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Direct linkVilhuber, Lars; Son, Hyuk Harry; Welch, Meredith; Wasser, David N.; Darisse, Michael – Journal of Statistics and Data Science Education, 2022
We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular…
Descriptors: Statistics Education, Data Science, STEM Education, Social Sciences
Towse, John; Davies, Rob; Ball, Ellie; James, Rebecca; Gooding, Ben; Ivory, Matthew – Journal of Statistics and Data Science Education, 2022
We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the "L"ancaster "U"niversity "ST"atistics "RE"sources (LUSTRE) package, used to manage student research project data in psychology and build…
Descriptors: Data Analysis, Information Management, Open Source Technology, Data Science
Rethlefsen, Melissa L.; Norton, Hannah F.; Meyer, Sarah L.; MacWilkinson, Katherine A.; Smith, Plato L.; Ye, Hao – Journal of Statistics and Data Science Education, 2022
Research Reproducibility: Educating for Reproducibility, Pathways to Research Integrity was an interdisciplinary, conference hosted virtually by the University of Florida in December 2020. This event brought together educators, researchers, students, policy makers, and industry representatives from across the globe to explore best practices,…
Descriptors: Interdisciplinary Approach, Educational Research, Replication (Evaluation), Integrity
