These coding tools will help you cut mistakes from your scientific manuscripts
If you’ve written a scientific manuscript, there’s a good chance you’re familiar with the app-switching two-step that happens when you copy your data from one program and paste them into another. That time-tested workflow does the job, but it isn’t always the most efficient process. Perhaps you receive new samples and need to update your numbers. Or maybe you have to fix an error you made when processing your data.
It’s not the easiest way to write a paper, Bartholdy concedes. It requires computational know-how and a steep learning curve. And flexibility is needed when collaborating with less tech-savvy co-authors. But many argue that the pay-off is worth the investment. “It reduces the amount of stupid manual things that you have to do,” says Sarah Pederzani, a geochemist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany.
R Markdown, Jupyter Book and Observable all support inline code execution. Authors could, for instance, indicate the number of samples in a study by counting the rows in a table, or insert the version number of a computational package in their methods. “The inline code just completely allows you to sleep well at night,” Marwick says.RStudio, a development environment for R , includes a bare-bones what-you-see-is-what-you-get visual editor to ease the R Markdown writing process.
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