How many authors does it take to write an article?

The concept of ‘literary author’ emerged gradually during the eighteenth century, when only a small part of the population was literate, and a print run of 1000 was ‘large’. The concept solidified in the nineteenth century with the introduction of powered printing presses and the spread of the ability to read (Johns 2003). Scientific authorship is different from literary authorship (Biagioli 2003). This chapter is about twenty-first-century writers of articles that describe new scientific work or that review already published scientific work. For brevity I call them author, without qualification. There are no simple rules about authorship, and this chapter is no more than a basis for thought and discussion.

What is an author? The answer is not always obvious. Consider the following patterns.

The science of ecology has passed from simple description to complex experiments, analyses, and modelling in less than a century. The British Ecological Society started the Journal of Ecology in 1913. As Figure 4.1 shows, the journal has grown about fourfold in 90 years, with hiccoughs during the two World Wars. The Society has also initiated five other journals. The growth in the Journal of Ecology is nowadays controlled mainly by finance. (The graphs stop at 2000 because subsequent production changes make comparisons with earlier years too equivocal.)

This book grew out of a repeated need, as graduate supervisor, referee (reviewer), and editor, to comment about irritants such as:

• the sins of ambiguity, circumlocution, confusion, inconsistency, vagueness and verbosity;

• misuse (or distractingly poor use) of words such as content, decimate, impact, level, light intensity, paradigm, parameter, and ratio;

• quantitative matters such as equations that didn’t equate, ludicrous precision of numerical values, misleading bar charts, graphs with inaccurate axis labels, nonsensical ‘log scales’, and undefined error bars;

• statistics-related misuses such as the difference between standard deviation and standard error, statistical fishing expeditions, the meaning of a ‘P-value’, and confusion among correlation, regression, and functional relations;

• incomprehensible talks and unattractive, or even repellent, posters.

What follows includes my gleanings during four decades as a university biologist: the things I wish now that I had known when I started out. It is mostly about broadcasting for the first time the results of original work in the biosciences (the inelegant umbrella term ‘bioscience’ embraces all the disciplines that work on living things, including biochemistry, molecular biology, genetics, microbiology, biology, botany, zoology, anatomy, morphology, physiology, ecology, and applications in, for example, medicine, psychology, and soil science). This book is intended mainly for those beginning a career in bioscience research. If you are such an apprentice or journeyman then I hope that you learn from the mistakes I made at your age. Established bioscientists may also find things to interest them.

A piece of scientific work is not complete until the results have been written up and published. Planning and doing the work is often exciting; writing about it may seem less so but is just as necessary, and delivers its own satisfaction when you see the work in print.

Why bother to acquire skill in describing your work? First, as you have to produce reports then better to do it well than badly. Second, the number of scientific articles published in a year has been doubling roughly every decade or two, but the time that any one scientist can spend reading does not increase. If you write badly those who ought to know your work may not be willing to make the effort needed to understand it, and those who do try will suspect that someone who writes so badly may have been just as confused and incompetent in doing the work as they are in trying to report it.

Most scientific articles exist between the ephemeral and the eternal. If you write concisely and clearly, your readers may begin to look forward to reading about your work, your reputation will rise, and the useful lifetime of your article will lengthen. Good writing cannot convert bad science into good – you cannot make a silk purse out of a sow’s ear – but you can ensure that your work is read.

Here is the message again, for the last time: aim to write, speak, and illustrate clearly and concisely. Use conservative grammar, not to pander to the prejudices of grammarians and pedants but because you aim to avoid distractions for your readers, for the members of your audience, and for your visitors.

To communicate well is a useful skill. More than that though, it is a service to all those who work in hope in the anthill that is the city of science.

Writing style is recognisable by the choice and arrangement of words, and by the physical layout (‘format’) of the work. The style of an instruction manual is different from that of a novel or a play, and their styles differ from that used in a newspaper, in a legal document or in poetry. Scientific style is closely linked to the main objective of scientific writing: to convey information as clearly and simply as possible.Do this, and you may one day deserve the obituarist’s note on the Nobel Prize winner Professor Sir Bernard Katz, whose first language was not English, that “His prose was simple, straightforward and unpretentious … Every new entrant [to the field of neural physiology] should read his work from beginning to end.”

Style may be acquired by wide reading of the works of those generally held to be ‘good’ stylists, or by learning rules (and exceptions). Be aware, however, that many scientists and almost all editors hold strong views about style, and many - perhaps all - would disagree with at least some of what follows in this chapter.

Swift had a pithy view of good style: “proper words in proper places”. (He used ‘proper’ in its older sense of suitable or appropriate.) More helpful perhaps is the advice of Matthew Arnold: that the secret of good writing lies in having something to say and saying it clearly.

After the main text of your article there is a section, usually headed References, containing bibliographic details of those publications that you have used for support in the main text. Each entry contains information about items such as author(s), date of publication, title, name of publication, and pages, given in at least sufficient detail to allow a reader to locate the work. In the main text you point to a particular reference with a cue or key. The cue+reference is a citation, but the cue or key is itself commonly called a citation, and I follow that custom here. There are many styles of citation(cue) and reference, and each journal makes its own choice. In many journals the ‘Instructions to Authors’ are vague about the citation and reference system to be used, and about formatting details for both, but you can infer these from examples in recent issues of the journal or get a reference program to use the journal format template if it offers one.

Author−date system

Here is an example of ‘author−date’ citations, sometimes called the Harvard system, in one style using space and ‘,’ as separators; ‘and’ to link two authors; ‘et al.’ for three or more authors; and multiple citations ordered by date:

Interpreting P values

Suppose you have just finished the measurements in a simple experiment to compare the abundance of weeds on 1-m2 plots sprayed either with a weedkiller in solution or with the same volume of water alone. You adopt the null hypothesis: that the weedkiller has no effect, and make a suitable test of this hypothesis (t-test or U-test, as appropriate) obtaining the result P = 0.04. That is to say that, if the hypothesis is true, then in a large number of similar trials one would expect to have got mean values at least as different as these were in about 4% of trials. The statistics stops at this point. So what next?

Anything further is a judgement by you and your readers. A convention has grown that for routine work of no special importance, if P ≤ 0.05 then, as practical people we think we will make fastest progress by concluding provisionally that the hypothesis is false while accepting that on average 1 in 20 of these provisional conclusions will be incorrect. But the value 0.05 is arbitrary.

• If we had only four digits on each hand it seems likely that the value would be 3 / 82 (= 0.047 in base 10) rather than 5 / 102 = 0.050.

• If the experiments are important and cannot be repeated for some reason (expense, no more material, unavailable equipment) then you may argue that you are going to reject the hypothesis provisionally with P as high as 0.1 (for example).

• If your life depends on avoiding wrongly rejecting the hypothesis then you might be inclined to require that P be ≪ 0.000 000 1.

• And if your life depends on avoiding wrongly rejecting the hypothesis yet there is a reward of 1 000 000 €/£/$ for correctly rejecting it, then you might be inclined to gamble at P < 0.000 1 or even P < 0.001. This is the sort of subconscious judgement you make every time you get into a car: the weighing of advantages and risks of an adverse outcome.