Editing, Succinctness, and Jargon/Editing, Succinctness, and Dealing with Jargon

Introduction

It is always important to write things as succinctly as possible when communicating science. You will find that you can eliminate unnecessary words from your sentences when editing initial drafts, and this is one way of making sure you are writing succinctly. On this page, you can find tips for shortening sentences, as well as advice for dealing with the jargon that you will certainly have to deal with in your writing.

When editing your work, you will often find that you can make things more concise just by writing in the active voice (rather than the passive). For more information on this, see the ‘Active vs. Passive’ page on our site. It is also important to use simple words, and to remove ambiguous/confusing words from your writing when editing. For advice on this, see the ‘Clarity – Using Simple Language’ page on our site.

Making Your Sentences Succinct

The simplest way of ensuring your sentences are as succinct as possible is to make sure every single word counts. You can do this by seeing whether each sentence would have the same meaning if certain words were removed. For example, in the first sentence in this paragraph, you could remove ‘single’ and it would mean the same thing. This might not seem that important in this example, but over the course of a long piece of writing, you will be surprised by how many words you can remove.

The second thing you should do when editing your work to make it more succinct is to check the transition words and phrases that link all of the different sentences together. There is an art to this, because these transitions are critical to making sure your writing reads well and follows a logical path. However, if there are simpler, more succinct alternatives to the transitions you have used, it would be a wise idea to change them. For example, why say “Despite the fact that,” when you could simply say “Despite…”

Dealing With Jargon

Science is a subject that requires you to use a certain amount of jargon, but this is potentially very problematic when you are communicating to non-specialist audiences. As a result, it is very important that you: 1) limit its use whenever possible, and 2) explain it using simple analogies (see our ‘Using Comparisons and Descriptions’ page).

Whenever possible, try not to have more than one jargon-heavy term in a single sentence, and use parentheses and abbreviations as a way of splitting up a passage of writing around these important but confusing terms. By placing the initial explanation of a term in parentheses, it forces your reader to pay special attention to it and it also sticks out from a body of text so that it is easy for your reader to skim back to it if they need to remind themselves of the explanation later on.

Some Examples

In the following examples, the second (B) version provides an example of dealing with jargon present in the first (A) version, before being edited for succinctness to give a final version (C).

1A: It is within the thermosphere that ionization occurs.

1B: The thermosphere is the layer of the Earth’s atmosphere that sits just below outer space. This is the part of the atmosphere in which individual atoms and molecules become charged (due to the loss or addition of electrons).

1C: Atoms and molecules become charged (due to losing or gaining electrons) in the thermosphere (the part of the Earth’s atmosphere that sits just below outer space).

2A: Our Linear Regression Model Fitter computer programme has proved to be very effective in highlighting patterns in bacterial populations. The Linear Regression Model Fitter computer programme was used by our researchers to show that antibiotic resistance can develop within 20 generations.

2B: Our Linear Regression Model Fitter computer programme (LRMF), which compares bacterial survival rate as drug dose changes, has proved to be very effective in highlighting patterns in bacterial populations. The LRMF was used by our researchers to show that antibiotic resistance can develop within 20 generations.

2C: Our Linear Regression Model Fitter computer programme (LRMF), which compares bacterial survival rate as drug dose changes, is effective in highlighting bacterial population patterns. The LRMF showed that antibiotic resistance can develop in 20 generations.