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Abstract Citizen science involves the public in science to investigate research questions. Although citizen science facilitates learning in informal educational settings, little is known about its use or effects in postsecondary (college or university) settings. Using a literature review and a survey, we describe how and why citizen science is being used in postsecondary courses, as well as the impacts on student learning. We found that citizen science is used predominantly in biologically related fields, at diverse types of institutions, to improve student engagement and expose students to authentic research. Considerable anecdotal evidence supporting improved student learning from these experiences exists, but little empirical evidence exists to warrant any conclusion. Therefore, there is a need to rigorously assess the relationship between citizen science participation and postsecondary student learning. We highlight considerations for instructors planning to incorporate citizen science and for citizen science projects wanting to facilitate postsecondary use.
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Citizen Science Amplification
We introduce the idea of Citizen Scientist Amplification applying the method to data gathered from the top 10 contributing citizen scientists on the Supernova Hunters project. We take a novel approach to avail of the complementary strengths of deep learning and citizen science achieving results that are competitive with experts.
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- Award ID(s):
- 1835530
- PAR ID:
- 10206942
- Date Published:
- Journal Name:
- HCOMP 2019
- Page Range / eLocation ID:
- 126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Citizen science yields increased scientific capacity in exchange for science literacy and promises of a more responsive science to society’s needs. Yet, citizen science projects are criticized for producing few scientific outputs and having exploitative relationships with the citizens who participate. In the eagerness to capture new data, scientists can fail to see the value of citizen scientists’ expertise beyond data generation and can forget to close the loop with outputs that benefit the public interest. Citizen scientists are experts in their local environments who, when asked, can improve scientific processes and products. To the degree that citizen scientists are relegated to data collection, we shortchange opportunities to advance science. Rather than merely critique, we present an evidence-based engagement approach for listening to citizen scientist participants and incorporating their input into science processes and products that can be retrofitted onto existing citizen science projects or integrated from a project’s inception. We offer this adaptable blueprint in four steps and illustrate this approach via a crowdsourced hydrology project on the Boyne River, USA. We show how engaging voices of citizen scientists at key points in the project improves both the products of science (a real-time ecohydrological model) and the process of conducting the science (adaptations to help improve data collection). Distinct from outreach or education, considering citizen scientists as an equally interesting site of inquiry can improve the practice and outputs of science.more » « less
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Abstract Citizen science is personal. Participation is contingent on the citizens’ connection to a topic or to interpersonal relationships meaningful to them. But from the peer-reviewed literature, scientists appear to have an acquisitive data-centered relationship with citizens. This has spurred ethical and pragmatic criticisms of extractive relationships with citizen scientists. We suggest five practical steps to shift citizen-science research from extractive to relational, reorienting the research process and providing reciprocal benefits to researchers and citizen scientists. By virtue of their interests and experience within their local environments, citizen scientists have expertise that, if engaged, can improve research methods and product design decisions. To boost the value of scientific outputs to society and participants, citizen-science research teams should rethink how they engage and value volunteers.more » « less
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Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.more » « less
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CitSci.org is a global citizen science software platform and support organization housed at Colorado State University. The mission of CitSci is to help people do high quality citizen science by amplifying impacts and outcomes. This platform hosts over one thousand projects and a diverse volunteer base that has amassed over one million observations of the natural world, focused on biodiversity and ecosystem sustainability. It is a custom platform built using open source components including: PostgreSQL, Symfony, Vue.js, with React Native for the mobile apps. CitSci sets itself apart from other Citizen Science platforms through the flexibility in the types of projects it supports rather than having a singular focus. This flexibility allows projects to define their own datasheets and methodologies. The diversity of programs we host motivated us to take a founding role in the design of the PPSR Core, a set of global, transdisciplinary data and metadata standards for use in Public Participation in Scientific Research (Citizen Science) projects. Through an international partnership between the Citizen Science Association, European Citizen Science Association, and Australian Citizen Science Association, the PPSR team and associated standards enable interoperability of citizen science projects, datasets, and observations. Here we share our experience over the past 10+ years of supporting biodiversity research both as developers of the CitSci.org platform and as stewards of, and contributors to, the PPSR Core standard. Specifically, we share details about: the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. We conclude by sharing overall successes, limitations, and recommendations as they pertain to trust and rigor in citizen science data sharing and interoperability. As the scientific community moves forward, we show that Citizen Science is a key tool to enabling a systems-based approach to ecosystem problems.more » « less
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