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Scientific publications have not traditionally been accompanied by data, either during the peer review process or when published. Concern has arisen that the literature in many fields may contain inaccuracies or errors that cannot be detected without inspecting the original data. Here, we introduce SPRITE (Sample Parameter Reconstruction via Interative TEchniques), a heuristic method for reconstructing plausible samples from descriptive statistics of granular data, allowing reviewers, editors, readers, and future researchers to gain insights into the possible distributions of item values in the original data set. This paper presents the principles of operation of SPRITE, as well as worked examples of its practical use for error detection in real published work. Full source code for three software implementations of SPRITE (in MATLAB, R, and Python) and two web-based implementations requiring no local installation (1, 2) are available for readers.
This pre-print manuscript version (1.0) serves as a longer exposition of the technique; we expect at present it will be reformatted and condensed for future publication.