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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Detalles Bibliográficos
Autores principales: Seibold, Heidi, Czerny, Severin, Decke, Siona, Dieterle, Roman, Eder, Thomas, Fohr, Steffen, Hahn, Nico, Hartmann, Rabea, Heindl, Christoph, Kopper, Philipp, Lepke, Dario, Loidl, Verena, Mandl, Maximilian, Musiol, Sarah, Peter, Jessica, Piehler, Alexander, Rojas, Elio, Schmid, Stefanie, Schmidt, Hannah, Schmoll, Melissa, Schneider, Lennart, To, Xiao-Yin, Tran, Viet, Völker, Antje, Wagner, Moritz, Wagner, Joshua, Waize, Maria, Wecker, Hannah, Yang, Rui, Zellner, Simone, Nalenz, Malte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216542/
https://www.ncbi.nlm.nih.gov/pubmed/34153038
http://dx.doi.org/10.1371/journal.pone.0251194

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216542/
https://www.ncbi.nlm.nih.gov/pubmed/34153038
http://dx.doi.org/10.1371/journal.pone.0251194

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