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A tutorial on computationally reproducible research
Abstract
Science is an exercise in seeing further by standing on the shoulders of giants. But what happens when those shoulders give way? The replication crisis has shaken confidence in published findings, with surveys revealing that more than 70% of researchers have failed to replicate another scientist's experiments (Baker, 2016). Even when researchers attempt the seemingly simpler task of computational reproducibility—applying the same analysis to the same data—success is far from guaranteed. A recent large-scale investigation found that only 52.6% of social and behavioral science papers could be precisely reproduced, even when original data were available (Miske et al., 2026). The costs are substantial: wasted resources, delayed discoveries, and eroded public trust in science.