Reproducibility is a foundation of the scientific method that is difficult to achieve in practice, contributing to a perceived 'crisis' in peer-reviewed research (Baker, 2016). A push towards more transparent, reproducible methods has inspired rapid development of new tools for data science, including R Markdown and R Notebooks with R Studio (RStudio Team, 2016) to generate reproducible analysis and reports in R (R Core Team, 2018). In contrast, tools to reduce errors and improve transparency at earlier stages have received relatively little attention. In biological studies, these earlier stages may include sample collecting, labelling, measuring, subsampling and tracking. Documenting these earlier stages is crucial for data sharing and scientific inquiry because errors in sample labelling and data collection propagate through all downstream analyses. As such, a reproducible analysis on faulty data can be scientifically less robust than an irreproducible analysis on carefully curated data.In many of the biological sciences, samples are collected under arduous field conditions that pose challenges for labelling, organizing, measuring and analysing samples. A common approach is to collect and preserve samples for later analysis, which may involve multiple users, movement to different locations, transferring to different storage media or vessels, subsampling and archiving. Misinterpreted labels, ambiguous handwriting, cut-and-paste errors, typographical
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