Contrary to what the title suggests, this book is not so concise since it discusses all the aspects of what is needed for successful communication in STEM disciplines. It is focussing on all kinds of traditional communication (papers, posters, lectures, reports, theses) but no books, blogs or vlogs. A remarkable feature is that practically everything that is discussed is illustrated with examples from real publications. These are inserted in a box with grey background and it is carefully indicated where the problem is or which lines or paragraphs illustrate how it is properly done. The good and the bad examples are easily distinguishable because the bad examples are marked with a fat X. Of course, indicating the source of all the examples requires a long list of references (737), but fortunately the reader does not have to look up all of them. The references about the issues on communication are conveniently separated from the others, but this list still contains 391 references to papers, books and websites, but again these are the sources used and one is not required to look them all up.
There are obviously general guidelines of how to organize any written or oral communication. For example, the use of proper English is one of them, and this chapter takes a substantial number of pages in this book. The author points to common errors, subtle differences in meaning (for example 'which' and 'that'), punctuation, gender neutral formulations (he or she), the use of 'I' or 'we', etc. But there are specific chapters for each of the forms of communication that were listed above, be it written or oral.
Extra chapters are devoted to mathematics, to statistics, and to graphs. The chapter on mathematics is relatively short. The guidelines are to take care of a logical structure, motivate and give insight, do not use a symbol or a notion before it has been defined, and on the technical side, use proper fonts, be consistent in notation, don't start a sentence with a symbol, and take care of punctuation. All of these are very recognizable errors.
Somewhat surprising to me was the chapter on data description and statistical inference. This is really a technical chapter about means, medians, deviation, quartiles, confidence intervals, data fitting, regression, trends, data smoothing, and statistical significance. Even (simple) matlab code is included in the notes at the end of the book to compute some of these. Perhaps this is not such a bad idea because it happens that statistical arguments are misused in some publications, even though it should be expected that authors in science and engineering should be trained in this subject.
Graphics form often a weak point of a publication. A proper caption should explain what is plotted, the axes should be labelled, have tick marks and units should be mentioned. The graphs should not be overloaded with too many different curves or data. If colours are used, a colour scale should be added, and perhaps confidence intervals are appropriate. Of course resolution needs to be high enough for publication, but that is usually required by the published, and it does not get published if the quality is not good enough.
More guidelines are provided to help you through a refereeing an publishing process once a paper is submitted to a journal. The role of impact factors and other ranking systems, and how you can promote your paper.
A separate chapter is devoted to ethical issues. What authors to list and in what order? What sanctions can be the consequence of plagiarism, or even self-plagiarism? Here, like in the rest of the book, the text refers to what is generally considered to be ethical or that the community considers to be the right way to behave. It is not the opinion of the author, nor is the text imperative on all topics.
Each chapter can easily be read separately. It is clearly spelled out in the introduction what is discussed and it always ends with a checklist of items to be verified before the work is finished. A PhD student who gives his first conference lecture or prepare his first poster may want to check the appropriate chapter. He or she will undoubtedly make errors but learn from them by experience. Similarly the supervisor will comment of first drafts of their paper and refer to the corresponding chapter or chapters. I do not think that giving a formal course on the topic is very helpful, if not experienced by the student.
Of course this is not the only book written on this topic. The list of the first 391 references in the book contains many examples, among which the text by Halmos and the book by Higham that I also mentioned at the end of my review of How to Write and Publish a Scientific Paper (B. Gastel and R.A. Day, 2017); the texts by Krantz and Tao given there are also not include in this book. Nevertheless, this book is a great asset for any PhD student or a fresh researcher in one of the STEM disciplines, and there should be a book like this on the (virtual) shelf of the library.