“The first draft of anything is shit” – Ernest Hemingway
Given the current global pandemic, lockdowns, and reduced laboratory access, we suspect many Core Ideas listeners are currently at home preparing scientific manuscripts. So, on this episode, we decided to continue our Contagious Ideas arc by examining some of the peculiarities of preparing scientific articles, and maybe even provide some moral support for those working through the process for the first time.
Writing up data and analyses for publication is critical for the advancement of Science; however, it can receive little attention during a scientist’s undergraduate education. Preparing your first manuscript while “learning by doing” can be an intimidating task. As the final product will often be the most polished piece of writing you have yet produced, given the need to be as succinct and clear as possible (some simple rules to follow can be found here).
To begin a new manuscript, the general narrative or story that the article will convey must be laid out. In our experience, this is typically done through an initial figure list, that will help select the datasets and analyses to be included. The next (and most important) step is to get some words down on paper, as revising is often much easier than filling empty space. Some level of Imposter Syndrome is common, but it’s important to remember that you are the foremost expert on the planet regarding your own data.
Once a manuscript is complete, selecting a scientific journal tis another key decision. Outside of the obvious choices related to discipline, how high up the impact factor pyramid should you aim? Publication in a more prestigious journal may lead to increased exposure for your work, and more citations. However, there is a risk vs. reward calculation involved, as aiming too high and being quickly rejected may simply waste your time.
Peer review can also be a stressful experience, as comments from the reviewers can range from easy to doable to questionable to impossible. Two things to keep in mind, are that broader questions are sometimes just that, questions from someone less familiar with your data, and that addressing comments through subtraction is often an option (as sometimes comments asking you to add something are actually a roundabout way of telling you to remove something).
We end the episode with some of our own pet peeves when we are sitting in the reviewer seat (spoiler: terrible maps and articles too heavily reliant on prior studies to be read independently).
Until next time, we hope that your writing goes well!