This article conceptualizes the notion of magic(al)ing in relation to post-pandemic ways of thinking about data production and analyses. Revisiting old data produced pre-COVID-19 and engaging with new data produced during COVID-19, we consider the possibilities and potential of magic(al)ing as a theoretical concept. We think with several ideas…

Large data sets invite students to engage in scientific practices such as question asking, identifying correlations, using visualizations, and practicing data literacy in an authentic context. However, authentic data sets are rarely introduced in the biology classroom. We prepared an online inquiry activity based on authentic gross characteristics…

Being able to understand, interpret, and critically evaluate data is necessary for all individuals in our society. Using the PreK-12 Guidelines for Assessment and Instruction in Statistics Education-II (GAISE-II; Bargagliotti et al., 2020) curriculum framework, the current paper outlines five evidence-based recommendations that teachers can use to…

This article discusses how each of the papers in this special issue explored some combination of subject, audience, and data scientist perspectives with an eye toward helping students situate their relationship to data. Specifically, within the data scientist perspective, the papers examined a variety of ways in which students can relate to data…

Cyberinfrastructure (CI) empowers the foundational computation resources underlying data analytics, spatial modeling, and many other domains serving the growing knowledge economy in the United States. In every part of these interactions with CI, questions of how to seamlessly integrate CI training into educational programs exist. In this article,…