Who speaks for “citizen science” on Twitter? Which territory of citizen science have they made visible so far? This paper offers the first description of the community of users who dedicate their online social media identity to citizen science. It shows that Twitter users who identify with the term “citizen science” are mostly U.S. science professionals in environmental sciences, and rarely projects' participants. In contrast to the original concept of “citizen science”, defined as a direct relationship between scientists and lay participants, this paper makes visible a third category of
Identifying private gardens in the U.K. as key sites of environmental engagement, we look at how a longer-term online citizen science programme facilitated the development of new and personal attachments of nature. These were visible through new or renewed interest in wildlife-friendly gardening practices and attitudinal shifts in a large proportion of its participants. Qualitative and quantitative data, collected via interviews, focus groups, surveys and logging of user behaviours, revealed that cultivating a fascination with species identification was key to both ‘helping nature’ and wider
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TEAM MEMBERS:
Nirwan SharmaSam GreavesAdvaith SiddharthanHelen AndersonAnnie RobinsonLaura Colucci-GrayAgung Toto WibowoHelen BostockAndrew SalisburyStuart RobertsDavid SlawsonRené van der Wal
Online citizen science platforms increasingly provide types of infrastructural support previously only available to organisationally-based professional scientists. Other practices, such as creative arts, also exploit the freedom and accessibility afforded by the World Wide Web to shift the professional-amateur relationship. This paper compares communities from these two areas to show that disparate practices can learn from each other to better understand their users and their technology needs. Three major areas are discussed: mutual acknowledgement, infrastructural support, and platform
Effective classification of large datasets is a ubiquitous challenge across multiple knowledge domains. One solution gaining in popularity is to perform distributed data analysis via online citizen science platforms, such as the Zooniverse. The resulting growth in project numbers is increasing the need to improve understanding of the volunteer experience; as the sustainability of citizen science is dependent on our ability to design for engagement and usability. Here, we examine volunteer interaction with 63 projects, representing the most comprehensive collection of online citizen science
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TEAM MEMBERS:
Helen SpiersAlexandra SwansonLucy FortsonBrooke SimmonsLaura TrouilleSamantha BlickhanChris Lintott
In citizen science, user-centred development is often emphasised for its potential to involve participants in the development of technology. We describe the development process of the mobile app “Naturblick” as an example of a user-centred design in citizen science and discuss digital user feedback with regard to the users' involvement. We have identified three types of digital user feedback using qualitative content analysis: general user feedback, contributory user feedback and co-creational user feedback. The results indicate that digital user feedback can link UCD techniques with more
The growing interest in citizen science has resulted in a new range of digital tools that facilitate the interaction and communications between citizens and scientists. Considering the ever increasing number of applications that currently exist, it is surprising how little we know about how volunteers interact with these technologies, what they expect from them, and why these technologies succeed or fail. Aiming to address this gap, JCOM organized this special issue on the role of User Experience (UX) of digital technologies in citizen science which is the first to focus on the qualities and
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TEAM MEMBERS:
Artemis SkarlatidouMarisa PontiJames SprinksChristian NoldMuki HaklayEiman Kanjo
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
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.
We have created an instrument to measure the prevalance of various motivations in a population of volunteers in an online citizen science project. Our project is Zooniverse (www.zooniverse.org), a collection of citizen science projects that have grown out of the Galaxy Zoo website. The instrument is based on a theoretical model of motivation, which is described in the attached document.
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TEAM MEMBERS:
Jordan RaddickKaren CarneyJason ReedAndrea Lardner
This workshop is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants. The project will conduct a two-day workshop that will gather citizen science project leaders to address barriers in citizen science research and infrastructure: The inability to holistically study the movement, engagement, persistence and learning outcomes among volunteers engaged in multiple projects. The past few years have been a time of tremendous growth in awareness of and interest in citizen science projects. The project will address an increasing gap preventing projects in three now-popular categories (apps, projects hosted on government websites, and event-based projects) from adopting the digital tools created and available through SciStarter.com. The workshop will bring citizen science project leaders together to deepen an understanding of their needs regarding the adoption of digital tools, developed by Scistarter, which will result in more comprehensive data in support of research in informal science learning outcomes of volunteers engaged in citizen science across projects and platforms. The in-person and online contributions from participants will guide the development of resources and tutorials to scale adoption.
SciStarter is a repository of hundreds of citizen science projects. Through previous NSF support, SciStarter developed digital affiliate tools which project leaders use on their own websites to enable analytics (statistics gathered from user activity online) to help projects more easily recruit and coordinator volunteers, help volunteers track their contributions across projects and platforms, and help researchers holistically study the movement and learning outcomes across projects and platforms. The proposed workshop will facilitate iteration and adoption of the tools among three classes of projects, not originally accounted for, which have dramatically increased in numbers during the past year: 1) app-based projects, 2) projects hosted on government websites, and 3) event-based projects.. By co-designing and implementing iterative versions of the tools among these projects, the project will address important gaps in research, enable a richer, more comprehensive understanding of volunteer engagement patterns, and discover opportunities to build a stronger community of citizen science practitioners who collaborate to enhance volunteer learning communities. The project will culminate in improved research in this field and improved management of citizen science projects for appropriate recruitment and retention that fosters STEM learning.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Explore the Science of Spring: A Live Media Event is an Innovations in Development project produced by the signature PBS series Nature. The new primetime series Spring LIVE (working title) will break the frame of a traditional documentary, letting viewers themselves explore the dramatic seasonal changes of spring through the immediacy of live television. On-camera hosts, scientists and naturalists in locations across the U.S., and scores of citizen scientists will use observation and scientific inquiry to explore the workings of nature during this season of rebirth. The unfolding stories of seasonal change will illuminate larger scientific insights--into the biodiversity of species in habitats, the interconnectedness of plants and animals in diverse ecosystems, the global phenomenon of species migration, and how spring "green-up" can be affected by environmental change--while inspiring appreciation for species conservation and habitat preservation. Spring LIVE is conceived as an ongoing series, with this inaugural season composed of three one-hour programs broadcast live on three consecutive nights, along with real-time interactions via Facebook. Reaching long-standing Nature viewers (2.5 million per episode), Spring LIVE will seek to turn mature adults and diverse families into citizen science doers, and leverage younger Nature online audiences through social media and community engagement in partnership with citizen science projects.
Spring LIVE will build public knowledge of and engagement in phenology and citizen science. The project will also conduct knowledge-building research on the effectiveness of Facebook as a science learning tool. It will experiment with eliciting audience participation via Facebook within the live shows to generate synchronous, second-screen thought and discussion. An exploratory study by Multimedia Research will look at the impact of this feature, addressing the question: To what extent and how does Facebook interactivity within live science shows impact adult engagement, learning and motivation? Spring LIVE will also engage multiple partners to expand reach and impact and build capacity in their fields. National partners include the National Park Service and Next Avenue; citizen science partners include Celebrate Urban Birds, National Phenology Network, Monarch Blitz, and SciStarter, among others. PBS stations will work with these organizations to involve diverse, intergenerational audiences in observation of nature and seasonal change. Project evaluation, implemented by Knight Williams Research Communications, will focus on the impact of live television on science learning, and the success of the integration of citizen science projects on air, online, and in communities. This project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. In this exploratory Change Makers project, the Concord Consortium will develop, test, and evaluate a citizen science program that leverages innovative technology, such that youth engage directly with energy issues through scientific inquiry. The project will create the Infrared Street View, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable infrared camera attached to a smartphone. The infrared camera serves as a high-throughput data acquisition instrument that collects thousands of temperature data points each time a picture is taken. Youth will collect massive geotagged thermal data that have considerable scientific and educational value for visualizing energy usage and improving energy efficiency at all levels. The Infrared Street View program will provide a Web-based platform for youth and anyone interested in energy efficiency to view and analyze the aggregated data to identify possible energy losses. By sharing their scientific findings with stakeholders, youth will make changes to the way energy is being used. The project will start with school, public, and commercial buildings in selected areas where performing thermal scan of the buildings and publishing their thermal images for educational and research purposes are permitted by school leaders, town officials, and property owners. In collaboration with high schools and out-of-school programs in Massachusetts, this project will conduct pilot-tests with approximately 200 students.
To contribute to advancing learning, the study will probe three research questions: 1) Under what circumstances can technology bridge out-of-school and classroom science learning and improve learning on both sides? 2) To what extent can unobtrusive assessment based on data mining support research and evaluation of student learning in out-of-school settings? and 3) To what extent can instructional intelligence built into the app used in the program help students learn in out-of-school programs and improve the quality of data they contribute to the citizen science project? Data sources for investigating these questions include students' interaction data with the app logged behind the scenes and the images they have taken, as well as results based on traditional assessments from a small number of participants. Throughout the project, staff will widely disseminate project products and findings through the Internet, science fairs, conferences, publications, and partner networks. An eight-member Advisory Board consisting of cleantech experts, science educators, and educational researchers will oversee and evaluate this project.