This project seeks to apply explainable artificial intelligence to the challenge of personalizing training for adult citizen scientists. The approach will be developed in the context of the Native Bee Watch (NBW) biodiversity monitoring project that began in 2016 at Colorado State University.
DATE:
-
TEAM MEMBERS:
Sarath SreedharanNikhil KrishnaswamyJill ZarestkyNathaniel Blanchard
We present the assets that collaboration across a land grant university brought to the table, and the Winterberry Citizen Science program design elements we have developed to engage our 1080+ volunteer berry citizen scientists ages three through elder across urban and rural, Indigenous and non-Indigenous, and formal and informal learning settings.
DATE:
TEAM MEMBERS:
Katie SpellmanJasmine ShawChristine VillanoChrista MulderElena SparrowDouglas Cost
We used a youth focused wild berry monitoring program that spanned urban and rural Alaska to test this method across diverse age levels and learning settings.
DATE:
TEAM MEMBERS:
Katie SpellmanDouglas CostChristine Villano
Plants with persistent fleshy fruits that last throughout fall and into winter and spring are an important source of nutrition for animals and people in boreal, subarctic, and arctic regions, but little information on fruit retention or loss is available for these regions. We evaluated fruit loss for four species across Alaska using data from our Winterberry community science network.
The Arctic Harvest-Public Participation in Scientific Research (which encompasses the Winterberry Citizen Science program), a four-year citizen science project looking at the effect of climate change on berry availability to consumers has made measurable progress advancing our understanding of key performance indicators of highly effective citizen science programs.
DATE:
TEAM MEMBERS:
Angela LarsonKelly KealyMakaela Dickerson
This project's goals are to:
Enable participants to contribute to any or all stages of the scientific process and enhance their learning using an online citizen science platform and live bird cams.
Generate new scientific knowledge about wildlife.
Advance the understanding of effective project design for co-created online citizen-science projects at a national scale.
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Informal learning institutions (ILIs) create opportunities to increase public understanding of science and promote increased inclusion of groups underrepresented in Science, Technology, Engineering, and Math (STEM) careers but are not equally distributed across the United States. We explore geographic gaps in the ILI landscape and identify three groups of underserved counties based on the interaction between population density and poverty percentage. Among ILIs, National Park Service lands, biological field stations, and marine laboratories occur in areas with the fewest sites for informal
Final External Evaluation Report for Informal STEM Learning at Biological Field Stations, an NSF AISL Exploratory Pathways project, which studied the pedagogical and andragogical characteristics of informal educational outreach activities at field stations. This report summarizes the project team’s major research activities and the contextual factors that supported that work.
Appendix includes interview protocol.
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
DATE:
TEAM MEMBERS:
Nirwan SharmaSam GreavesAdvaith SiddharthanHelen AndersonAnnie RobinsonLaura Colucci-GrayAgung Toto WibowoHelen BostockAndrew SalisburyStuart RobertsDavid SlawsonRené van der Wal
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
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.