Bridging the Gap: Inclusive Approaches to Data Literacy Across Disciplines

Buzzwords like "Open Science" and "Big Data" often capture attention but risk alienating those in the humanities and social sciences who may not see their work as data-driven. Simultaneously, STEM students’ confidence in handling quantitative data can mask gaps in their understanding of data literacy as a broader concept. At Carnegie Mellon University Libraries, we address these challenges by bridging the divide between STEM-centric and humanistic data literacy, equipping researchers with skills to engage effectively with data science across disciplines. This paper explores data literacy through an interdisciplinary lens, emphasizing the importance of qualitative and textual data practices alongside quantitative approaches. By recognizing that all data—no matter how abstract—is socially constructed, we challenge the notion that data literacy is solely quantitative. Our strategies include developing pedagogical tools that address epistemological differences, supporting humanities researchers in technical data methods, and broadening STEM students’ narrow definitions of data literacy to encompass the entire data lifecycle. Drawing on our expertise across disciplines, we have led initiatives to contextualize data literacy within humanities, social sciences, and STEM, fostering cross-disciplinary engagement. Our efforts focus on: Recognizing diverse disciplinary communicative practices, Developing flexible, adaptable pedagogical approaches, Decentering STEM-centric models of data engagement, and Emphasizing the role of qualitative research in data science. Through comparative analysis of institutional experiences, we highlight the importance of crafting inclusive, accessible educational approaches that respect diverse learning modes and prior experiences. Our work demonstrates how librarians can develop frameworks to empower researchers and students across academic communities, influencing university policy and fostering data fluency for all. This presentation provides a roadmap for integrating these principles into data literacy programs at other institutions.