Embedded assessment (EA) is particularly well-suited for evaluating citizen science volunteers’ proficiency of science inquiry skills; however they remain uncommon in informal education. Using design-based research, we are examining processes to streamline EA development by building on existing data validation procedures within five citizen science projects. Here, we focus on the critical first step of supporting citizen science project leaders in identifying appropriate skills that are important, relevant, accessible, and potentially hiding in plain sight in their existing data. Our research
“Science crowdfunding” is a research funding system in which members of the public make small financial contributions towards a research project via the Internet. We compared the more common research process involving public research funding with science crowdfunding. In the former, academic-peer communities review the research carried out whereas the Crowd Community, an aggregation of backers, carries out this function in the latter. In this paper, we propose that science crowdfunding can be successfully used to generate “crowd-supported science” by means of this Crowd Community.
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Yuko IkkataiEuan McKayHiromi Yokoyama
For decades the idea that scientists, policy makers and industry know best in research and innovation has been convincingly challenged. The concept of Responsible Research and Innovation [RRI] combines various strands of critique and takes up the idea that research and innovation need to be democratized and must engage with the public in order to serve the public. The proposed future EU research funding framework programme, Horizon Europe, excludes a specific program line on research in RRI. We propose a number of steps the European Parliament should take to institutionalize RRI in Horizon
Citizen science (CS) terms the active participation of the general public in scientific research activities. With increasing amounts of information generated by citizen scientists, best practices to go beyond science communication and publish these findings to the scientific community are needed. This letter is a synopsis of authors' personal experiences when publishing results from citizen science projects in peer-reviewed journals, as presented at the Austrian Citizen Science Conference 2018. Here, we address authors' selection criteria for publishing CS data in open-access, peer-reviewed
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Gabriele GadermaierDaniel DorlerFlorian HeiglStefan MayrJohannes RudisserRobert BrodschneiderChristine Marizzi
The characteristics of interaction and dialogue implicit in the Web 2.0 have given rise to a new scenario in the relationship between science and society. The aim of this paper is the development of an evaluation tool scientifically validated by the Delphi method that permits the study of Internet usage and its effectiveness for encouraging public engagement in the scientific process. Thirty four indicators have been identified, structured into 6 interrelated criteria conceived for compiling data that help to explain the role of the Internet in favouring public engagement in science.
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Lourdes LopezMaria Dolores Olvera-Lobo
This INSPIRE award is partially funded by the Cyber-Human Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer Science and Engineering, the Gravitational Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, and the Office of Integrative Activities.
This innovative project will develop a citizen science system to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. Before the end of this decade it will open up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to noncosmic artifacts and noise that must be identified and separated from cosmic signals. Teaching computers to identify and morphologically classify these artifacts in detector data is exceedingly difficult. Human eyesight is a proven tool for classification, but the aLIGO data streams from approximately 30,000 sensors and monitors easily overwhelm a single human. This research will address these problems by coupling human classification with a machine learning model that learns from the citizen scientists and also guides how information is provided to participants. A novel feature of this system will be its reliance on volunteers to discover new glitch classes, not just use existing ones. The project includes research on the human-centered computing aspects of this sociocomputational system, and thus can inspire future citizen science projects that do not merely exploit the labor of volunteers but engage them as partners in scientific discovery. Therefore, the project will have substantial educational benefits for the volunteers, who will gain a good understanding on how science works, and will be a part of the excitement of opening up a new window on the universe.
This is an innovative, interdisciplinary collaboration between the existing LIGO, at the time it is being technically enhanced, and Zooniverse, which has fielded a workable crowdsourcing model, currently involving over a million people on 30 projects. The work will help aLIGO to quickly identify noise and artifacts in the science data stream, separating out legitimate astrophysical events, and allowing those events to be distributed to other observatories for more detailed source identification and study. This project will also build and evaluate an interface between machine learning and human learning that will itself be an advance on current methods. It can be depicted as a loop: (1) By sifting through enormous amounts of aLIGO data, the citizen scientists will produce a robust "gold standard" glitch dataset that can be used to seed and train machine learning algorithms that will aid in the identification task. (2) The machine learning protocols that select and classify glitch events will be developed to maximize the potential of the citizen scientists by organizing and passing the data to them in more effective ways. The project will experiment with the task design and workflow organization (leveraging previous Zooniverse experience) to build a system that takes advantage of the distinctive strengths of the machines (ability to process large amounts of data systematically) and the humans (ability to identify patterns and spot discrepancies), and then using the model to enable high quality aLIGO detector characterization and gravitational wave searches
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TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
This study examines the relative efficacy of citizen science recruitment messages appealing to four motivations that were derived from previous research on motives for participation in citizen-science projects. We report on an experiment (N=36,513) that compared the response to email messages designed to appeal to these four motives for participation. We found that the messages appealing to the possibility of contributing to science and learning about science attracted more attention than did one about helping scientists but that one about helping scientists generated more initial
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Tae Kyoung LeeKevin CrowstonMahboobeh HarandiCarsten ØsterlundGrant Miller
A report following the 2016 Environmental Health Summit recommended engaging citizens in creating their own knowledge and solutions, thus ensuring that their concerns are adequately addressed and promoting sustainability of community projects. Indeed, citizen science has the potential to initiate a cascade of events with a positive ripple effect that includes a more diverse future STEM and biomedical workforce. This SEPA proposal involves the establishment of WE ENGAGE – an informal, citizen science-based, environmental health experiential learning program designed in partnership with and for under resourced communities struggling with health and environmental health challenges. Its purpose is to actively engage and build the citizen science capacity of citizens living in a single cluster of three contiguous under resourced, minority Cincinnati neighborhoods where generational challenges continue to plague residents despite the presence of established academic-community partnerships. Our hypothesis is that community-informed, experiential learning opportunities outside of the classroom that are structured, multi-generational, and story-based will encourage a) the active asking, discussion about, and answering of relevant complex health and environmental questions so that individuals and communities can plan action steps to make better health choices and pursue healthier environments, and b) greater interest and confidence in pursuing formal biomedical/STEM education and STEM careers. Our program has three specific aims: 1) We will co-create tailored story- based (graphic novel style) STEM education materials with a community advisory board and offer informal STEM education and research training to our target communities; 2) we will facilitate the application of scientific inquiry skills to improve health via community-led health fairs that use an innovative electronic health passport platform to collect data and through facilitated community discussions of health fair data to generate motivating stories to share; and 3) we will facilitate the application of scientific inquiry skills to foster community pride and activism in promoting healthier/safer built environments via walking environmental assessments. As in aim 2, facilitated discussions will be held to spur future community based participatory research studies and interventions. Critical to our success is the concept of storytelling. Storytelling is a foundation of the human experience. A key purpose of storytelling is not just understanding the world, but positively transforming it. It is a common language. Bringing together STEM concepts in the form of a story increases their appeal and meaning. Later, the very process of community data collection gives individuals a voice. In a data story, hundreds to millions of voices can be distilled into a single narrative that can help community members probe important underlying associations and get to the root causes of complicated health issues relevant to their communities. Through place based, understandable, motivating data stories, the community’s collective voice is clearer—leading to relevant and viable actions that can be decided and taken together. From preventing chronic disease, to nurturing healthier environments, to encouraging STEM education — stories have unlimited potential.
Public Health Relevance Statement:
Narrative WE ENGAGE is an informal citizen science-based, experiential learning program designed in partnership with and for middle schoolers to adults living in under resourced minority communities. Using the power of data collection and storytelling, its purpose is to actively engage citizens in STEM/research education and training to encourage a more diverse future workforce and to sustainably build local capacity to ask and answer complex health and environmental questions relevant to their communities. Further, by engaging citizens and giving them a more equitable stake in the research process, they are better able to discover their own solutions.
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Melinda Sue ButschkovacicSusan Ann Hershberger
The goals of this proposal are: 1) to provide opportunities for underrepresented students to consider careers in basic or clinical research by exciting them through an educational Citizen Science research project; 2) to provide teachers with professional development in science content and teaching skills using research projects as the infrastructure; and 3) to improve the environments and behaviors in early childcare and education settings related to healthy lifestyles across the state through HSTA students Citizen Science projects. The project will complement or enhance the training of a workforce to meet the nation’s biomedical, behavioral and clinical research needs. It will encourage interactive partnerships between biomedical and clinical researchers,in-service teachers and early childcare and education facilities to prevent obesity.
Specific Aim I is the Biomedical Summer Institute for Teachers led by university faculty. This component is a one week university based component. The focus is to enhance teacher knowledge of biomedical characteristics and problems associated with childhood obesity, simple statistics, ethics and HIPAA compliance, and the principles of Citizen Science using Community Based Participatory Research (CBPR). The teachers, together with the university faculty and staff, will develop the curriculum and activities for Specific Aim II.
Specific Aim II is the Biomedical Summer Institute for Students, led by HSTA teachers guided by university faculty. This experience will expose 11th grade HSTA students to the biomedical characteristics and problems associated with obesity with a focus on early childhood. Students will be trained on Key 2 a Healthy Start, which aims to improve nutrition and physical activity best practices, policies and environments in West Virginia’s early child care and education programs. The students will develop a meaningful project related to childhood obesity and an aspect of its prevention so that the summer institute bridges seamlessly into Specific Aim III.
Specific Aim III is the Community Based After School Club Experiences. The students and teachers from the summer experience will lead additional interested 9th–12th grade students in their clubs to examine their communities and to engage community members in conducting public health intervention research in topics surrounding childhood obesity prevention through Citizen Science. Students and teachers will work collaboratively with the Key 2 a Healthy Start team on community projects that will be focused on providing on-going technical assistance that will ultimately move the early childcare settings towards achieving best practices related to nutrition and physical activity in young children.
Citizen science is a form of Public Participation in Scientific Research (PPSR) in which the participants are engaged in the scientific process to support research that results in scientifically valid data. Opportunities for participation in real and authentic scientific research have never been larger or broader than they are today. The growing popularity and refinement of PPSR efforts (such as birding and species counting studies orchestrated by the Cornell Lab of Ornithology) have created both an opportunity for science engagement and a need for more research to better implement such projects in order to maximize both benefits to and contributions from the public.
Towards this end, Shirk et al. have posted a design framework for PPSR projects that delineates distinct levels of citizen scientist participation; from the least to the highest level of participation, these categories are contract, contribute, collaborate, co-create, and colleagues. The distinctions among these levels are important to practitioners seeking to design effective citizen science programs as each increase in citizen science participation in the scientific process is hypothesized to have both benefits and obstacles. The literature on citizen science models of PPSR calls for more research on the role that this degree of participation plays in the quality of that participation and related learning outcomes (e.g., Shirk et al., 2012; Bonney et al., 2009). With an unprecedented interest in thoughtfully incorporating citizen science into health-based studies, citizen science practitioners and health researchers first need a better understanding of the role of culture in how different communities approach and perceive participation in health-related studies, the true impact of intended educational efforts from participation, and the role participation in general has on the scientific process and the science outcome.
Project goal to address critical barrier in the field: Establish best practices for use of citizen science in the content area of human health-based research, and better inform the design of future projects in PPSR, both in the Denver Museum of Nature & Science’s Genetics of Taste Lab (Lab), and importantly, in various research and educational settings across the field.
Aims
Understand who currently engages in citizen science projects in order to design strategies to overcome the barriers to participation that occur at each level of the PPSR framework, particularly among audiences underrepresented in STEM.
Significantly advance the current knowledge regarding how citizen scientists engage in, and learn from, and participate in the different levels of the PPSR framework.
Determine the impact that each stage of citizen science participation has on the scientific process.
A team of experts from five institutions (University of Minnesota, Adler Planetarium, University of Wyoming, Colorado State University, and UC San Diego) links field-based and online analysis capabilities to support citizen science, focusing on three research areas (cell biology, ecology, and astronomy). The project builds on Zooniverse and CitSci.org, leverages the NSF Science Gateways Community Institute, and enhances the quality of citizen science and the experience of its participants.
This project creates an integrated Citizen Science Cyberinfrastructure (CSCI) framework that expands the capacity of research communities across several disciplines to use citizen science as a suitable and sustainable research methodology. CSCI produces three improvements to the infrastructure for citizen science already provided by Zooniverse and CitSci.org:
Combining Modes - connecting the process of data collection and analysis;
Smart Assignment - improving the assignment of tasks during analysis; and
New Data Models - exploring the Data-as-Subject model. By treating time series data as data, this model removes the need to create images for classification and facilitates more complex workflows. These improvements are motivated and investigated through three distinct scientific cases:
Biomedicine (3D Morphology of Cell Nucleus). Currently, Zooniverse 'Etch-a-Cell' volunteers provide annotations of cellular components in images from high-resolution microscopy, where a single cell provides a stack containing thousands of sliced images. The Smart Task Assignment capability incorporates this information, so volunteers are not shown each image in a stack where machines or other volunteers have already evaluated some subset of data.
Ecology (Identifying Individual Animals). When monitoring wide-ranging wildlife populations, identification of individual animals is needed for robust estimates of population sizes and trends. This use case combines field collection and data analysis with deep learning to improve results.
Astronomy (Characterizing Lightcurves). Astronomical time series data reveal a variety of behaviors, such as stellar flares or planetary transits. The existing Zooniverse data model requires classification of individual images before aggregation of results and transformation back to refer to the original data. By using the Data-as-Subject model and the Smart Task Assignment capability, volunteers will be able to scan through the entire time series in a machine-aided manner to determine specific light curve characteristics.
The team explores the use of recurrent neural networks (RNNs) to determine automated learning architectures best suited to the projects. Of particular interest is how the degree to which neighboring subjects are coupled affects performance. The integration of existing tools, which is based on application programming interfaces (APIs), also facilitates further tool integration. The effort creates a citizen science framework that directly advances knowledge for three science use cases in biomedicine, ecology, and astronomy, and combines field-collected data with data analysis. This has the ability to solve key problems in the individual applications, as well as benefiting the research of the dozens of projects on the Zooniverse platform. It provides benefits to researchers using citizen scientists, and to the nearly 1.6 million citizen scientists themselves.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Research on Learning in Formal and Informal Settings, within the NSF Directorate for Education and Human Resources.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
The Adler Planetarium, Johns Hopkins University, and Southern Illinois University-Edwardsville are investigating the potential of online citizen science projects to broaden the pool of volunteers who participate in analysis and investigation of digital data and to deepen volunteers' engagement in scientific inquiry. The Investigating Audience Engagement with Citizen Science project is administering surveys and conducting case studies to identify factors that lead volunteers to engage in the astronomy-focused Galaxy Zoo project and its Zooniverse extensions. The project is (1) identifying volunteers' motivations for joining and staying involved, (2) determining factors that influence volunteers' movement from lower to higher levels of involvement, and (3) designing features that influence volunteer involvement. The project's research findings will help informal science educators and scientists refine existing citizen science programs and develop new ones that maximize volunteer engagement, improve the user experience, and build a more scientifically literate public.