Abstract As data science advances with the emergence of new computational technologies, science data literacy (SDL)—the ability to understand, use, and critically engage with data to address real-world scientific problems—becomes increasingly vital in science education. However, research on how data practices vary across scientific disciplines and educational levels remains limited, hindering the development of a more cohesive understanding of how SDL can be systematically integrated into diverse educational contexts. This review examines SDL by analyzing 42 peer-reviewed empirical studies (2000–2023) to investigate how students have been engaged in data practices across K–16 science education research. We identify seven core data practices: understanding problems, designing experiments, collecting data, cleaning data, analyzing data, and evaluating and disseminating results, along with their associated facets. Through frequency and association analyses, we observe systematic differences in how these practices are emphasized across disciplines, grade levels, and data types. The reviewed studies suggest that engagement in data practices is associated with both cogn
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