process of gathering data), or if gaming is distracting or uninteresting in this context. The game itself will be made in a system assemblage approach, meaning that it will incorporate different features and technologies in order to appeal to users. Users will be asked to either upload or identify photographed species according to preset parameters that vary from game to game. Their interaction with the game will be recorded in order to determine the games that are the most fun, motivational, and that result in the highest data quality; this information will be used for improving their gaming techniques.
Purdam, Kingsley. 2014. “Citizen Social Science and Citizen Data? Methodological and Ethical Challenges for Social Research.” Current Sociology 62 (3): 374–92. https://doi:.org/10.1177/0011392114527997.
    Purdam explores the practical implications of citizen social science in a real world setting. The volunteers are trained by social scientists to systematically collect observation data throughout their routine daily lives rather than go out of their way to target the specific focus of the study. The research focus is Central London beggars, specifically because London is a densely populated city with a high rate of beggars and limited research in this field. The main concerns raised are in relation to the methodology, quality of the data collected, ethical implications that observation of others raises, presentation of the data, and potential value of a citizen engaged model for social science research. The various findings of this research are of interest to the social sciences and other groups, including international charities, local authorities, and policymakers. The ethical issue of surveillance is strongly acknowledged, especially in terms of what this type of research could mean if it were formalized and the scope was increased. However, the authors argue that the form of surveillance in this study adheres to ethical standards since the observers were not spies and lacked political interest in their subjects, and since the study is based on an ethically approved research design and followed a strict set of instructions. One limitation that is emphasized is the fact that the targets were only observed for a short time, which fails to provide the researchers with facts that may help improve their situation. Other limitations were found in the sample size and strategies that were adopted, and the potential for generalizations to occur. The authors believe that such citizen social sciences, backed up with new theoretical frameworks, could help with research projects, and help monitor and inequality and oppression in a coordinated way.
**Rotman, Dana, Jenny Preece, Jen Hammock, Kezee Procita, Derek Hansen, Cynthia Parr, Darcy Lewis, and David Jacobs. 2012. “Dynamic Changes in Motivation in Collaborative Citizen-Science Projects.” In CSCW ’12 Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, 217–226. New York: ACM. https://doi:.org/10.1145/2145204.2145238.
    Rotman et al. conduct a study that borrows from a motivational model in order to determine the various incentives of volunteers to participate and perform well in citizen science projects related to ecological scientific research. Although many successful citizen science projects exist, they are yet to fully take advantage of the collaborative possibilities between the scientists and volunteers; hence, studying the motivation of each party and designing an environment that rewards and motivates all parties could drastically improve the field altogether. After conducting the study, the authors found that volunteers are primarily motivated by their curiosity, drive for learning, and desire for conservation, whereas the scientists were primarily motivated by their career and scientific advancement more generally. They also found that the two most important motivational moments for volunteers are the first encounter with the project and group, and the wrapping up of a project when volunteers decide whether or not to participate in other projects. The authors also contribute a dynamic model that displays the engagement cycle of the participants throughout the different stages of the project.
Silvertown, Jonathan. 2009. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24 (9): 467–71. https://doi:.org/10.1016/j.tree.2009.03.017.
    Silvertown calls attention to the burgeoning citizen science projects, especially in environmental sciences, and addresses the main underlying reasons for such an exponential growth. The first is the availability of tools that facilitate the gathering and dissemination of information to and from the public by the volunteers themselves. The second is the fact that citizen science is carried out by volunteers that bring a diverse set of skills, thereby significantly cutting down on project costs. Finally, he states that present funding agencies require scientific research to incorporate an element of project-related outreach, and a means to ensure that the public values taxpayer-funded work; having members of the public directly participate in scientific research allows them to reach this goal. Despite its established roots, dating from the nineteenth century, the author points out that citizen science is underrepresented in formal scientific literature because the term itself is fairly recent and the practice has yet to fit within the standard methods of scientific research that is based on hypothesis-testing. He concludes by pointing to guidelines for good practice in citizen science, outlining various challenges that may spring up, and arguing for the benefits of citizen science in large scale projects.
**Wiggins, A., and K. Crowston. 2011. “From Conservation to Crowdsourcing: A Typology of Citizen Science.” In 2011 44th Hawaii International Conference on System Sciences (HICSS), 1–10. doi:10.1109/HICSS.2011.207.
    Wiggins and Crowston engage in a discussion of citizen science in terms of the common attributes many projects share, and attempt to provide a theoretical sampling that future citizen science projects may rely on. The authors argue that the majority of scholarship on citizen science is invested in describing the process of integrating volunteers into the various levels of scientific research, without taking into account the macrostructural and sociotechnical factors. They believe that this comes at the expense of crucial design and process management. Wiggins and Crowston identify and discuss five distinct typologies witnessed in various citizen science projects: action, conservation, investigation, virtuality, and education. The authors classify these typologies by major goals and extent to which they are virtual. One of the main motivations for developing these typologies is to describe the existing state of citizen science and to make accessible the necessary conditions for successful citizen science projects.

## References

* Bonney, Rick, Caren B. Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V. Rosenberg, and Jennifer Shirk. 2009. “Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy.” BioScience 59 (11): 977–84. http://dx.doi.org/10.1525/bio.2009.59.11.9.
* Gallo, Travis, and Damon Waitt. 2011. “Creating a Successful Citizen Science Model to Detect and Report Invasive Species.” BioScience 61 (6): 459–65. https://doi.org/10.1525/bio.2011.61.6.8.
* Jordan, Rebecca C., Heidi L. Ballard, and Tina B. Phillips. 2012. “Key Issues and New Approaches for Evaluating Citizen-Science Learning Outcomes.” Frontiers in Ecology and the Environment 10 (6): 307–9. https://doi.org/10.1890/110280.
* Mayer, Amy. 2010. “Phenology and Citizen Science.” BioScience 60 (3): 172–75. https://doi.org/10.1525/bio.2010.60.3.3.
* Newman, Greg, Andrea Wiggins, Alycia Crall, Eric Graham, Sarah Newman, and Kevin Crowston. 2012. “The Future of Citizen Science: Emerging Technologies and Shifting Paradigms.” Frontiers in Ecology and the Environment 10 (6): 298–304.
* Oberhauser, Karen, and Gretchen LeBuhn. 2012. “Insects and Plants: Engaging Undergraduates in Authentic Research through Citizen Science.” Frontiers in Ecology and the Environment 10 (6): 318–20. https://doi.org/10.1890/110274.
* Phillips, Tina, Janis Dickinson, Caren B. Cooper, and Rick Bonney. 2007. “Citizen Science as a Tool for Conservation in Residential Ecosystems.” Ecology and Society 12 (2): 11. http://www.ecologyandsociety.org/vol12/iss2/art11/.
* Prestopnik, Nathan, and Kevin Crowston. 2011. "Gaming for (Citizen) Science: Exploring Motivation and Data Quality in the Context of Crowdsourced Science through the Design and Evaluation of a Social Computational System." e-Science Workshops (eScienceW), 2011 IEEE Seventh International Conference, 28-33. http://doi.org/10.1109/eScienceW.2011.14.
* Purdam, Kingsley. 2014. “Citizen Social Science and Citizen Data? Methodological and Ethical Challenges for Social Research.” Current Sociology 62 (3): 374–92. https://doi.org/10.1177/0011392114527997.
* Rotman, Dana, Jenny Preece, Jen Hammock, Kezee Procita, Derek Hansen, Cynthia Parr, Darcy Lewis, and David Jacobs. 2012. “Dynamic Changes in Motivation in Collaborative Citizen-Science Projects.” In CSCW ’12 Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, 217–226. New York: ACM. https://doi.org/10.1145/2145204.2145238.
* Silvertown, Jonathan. 2009. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24 (9): 467–71. https://doi.org/10.1016/j.tree.2009.03.017.
* Wiggins, A., and K. Crowston. 2011. “From Conservation to Crowdsourcing: A Typology of Citizen Science.” In 2011 44th Hawaii International Conference on System Sciences (HICSS), 1–10. https://doi:.org/10.1109/HICSS.2011.207.