Prioritising your academic tasks

18 04 2018

The following is an abridged version of one of the chapters in my recent book, The Effective Scientist, regarding how to prioritise your tasks in academia. For a more complete treatise of the issue, access the full book here.

splitting tasks

Splitting tasks. © René Campbell renecampbellart.com

How the hell do you balance all the requirements of an academic life in science? From actually doing the science, analysing the data, writing papers, reviewing, writing grants, to mentoring students — not to mention trying to have a modicum of a life outside of the lab — you can quickly end up feeling a little daunted. While there is no empirical formula that make you run your academic life efficiently all the time, I can offer a few suggestions that might make your life just a little less chaotic.

Priority 1: Revise articles submitted to high-ranked journals

Barring a family emergency, my top priority is always revising an article that has been sent back to me from a high-ranking journal for revisions. Spend the necessary time to complete the necessary revisions.

Priority 2: Revise articles submitted to lower-ranked journals

I could have lumped this priority with the previous, but I think it is necessary to distinguish the two should you find yourself in the fortunate position of having to do more than one revision at a time.

Priority 3: Experimentation and field work

Most of us need data before we can write papers, so this is high on my personal priority list. If field work is required, then obviously this will be your dominant preoccupation for sometimes extended periods. Many experiments can also be highly time-consuming, while others can be done in stages or run in the background while you complete other tasks.

Priority 4: Databasing

This one could be easily forgotten, but it is a task that can take up a disproportionate amount of your time if do not deliberately fit it into your schedule. Well-organised, abundantly meta-tagged, intuitive, and backed-up databases are essential for effective scientific analysis; good data are useless if you cannot find them or understand to what they refer. Read the rest of this entry »





The Effective Scientist

22 03 2018

final coverWhat is an effective scientist?

The more I have tried to answer this question, the more it has eluded me. Before I even venture an attempt, it is necessary to distinguish the more esoteric term ‘effective’ from the more pedestrian term ‘success’. Even ‘success’ can be defined and quantified in many different ways. Is the most successful scientist the one who publishes the most papers, gains the most citations, earns the most grant money, gives the most keynote addresses, lectures the most undergraduate students, supervises the most PhD students, appears on the most television shows, or the one whose results improves the most lives? The unfortunate and wholly unsatisfying answer to each of those components is ‘yes’, but neither is the answer restricted to the superlative of any one of those. What I mean here is that you need to do reasonably well (i.e., relative to your peers, at any rate) in most of these things if you want to be considered ‘successful’. The relative contribution of your performance in these components will vary from person to person, and from discipline to discipline, but most undeniably ‘successful’ scientists do well in many or most of these areas.

That’s the opening paragraph for my new book that has finally been release for sale today in the United Kingdom and Europe (the Australasian release is scheduled for 7 April, and 30 April for North America). Published by Cambridge University Press, The Effective ScientistA Handy Guide to a Successful Academic Career is the culmination of many years of work on all the things an academic scientist today needs to know, but was never taught formally.

Several people have asked me why I decided to write this book, so a little history of its genesis is in order. I suppose my over-arching drive was to create something that I sincerely wish had existed when I was a young scientist just starting out on the academic career path. I was focussed on learning my science, and didn’t necessarily have any formal instruction in all the other varied duties I’d eventually be expected to do well, from how to write papers efficiently, to how to review properly, how to manage my grant money, how to organise and store my data, how to run a lab smoothly, how to get the most out of a conference, how to deal with the media, to how to engage in social media effectively (even though the latter didn’t really exist yet at the time) — all of these so-called ‘extra-curricular’ activities associated with an academic career were things I would eventually just have to learn as I went along. I’m sure you’ll agree, there has to be a better way than just muddling through one’s career picking up haphazard experience. Read the rest of this entry »





A gender-diverse lab is a good lab

18 09 2017

sexism

Another little expurgated teaser from my upcoming book with Cambridge University Press.

My definition of a ‘lab’ is simply a group of people who do the science in question — and people are indeed a varied mob. I’d bet that most scientists do not necessarily give much thought to the diversity of the people in their lab, and instead probably focus more on obtaining the most qualified and cleverest people for the jobs that need doing. There are probably few of us who are overtly racist, sexist, or otherwise biased against or for certain types of people.

But the problem is not that scientists tend to exclude certain types of people deliberately based on negative stereotypes; rather, it concerns more the subconscious biases that might lurk within, and about which unfortunately most of us are blissfully unaware. But a scientist should be aware of, and seek to address, these hidden biases.

I acknowledge that as a man, I am stepping onto thin ice even to dare to discuss the thorny issue of gender inequality in science today, for it is a massive topic that many, far more qualified people are tackling. But being of the male flavour means that I have to, like an alcoholic, admit that I have a problem, and then take steps to resolve that problem.

Read the rest of this entry »





The sticky subject of article authorship

2 10 2015

CriticVs.Shakespeare-copyI have a few ‘rules’ (a.k.a. ‘guidelines’) in my lab about the authorship of articles, but I’ve come to realise that each article requires its own finessing each time authorship is in question. After a lengthy discussion yesterday with the members of Franck Courchamp‘s lab, I decided I should probably write down my thoughts on this, one of the stickiest of subjects in the business of science.

The following discussion can be divided into to two main categories: (1) who to include as a co-author, and once the list of co-authors has been determined, (2) in what order should they be listed?

Before launching into discussing the issues related to Category 1, it is prudent to declare that there are as probably as many conventions as there are publishing scientists, and each discipline’s most general conventions differ across the scientific spectrum. I’m sure if you asked 10 people about what they considered appropriate, you could conceivably receive 10 different answers.

That said, I do still think there are some good-behaviour guidelines on authorship that one should strive to follow, all of which are based on my own experiences (both good and awful).

So who to include? It seems like a simple question superficially because clearly if someone contributed to writing a peer-reviewed article, he/she should be listed as a co-author. The problem really doesn’t concern the main author (the person who did most of the actual composition) because it’s clear here who that will be in almost every case. In most circumstances, this also happens to be the lead author (but more on that below). The question should really apply then to those individuals whose effort was more modest in the production of the final paper.

Strictly speaking, an ‘author’ should write words; but how many words do they need to write before being included? Would 10 suffice, or at least 10%? You can see why this is in itself a sticky subject because there are no established or accepted thresholds. Of course, science generally requires much more than just writing words: there are for most papers experiments to design, grants to obtain to fund them, data to collect, analysis and modelling to be done, figures and tables to prepare and finally, words to write. I’ll admit that I’ve co-authored many papers where I’ve done mainly one of those things (analysis, data collection, etc.), but I can also hold my hand over my heart and state that I’ve contributed more than a good deal to the actual writing of the paper in all circumstances where I’ve been listed as a co-author (the amount of which depends entirely on the lead author’s writing capacity). Read the rest of this entry »