Academic Data Writing Tips

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As an editor of an economics journal, I’m always intrigued by advice on what goes into writing a good academic paper.

Jon Zelner, Kelly Broen and Ella August tackle this piñata in “A guide to backback paper writing for the data sciences” (Grounds, January 2022). As usual with this kind of papers, some advice is valid but boring and very basic. But there are also ideas that resonated with me, and I will highlight them here.

From the intro:

Academic and applied research in data-intensive fields requires the development of a diverse skill set, of which writing and clear communication are among the most important. However, in our experience, the art of science communication is often overlooked in the formal training of data scientists in various fields spanning life, social, physical, mathematical, and medical sciences. Instead, clear communication is supposed to be learned by osmosis, through the practice of reading the work of others, writing our own work, and receiving feedback from mentors and colleagues.

It will not shock any reader of academic literature that this “learning by osmosis” is at best a partial success in producing clear writing and communication.

A well-designed data science paper is an educational tool that not only conveys information from the author to the reader, but facilitates understanding of complex concepts. It works both ways: the process of writing an article is an opportunity for writers to learn and clarify their understanding of the topic in addition to communicating it to someone else. If we can accept the idea of ​​this type of writing as a lesson, we can learn a lesson from research and practice in the field of educational development, in particular the backward approach to curriculum design, introduced by Williams and McTighe in their book Understand by design: “Our lessons, units and courses must be logically deduced from the [learning outcomes] researched, not derived from the methods, books and activities with which we are most comfortable. The curriculum should present the most effective ways to achieve specific outcomes…the best designs flow upstream from the learnings sought.

This rings true for me in several ways. When you write, you need to listen carefully to what you say, to better understand yourself. The great author Flannery O’Connor once wrote, “I don’t quite know what I’m thinking until I see what I’m saying. Writing down the results should help you understand yourself. Another part of the job is not just telling others what you have done, but teaching them. (Granted, all academics know the difference between telling and teaching, right?) Finally, the idea that the paper should emerge from the intended learning outcome, notthe methods, books and activities we are most comfortable withseems worth taking to heart. This approach is what the authors call a “retrospective design approach” to academic writing.

In a retrospective design approach, the overall goals of a course are first defined, then used to motivate and shape everything from the assignments students will complete, the nature and volume of reading material, and how class meetings will be used to move forward. towards these goals. In this way of thinking, a course has a standard set of components – homework, reading, class time – but the way they are designed and organized is organized around supporting the learning goals of the class. The same approach can be applied to building a research paper: even though most papers have the same sections (introduction, methods, results, discussion), early career researchers may underestimate the flexibility and room for creativity they have to use these components to achieve their scientific and professional development goals. The retrospective approach we outline here is to start at the end by answering the questions “What do I want to accomplish with this document?” and scaffold each piece to help achieve those goals. This contrasts with most ad hoc forward approach that most of us have learned to live with, in which we start with the introduction and struggle through to the conclusion with the main goal of just finishing the manuscript.

The article discusses the retrospective design approach in more detail. Finally, I enjoyed these thoughts on numbers and tables:

If it can be conveyed visually, do it! Prefer numbers to tables and descriptions in the text when possible. …. Reasonably informed readers should be able to figure out what’s going on by looking at your figure and reading the caption, even if they haven’t read the rest of the article. It’s not a hard and fast rule, but if you work on it, you’ll make sure the numbers convey as much information as possible. … If you must make a results table, keep it small and simple. Large, complex tables are where the reader’s attention will die. If the information is best conveyed by a table, be sure to include only the most essential information. When a table gets too big, it becomes easy to forget what it’s for. By keeping it short and cutting out superfluous information, you’re better able to stay focused on your message.

I fully intend to steal the phrase that “large, complex tables are where the reader’s attention will die” for future editorial comments.

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