A fairer way to rank a researcher’s relative citation performance?

23 04 2020

runningI do a lot of grant assessments for various funding agencies, including two years on the Royal Society of New Zealand’s Marsden Fund Panel (Ecology, Evolution, and Behaviour), and currently as an Australian Research Council College Expert (not to mention assessing a heap of other grant applications).

Sometimes this means I have to read hundreds of proposals made up of even more researchers, all of whom I’m meant to assess for their scientific performance over a short period of time (sometimes only within a few weeks). It’s a hard job, and I doubt very much that there’s a completely fair way to rank a researcher’s ‘performance’ quickly and efficiently.

It’s for this reason that I’ve tried to find ways to rank people in the most objective way possible. This of course does not discount reading a person’s full CV and profile, and certainly taking into consideration career breaks, opportunities, and other extenuating circumstances. But I’ve tended to do a first pass based primarily on citation indices, and then adjust those according to the extenuating circumstances.

But the ‘first pass’ part of the equation has always bothered me. We know that different fields have different rates of citation accumulation, that citations accumulate with age (including the much heralded h-index), and that there are gender (and other) biases in citations that aren’t easily corrected.

I’ve generally relied on the ‘m-index’, which is simply one’s h-index divided by the number of years one has been publishing. While this acts as a sort of age correction, it’s still unsatisfactory, essentially because I’ve noticed that it tends to penalise early career researchers in particular. I’ve tried to account for this by comparing people roughly within the same phase of career, but it’s still a subjective exercise.

I’ve recently been playing with an alternative that I think might be a way forward. Bear with me here, for it takes a bit of explaining. Read the rest of this entry »





Did people or climate kill off the megafauna? Actually, it was both

10 12 2019

When freshwater dried up, so did many megafauna species.
Centre of Excellence for Australian Biodiversity and Heritage, Author provided

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Earth is now firmly in the grips of its sixth “mass extinction event”, and it’s mainly our fault. But the modern era is definitely not the first time humans have been implicated in the extinction of a wide range of species.

In fact, starting about 60,000 years ago, many of the world’s largest animals disappeared forever. These “megafauna” were first lost in Sahul, the supercontinent formed by Australia and New Guinea during periods of low sea level.

The causes of these extinctions have been debated for decades. Possible culprits include climate change, hunting or habitat modification by the ancestors of Aboriginal people, or a combination of the two.


Read more: What is a ‘mass extinction’ and are we in one now?


The main way to investigate this question is to build timelines of major events: when species went extinct, when people arrived, and when the climate changed. This approach relies on using dated fossils from extinct species to estimate when they went extinct, and archaeological evidence to determine when people arrived.


Read more: An incredible journey: the first people to arrive in Australia came in large numbers, and on purpose


Comparing these timelines allows us to deduce the likely windows of coexistence between megafauna and people.

We can also compare this window of coexistence to long-term models of climate variation, to see whether the extinctions coincided with or shortly followed abrupt climate shifts.

Data drought

One problem with this approach is the scarcity of reliable data due to the extreme rarity of a dead animal being fossilised, and the low probability of archaeological evidence being preserved in Australia’s harsh conditions. Read the rest of this entry »





First Australians arrived in large groups using complex technologies

18 06 2019

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One of the most ancient peopling events of the great diaspora of anatomically modern humans out of Africa more than 50,000 years ago — human arrival in the great continent of Sahul (New Guinea, mainland Australia & Tasmania joined during periods of low sea level) — remains mysterious. The entry routes taken, whether migration was directed or accidental, and just how many people were needed to ensure population viability are shrouded by the mists of time. This prompted us to build stochastic, age-structured human population-dynamics models incorporating hunter-gatherer demographic rates and palaeoecological reconstructions of environmental carrying capacity to predict the founding population necessary to survive the initial peopling of late-Pleistocene Sahul.

As ecological modellers, we are often asked by other scientists to attempt to render the highly complex mechanisms of entire ecosystems tractable for virtual manipulation and hypothesis testing through the inevitable simplification that is ‘a model’. When we work with scientists studying long-since-disappeared ecosystems, the challenges multiply.

Add some multidisciplinary data and concepts into the mix, and the complexity can quickly escalate.

We do have, however, some powerful tools in our modelling toolbox, so as the Modelling Node for the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage (CABAH), our role is to link disparate fields like palaeontology, archaeology, geochronology, climatology, and genetics together with mathematical ‘glue’ to answer the big questions regarding Australia’s ancient past.

This is how we tackled one of these big questions: just how did the first anatomically modern Homo sapiens make it to the continent and survive?

At that time, Australia was part of the giant continent of Sahul that connected New Guinea, mainland Australia, and Tasmania at times of lower sea level. In fact, throughout most of last ~ 126,000 years (late Pleistocene and much of the Holocene), Sahul was the dominant landmass in the region (see this handy online tool for how the coastline of Sahul changed over this period).

Read the rest of this entry »





Legacy of human migration on the diversity of languages in the Americas

12 09 2018

quechua-foto-ale-glogsterThis might seem a little left-of-centre for CB.com subject matter, but hang in there, this does have some pretty important conservation implications.

In our quest to be as transdisciplinary as possible, I’ve team up with a few people outside my discipline to put together a PhD modelling project that could really help us understand how human colonisation shaped not only ancient ecosystems, but also our own ancient cultures.

Thanks largely to the efforts of Dr Frédérik Saltré here in the Global Ecology Laboratory, at Flinders University, and in collaboration with Dr Bastien Llamas (Australian Centre for Ancient DNA), Joshua Birchall (Museu Paraense Emílio Goeldi, Brazil), and Lars Fehren-Schmitz (University of California at Santa Cruz, USA), I think the student could break down a few disciplinary boundaries here and provide real insights into the causes and consequences of human expansion into novel environments.

Interested? See below for more details?

Languages are ‘documents of history’ and historical linguists have developed comparative methods to infer patterns of human prehistory and cultural evolution. The Americas present a more substantive diversity of indigenous language stock than any other continent; however, whether such a diversity arose from initial human migration pathways across the continent is still unknown, because the primary proxy used (i.e., archaeological evidence) to study modern human migration is both too incomplete and biased to inform any regional inference of colonisation trajectories. Read the rest of this entry »





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 »





Two new postdoctoral positions in ecological network & vegetation modelling announced

21 07 2017

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With the official start of the new ARC Centre of Excellence for Australian Biodiversity and Heritage (CABAH) in July, I am pleased to announce two new CABAH-funded postdoctoral positions (a.k.a. Research Associates) in my global ecology lab at Flinders University in Adelaide (Flinders Modelling Node).

One of these positions is a little different, and represents something of an experiment. The Research Associate in Palaeo-Vegetation Modelling is being restricted to women candidates; in other words, we’re only accepting applications from women for this one. In a quest to improve the gender balance in my lab and in universities in general, this is a step in the right direction.

The project itself is not overly prescribed, but we would like something along the following lines of inquiry: Read the rest of this entry »