10 Tips for Writing Scientific Papers
By: Ygor Rebouças Serpa
Tags: Writing, Education, Data Science, Writing Tips, Academia

#1: Adhere to the Macro

Form matters. Writing in a pre-defined form eases reading, as readers have a sense of what’s next and can easily relate your work to others. Most papers stick to the following meta-structure, and you should do so as well:

Abstract: One paragraph synthesis of the entire document. Introduction: Field, problems, and contributions. Related work: To-date solutions and how they are lacking Body: Free-zone. Describe your innovative work here. Results: Comparison to the state-of-the-art; proof of usefulness Conclusions: Sum-up and avenues for future investigation.

While there are some exceptions, this is the structure you will see the most when reading other people’s work. These sections might mix or swap, but they are always there. What usually changes, from paper to paper, is the internal structure within each section.

#2: Pay Attention to the Micro

Inside each paragraph, each sentence should have a reason to exist. A simple exercise is to name each sentence based on its role to the whole. If you are unable to name or label it, remove it. Another way to put it is: all sentences must add up to the paragraph conclusion. This tip is about finding and eliminating text that is not aiding or building towards anything.

As an example, picking a paper at random, see the first paragraph of the Ray tracing on programmable graphics hardware paper:

Ray tracing on programmable graphics hardware first paragraph.

Here, I color-coded each sentence. Try for yourself to label each one based on their role/task. Moreover, which sentences depend on others?

In order: the topic introduction (yellow), computing power has increased (blue), real-time ray-tracing is real (green), special chips are being made (pink), and conclusion (teal). Yellow states ray-tracing, which is mentioned in all other sentences. Blue and green do not depend on each other. Pink adds to the information of blue and green. Teal gathers everything to conclude the paragraph. Therefore, every sentence has a role in the conclusion.

If the authors were having trouble fitting the page count needed for a specific submission, this rationale could be used to find which are the least essential sentences. In this example, between blue, green, and pink, pink is the least informative argument and would be my candidate for removal.

#3: Write like J. S. Bach

In the previous example, the microstructure of the paragraph includes an introduction (yellow) and ends in a conclusion (teal). At the section scope, there is an introductory paragraph and a conclusive paragraph. The same goes for the paper scope, with the introduction and conclusion sections. As in any Bach’s piece, your text should be a constant stream of arguments and conclusions. Paragraphs must deliver something. Sections must deliver something. The paper has to deliver something. All levels matter.

There is no need to be a trained musician to understand this. Close your eyes and listen to the first minute of the Chaconne. Pay attention to how it constantly argues, counter-argues, and concludes the musical phrases.

Bach’s Greatest Work: The Chaconne, played by Hilary Hahn

The exercise is: for each paragraph, ask yourself: what it brings to the table? How does this add to the section narrative? Inconclusive paragraphs should be reviewed, merged, or removed. The same goes for conclusive paragraphs that do not add to the section message. The previous tip was about removing unnecessary text. This one is about tying everything up into a narrative.

#4: A Strong Related Work is a Strong Work

Out of all sections, the related work is the one that is more often lacking. By the time a reader reaches this section, he/she should be aware of the problems you are targeting, but not exactly sure what has been done to tackle it. The job of the related work is two-fold: to remark the important literature and to delineate a common issue with most to-date solutions.

A weak related work merely states what has been done so far. It does not pose a question or shows a common weakness. In a sense, it does not give a reason for your paper to exist. Why should there be another paper if there is no lingering problem to be solved?

An excellent related work values the superb work that has been done so far, while elegantly stating that there is still more to be done; and one or several of these “more” s is what you are doing in the article. This takes a good scientist to write, not just a good writer. A researcher’s eye sees the gap in the literature. This is where a good adviser comes into play. Use his/her eyes until you can use yours.

The Focal Loss paper gives a good example of this. The field is deep learning, and the specific problem is object detection. I recommend reading the entire related work. However, for brevity, consider just the third and fourth paragraphs, given bellow:

The paragraph begins enumerating the seminal one-stage detection architectures. Then, in yellow, it states the “gap”: one-stage detectors are faster but less accurate than two-stage approaches. In contrast, their work investigates: is it possible to be as accurate while retaining the speed?. Finally, they emphasize that their approach is based on a novel loss, not an innovative architecture. In sum, we have been presented the methods, their common issue, and how the authors addressed it.

It doesn’t hurt to mention how the microstructure of these paragraphs is outstanding and how it constantly poses arguments and ties them together. The first paragraph is special, as it even has a mid-paragraph resolution. There is no limit to how micro you can get.

#5: The Reordering Game

For each paragraph, consider all of its sentences independently. Can you reorder them without compromising the meaning? If almost no scrambling is possible, you have a clear and growing argument. Else, you might be just throwing one argument after another, until the last sentence comes to round up everything. Sentence and paragraph order matters; some orders are more effective at delivering a message than others.

On the Focal Loss example, put the first highlighted sentence just before the second and read it all. The message is more or less retained. However, this created a long stream of facts about detectors. Putting that sentence in the middle gives the readers a pause to breathe and to understand why these facts were given, which will ease digesting the next set of facts.

Consider the tip #4 text, second, third, and fourth paragraphs. The first two cover what a weak/strong related work is and the third gives a strong example. The second and third could be swapped without changing the meaning. However, we would be talking about weak related work and go to a strong example. This is sub-optimal. If you are giving a strong example, put it near the definition of what strong means.

This whole article is made of ten individual tips that could be said in any order. However, some orders place similar ideas closer and distinct ideas apart. In many cases, much can be improved by just aligning ideas properly. If you will compare weak with strong, start with the weak. If you will talk about A and B, and give an example of A, put B first. Ideas should always be building up. Write towards a climax.

#6: Do Not Repeat Yourself

Another common mistake is to repeat concepts said in previous sections to “remind the reader.” Do not do this. Write for readers that pay attention. Do not cue the reader as if he had just forgotten what you said.

If you do need to recall previous ideas, consider (1) naming them or, (2) assigning numbers. An example of (1) is Freud’s concepts of ego, super-ego, and id. In his theories, he extensively relied on these names on many occasions, instead of restating their definitions all the time. An example of (2) is given here and now, using the numbers in parenthesis. No need to repeat.

Keep in mind, however, that nothing said in the abstract should be relied upon on the text. Consider the abstract as not being part of the manuscript at all. Repeat anything you need at the introduction.

#7: Describe the Problem, not the Field

In most papers, you are within an ample field, and you are targeting a specific issue. For instance, you might be studying children’s development and focusing on their performance on puzzles. You can expect familiarity with the field, but not as much for the problem.

In more detail, “Children Development” is the field. You may describe it, but assume your readers are familiar. “Performance on Puzzles” is a specific problem. Explain this thoroughly; do not expect your readers to be familiar with this or to be aware of the typical puzzles used to assess development in kids. For instance, consider the Focal Loss paper once again. It does not try to overview the field: what neural networks are. Instead, it outlines the problem of object detection.

#8: Papers Are Not Tiny Books

Book authors expect you to read it front to back. No section skipping. Articles, on the other hand, are usually read in crazy ways. Of all parts, the “body” is the less often read. As a rule of thumb, your text should be ready for readers that jump from any section to any other section.

This happens all the time. After a year on a specific problem, you may have a set of “favorite papers,” in which you regularly consult the “body” and “results” section only. Whenever a new paper on the topic arises, you go instantly to the conclusions to see what it did. On new problems, we quickly skim hundreds of titles, of which some we read the abstract, and so on.

Imagine a master student who has two days to judge 20 papers as relevant to some question. This smart kid won’t read any of them thoroughly. He will read the 20 abstracts and filter his pile to 12 papers. Then, he will read 12 introductions, and, for some of them, he might read the conclusions, arriving at eight papers. Job’s done. Later on, a Ph.D. receives the eight documents and reads their abstracts. Two of them he saved to read entirely next week, the other six he skimmed the “body” section to get a feeling for it and selected one more to understand fully. So far, no one followed the actual order of any paper.

Edit: I just read an article that illustrates my point. The author calls it “three pass approach”. Literature reviews are done in passes, and they purposely focus on articles non-linearly. Make sure your paper is ready to be read like that.

#9: Good Writers Borrow; Great Writers Steal

Sometimes you are in the writer’s block. Nothing is coming out, but the deadlines are coming closer. Writing is hard; filling is easier. Find a paper you like, from another topic, then color-code the sentences or paragraphs of the section you are in right now, same as before with the other tips. Now, your task is just to fill these colors with your own content. You stole the structure, not the text.

Consider the following text. It steals the structure from the color-coded paragraph used in tip #2 and sets it in a totally different field.

It is valid to consider if this could be seen as copyright infringement or some form of plagiarism. However, this is not the case. This idea is not new and certainly is not rare. This is a standard procedure and an authentic way of getting the juices flowing. Most of the time, you only need the first paragraph to get going, then it is all yours.

#10: A Table is Worth a Thousand Plots

Plots are nice; plots are beautiful. Include as many as you can. However, keep in mind that you should have at least one table that sums up the data on most of the plots. Tables are the truth. They allow reviewers to do their own visualizations, to bypass log-scales, to compare your results to others, etc. Feel free to use several tables or a single master one, as long as there is a numerical version of your main results available to readers.

Consider the Focal Loss paper once more. Data science is full of experiments, and each experiment yields a ton of data. It is hard to judge whether a technique is really good by just reading about it and seeing some plots. Now, take a look at table 1:

Given the table, you might still object to the work or question the proposed loss function. However, one thing you cannot deny: tests were made. Tables show the true amount of effort that has been put to prove the effectiveness of the proposed method. The saying “a picture is worth a thousand words” does not generalize to plotting: no set of plots can ever make for a good table.