A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of long...

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Autors principals:	Heidi Seibold, Severin Czerny, Siona Decke, Roman Dieterle, Thomas Eder, Steffen Fohr, Nico Hahn, Rabea Hartmann, Christoph Heindl, Philipp Kopper, Dario Lepke, Verena Loidl, Maximilian Mandl, Sarah Musiol, Jessica Peter, Alexander Piehler, Elio Rojas, Stefanie Schmid, Hannah Schmidt, Melissa Schmoll, Lennart Schneider, Xiao-Yin To, Viet Tran, Antje Völker, Moritz Wagner, Joshua Wagner, Maria Waize, Hannah Wecker, Rui Yang, Simone Zellner, Malte Nalenz
Format:	Artigo
Idioma:	Inglês
Publicat:	Public Library of Science (PLoS) 2021-01-01
Col·lecció:	PLoS ONE
Accés en línia:	https://doi.org/10.1371/journal.pone.0251194
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