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Quickly disseminating an innovative, timely afterschool program raises challenges, from recruitment and professional development to assessment, program fidelity, and quality. In this paper, we describe our experience as project developers, trainers, and researchers working with an afterschool network, Imagine Science, to disseminate a middle school club program about epidemic diseases and data. What we learned from working with this network may be useful to others who have created an afterschool science, technology, engineering, and mathematics (STEM) program they hope to spread widely. 

Data science is not a spectator sport: It involves adventure, observation, perseverance, and service, as demonstrated by the 50-year-long endeavor to re-establish puffin colonies in Maine. Our project, based on lead scientist Stephen Kress’s children’s books about the puffins’ comeback, engages middle school science students in the data-driven work of ornithologists. Monitoring seabird restoration demands different types of data, ranging from physiological characteristics of birds to environmental factors, such as fish availability and changing sea surface temperature. Scientists doing restoration work must be persistent and inquisitive users of data. Our project’s aim is to demonstrate to middle school students the centrality of data in saving seabird species that are threatened or endangered. Research on the project’s impact focuses on data competencies, data values, and data fascination. A major audience is multilingual learners who are recent Maine immigrants, along with other sixth-grade students who, like puffins, consider Maine their home. 

We examine how developers of data science curricula determine what makes a pedagogically effective dataset enabling 10–14 year-old students (“middle school” in the United States) to engage in the data investigation cycle by posing their own questions about relationships among variables. We describe strategies for curating existing datasets to address goals for learning about data, and for optimizing the use of these datasets once they are curated. We investigate how data science educators can transform existing datasets into ones appropriate for students with little data experience, drawing on our experience working with several publicly available datasets, which students explored in CODAP (the Common Online Data Analysis Platform).