Andrew Reid PhD

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The business of academic writing
Published on 2016-02-04
by Andrew Reid
#4

Philosophical Transactions has the distinction of being the first periodical scientific publication, as we currently understand the concept — dating back to 1665. It was initially a private affair, organized under the auspices of the Royal Society of London, a collection of prominent researchers who gathered in meetings which are precursors to our modern conventions. Transactions was curated and financed by Henry Oldenburg, who also pocketed the proceeds, meagre though they were. This pioneering venture constituted the framework on which our modern academic publishing system is built. Unlike those early efforts, however, journal publication has grown into a lucrative industry, with all the advantages and pitfalls that this entails.

I want to wander a bit through this system, because I believe it to be a fundamental driver of our current scientific process, for better or for worse.

What do I mean by that? First off, let's have a look at the evolution of publishing houses since Oldenburg's day. As discussed in more detail here, the scientific journal as we know it came about in the 19th century, when well-known titles like Science and Nature were established. These were private ventures, and thus required profitable business models; consequently, the cost of printing, editing, and shipping journals had to be offset by revenue from subscription fees.

Interestingly, these early for-profit publishers did not commonly utilize a peer review process, in which other outstanding researchers in the field are invited to provide their opinion of an article manuscript with respect to quality, accuracy, and consistency (discussed in detail here). While it was not unheard of for an editor to seek an external review, typically a manuscript was assessed by the editor alone. According to the linked article, even Einstein, who published roughly 300 articles in his prolific career, was only subjected to peer review on one occasion. Today, of course, peer review is a de facto standard of the scientific process, and one around which proponents of traditional publishing practices most often rally. It did not begin to attain this status, however, until the end of the Second World War.

Another prominent aspect of the modern scientific article is its perceived impact on future scholarly activity. For various reasons — including the vast scope of scientific effort, ranging from the highly specific to the very general, the microscopic to the macroscopic, and everything in between — scientific impact is a concept that is notoriously difficult to quantify, although not through lack of effort. At present, there is a plethora of proposed metrics which target journals or individual articles. The simplest and most utilized metric, however, remains the impact factor, which constitutes a sort of base currency for scientific research. A journal's impact factor is, quite simply, the number of articles it publishes, divided by the number of times those articles are cited — resulting in a metric which reflects the average citation per article.

I use the term currency intentionally, because it provides a segway into the topic I really want to address: the business of scientific publishing. The traditional business model of a scientific publishing firm went something as follows. For a given journal, overhead expenses consisted of: printing costs (extra costs for colour plates); salaries for editorial, type-setting, and administrative staff; marketing costs; and delivery costs. Profits were derived through subscription fees and advertisements. This made complete sense in a world where all media was physical media, and it still made sense when I was a M.Sc. student wandering an immense and mystifying library to find and photocopy the articles I was interested in (for the record, I'm not ancient — I did have access to Medline, but this was all still new on the scene).

It does not make sense today. I cannot recall the last time I've had a physical periodical in my hands, or even for that matter visited a physical library. If I want an article, I access it online and download and/or print it out. This has indeed become the standard of scientific communication in our modern era, but while the major publishers have adapted in the sense that they without exception provide web-based platforms for disseminating articles, their business models appear to be stuck in the 1960s. For the modern publisher, overhead no longer includes printing or delivery costs, but rather the substantially smaller (and fixed) expense of maintaining a web server. Moreover, a large part of the effort that goes into the peer review process — that of managing or reviewing articles — is work done by highly qualified but unpaid academics. As far as I can see, only the administrative, type-setting, and high-level editing work constitutes additional overhead. And marketing, of course.

Despite this, the subscription fees typically applied to journals do not seem to have been adjusted accordingly. Many journals (without naming names) still charge enormous fees for colour prints, as if authors had any interest in physically printing their articles. The library of my alma mater, Memorial University of Newfoundland, recently announced its decision to cancel subscriptions for 4000 journals, for which it was paying $1.4 million CAD. Indeed, the Canadian Association of Research Libraries recently reported that journal costs have actually increased 25% over the previous four years, estimating that Canadian universities collectively spent $250M in 2015 to access online articles. To quote that site:

Recent research shows that journal prices are much higher than the true cost of publishing. The top five publishers, who control over 50% of the market and above 70% in some disciplines have profit margins in the order of 28-38.9% in their companies' scholarly journal divisions, bringing them in close proximity to pharmaceuticals industry leader Pfizer (42%), and vastly outpacing corporate giants such as Disney (14%) or Toyota (7%). Meanwhile, researchers are finding that open access publishing costs are far lower costs for publishing research. It is becoming increasingly clear that the subscription-based publishing system is simply unsustainable.

Wow. Scientific publishing is not only an industry, but it is a very lucrative one. Not bad for an enterprise which does little to actually produce their product, beyond sending emails and paying someone to typeset (and even here, using the proper Latex template, one can pretty much do this on their own). And marketing, of course.

How, you may reasonably ask, have they been allowed to continue milking this cash cow? A fine question. I think it lies mostly in the way academia is currently organized. Namely, universities geared to produce an overabundance of advanced degrees, creating a whole lot of junior researchers trying to find their path in science while at the same time distinguish themselves from their numerous peers. Increasingly, the means by which one distinguishes oneself (i.e., to a funding or hiring committee) is through the publication of articles in high-ranking journals. The impact of one's articles actually takes second fiddle to the impact of the journal in which they are published. Here again, we come back to the impact factor, which basically defines how highly a journal ranks. Journals with high impact factors (typically traditional journals such as Science, Nature, Cell, New England Journal of Medicine, etc.) are clearly in high demand, and as such, their publishers have the luxury of setting high fees. In addition, publishers (emulating cable providers) tend to bundle their high-demand titles with lots of lower-demand ones that few serious scientists have an interest in, jacking the prices for these as well.

This business arrangement also has an impact on the nature of the scientific process itself. The types of research lines that are typically high impact are also of a more general nature, since they appeal to a wider audience of researchers. However, in the field of science, general observations are almost always built on a body of evidence derived from narrower, specific research lines; as well as articles with a more technical or methodological focus. Since such approaches are lower impact, they are less attractive to young scientists trying to distinguish themselves. This leads to the tendency for research articles to focus on general interest topics (e.g., the relationship of functional connectivity to some cognitive or disease state), without rigorously testing the assumptions that are necessary to interpret such results. The focus on impact factor thus risks sacrificing detail for popularity. While excellent research will happen regardless, it is important to note the ways in which our reward systems can influence our priorities.

One alternative that is becoming increasingly popular is the open access model. Here, publishers eliminate subscription fees and make articles available to everyone, replacing these instead with submission fees, to be paid by the researcher. Open access fees are typically in the $thousands, and still constitute a substantial cost for research institutions. As the fee varies drastically depending on the journal, there is clearly still a high profit margin to be gained from this model. Another annoyance with the open source model is a new breed of start-up journals with dubious names, editorial boards, and review processes. Because this form of publishing has little overhead, it has become a new form of spam; few days go by when I do not encounter a handful of emails soliciting papers or offering me editorial positions.

As a closing note, I'd like to point out that not all hope is lost. On the contrary, in many countries academia is beginning to reassert itself, most conspicuously by means of a growing chorus of voices demanding open access publishing models and open data obligations. Dutch universities, for instance, have recently reached a deal with Elsevier, ensuring that 10% of output from Dutch universities will be published open access without additional cost to the researcher, a percentage which will grow by 10% annually. In the U.S., the NIH has stipulated that studies which it funds must be made open access via PubMed Central. The trend in many other countries is also going in this direction. New journals with modern philosophies, such as eLife and PeerJ, are cropping up, and promise to be a major headache for tradition publishing practices in the years to come. I know I plan to move in their direction as much as possible.

Comments here
Publishers of scientific articles have been slow to adapt their business models to the rapid evolution of scientific communication — mostly because there is profit in dragging their feet. I explore the past, present, and future of this important issue.
Tags:Journals · Articles · Impact factor · Citations · Business · Musings
journals,articles,impact factor,citations,business,Musings
Causal discovery: An introduction
Published on 2024-09-23
by Andrew Reid
#21
This post continues my exploration of causal inference, focusing on the type of problem an empirical researcher is most familiar with: where the underlying causal model is not known. In this case, the model must be discovered. I use some Python code to introduce the PC algorithm, one of the original and most popular approaches to causal discovery. I also discuss its assumptions and limitations, and briefly outline some more recent approaches. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · Causality · Causal inference · Causal discovery · Graph theory · Teaching
stats,causality,causal inference,causal discovery,graph theory,Teaching
Causal inference: An introduction
Published on 2023-07-17
by Andrew Reid
#20
Hammer about to hit a nail, representing a causal event.
In this post, I attempt (as a non-expert enthusiast) to provide a gentle introduction to the central concepts underlying causal inference. What is causal inference and why do we need it? How can we represent our causal reasoning in graphical form, and how does this enable us to apply graph theory to simplify our calculations? How do we deal with unobserved confounders? This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · Causality · Causal inference · Graph theory · Teaching
stats,causality,causal inference,graph theory,Teaching
Multiple linear regression: short videos
Published on 2022-08-10
by Andrew Reid
#19
In a previous series of posts, I discussed simple and multiple linear regression (MLR) approaches, with the aid of interactive 2D and 3D plots and a bit of math. In this post, I am sharing a series of short videos aimed at psychology undergraduates, each explaining different aspects of MLR in more detail. The goal of these videos (which formed part of my second-year undergraduate module) is to give a little more depth to fundamental concepts that many students struggle with. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · Linear regression · Teaching
stats,linear regression,Teaching
Learning about multiple linear regression
Published on 2021-12-30
by Andrew Reid
#18
In this post, I explore multiple linear regression, generalizing from the simple two-variable case to three- and many-variable cases. This includes an interactive 3D plot of a regression plane and a discussion of statistical inference and overfitting. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · Linear regression · Teaching
stats,linear regression,Teaching
Learning about fMRI analysis
Published on 2021-06-24
by Andrew Reid
#17
In this post, I focus on the logic underlying statistical inference based on fMRI research designs. This consists of (1) modelling the hemodynamic response; (2) "first-level" within-subject analysis of time series; (3) "second-level" population inferences drawn from a random sample of participants; and (4) dealing with familywise error. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · FMRI · Hemodynamic response · Mixed-effects model · Random field theory · False discovery rate · Teaching
stats,fMRI,hemodynamic response,mixed-effects model,random field theory,false discovery rate,Teaching
Learning about simple linear regression
Published on 2021-03-25
by Andrew Reid
#16
In this post, I introduce the concept of simple linear regression, where we are evaluating the how well a linear model approximates a relationship between two variables of interest, and how to perform statistical inference on this model. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.
Tags:Stats · Linear regression · F distribution · Teaching
stats,linear regression,F distribution,Teaching
New preprint: Tract-specific statistics from diffusion MRI
Published on 2021-03-05
by Andrew Reid
#15
In our new preprint, we describe a novel methodology for (1) identifying the most probable "core" tract trajectory for two arbitrary brain regions, and (2) estimating tract-specific anisotropy (TSA) at all points along this trajectory. We describe the outcomes of regressing this TSA metric against participants' age and sex. Our hope is that this new method can serve as a complement to the popular TBSS approach, where researchers desire to investigate effects specific to a pre-established set of ROIs.
Tags:Diffusion-weighted imaging · Tractography · Connectivity · MRI · News
diffusion-weighted imaging,tractography,connectivity,MRI,News
Learning about correlation and partial correlation
Published on 2021-02-04
by Andrew Reid
#14
This is the first of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology. In this post, I will try to provide an intuitive explanation of (1) the Pearson correlation coefficient, (2) confounding, and (3) how partial correlations can be used to address confounding.
Tags:Stats · Linear regression · Correlation · Partial correlation · Teaching
stats,linear regression,correlation,partial correlation,Teaching
Linear regression: dealing with skewed data
Published on 2020-11-17
by Andrew Reid
#13
One important caveat when working with large datasets is that you can almost always produce a statistically significant result when performing a null hypothesis test. This is why it is even more critical to evaluate the effect size than the p value in such an analysis. It is equally important to consider the distribution of your data, and its implications for statistical inference. In this blog post, I use simulated data in order to explore this caveat more intuitively, focusing on a pre-print article that was recently featured on BBC.
Tags:Linear regression · Correlation · Skewness · Stats
linear regression,correlation,skewness,Stats
Functional connectivity as a causal concept
Published on 2019-10-14
by Andrew Reid
#12
In neuroscience, the conversation around the term "functional connectivity" can be confusing, largely due to the implicit notion that associations can map directly onto physical connections. In our recent Nature Neuroscience perspective piece, we propose the redefinition of this term as a causal inference, in order to refocus the conversation around how we investigate brain connectivity, and interpret the results of such investigations.
Tags:Connectivity · FMRI · Causality · Neuroscience · Musings
connectivity,FMRI,causality,neuroscience,Musings
Functional connectivity? But...
Published on 2017-07-26
by Andrew Reid
#11
Functional connectivity is a term originally coined to describe statistical dependence relationships between time series. But should such a relationship really be called connectivity? Functional correlations can easily arise from networks in the complete absence of physical connectivity (i.e., the classical axon/synapse projection we know from neurobiology). In this post I elaborate on recent conversations I've had regarding the use of correlations or partial correlations to infer the presence of connections, and their use in constructing graphs for topological analyses.
Tags:Connectivity · FMRI · Graph theory · Partial correlation · Stats
connectivity,fMRI,graph theory,partial correlation,Stats
Driving the Locus Coeruleus: A Presentation to Mobify
Published on 2017-07-17
by Andrew Reid
#10
How do we know when to learn, and when not to? Recently I presented my work to Vancouver-based Mobify, including the use of a driving simulation task to answer this question. They put it up on YouTube, so I thought I'd share.
Tags:Norepinephrine · Pupillometry · Mobify · Learning · Driving simulation · News
norepinephrine,pupillometry,Mobify,learning,driving simulation,News
Limitless: A neuroscientist's film review
Published on 2017-03-29
by Andrew Reid
#9
In the movie Limitless, Bradley Cooper stars as a down-and-out writer who happens across a superdrug that miraculously heightens his cognitive abilities, including memory recall, creativity, language acquisition, and action planning. It apparently also makes his eyes glow with an unnerving and implausible intensity. In this blog entry, I explore this intriguing possibility from a neuroscientific perspective.
Tags:Cognition · Pharmaceuticals · Limitless · Memory · Hippocampus · Musings
cognition,pharmaceuticals,limitless,memory,hippocampus,Musings
The quest for the human connectome: a progress report
Published on 2016-10-29
by Andrew Reid
#8
The term "connectome" was introduced in a seminal 2005 PNAS article, as a sort of analogy to the genome. However, unlike genomics, the methods available to study human connectomics remain poorly defined and difficult to interpret. In particular, the use of diffusion-weighted imaging approaches to estimate physical connectivity is fraught with inherent limitations, which are often overlooked in the quest to publish "connectivity" findings. Here, I provide a brief commentary on these issues, and highlight a number of ways neuroscience can proceed in light of them.
Tags:Connectivity · Diffusion-weighted imaging · Probabilistic tractography · Tract tracing · Musings
connectivity,diffusion-weighted imaging,probabilistic tractography,tract tracing,Musings
New Article: Seed-based multimodal comparison of connectivity estimates
Published on 2016-06-24
by Andrew Reid
#7
Our article proposing a threshold-free method for comparing seed-based connectivity estimates was recently accepted to Brain Structure & Function. We compared two structural covariance approaches (cortical thickness and voxel-based morphometry), and two functional ones (resting-state functional MRI and meta-analytic connectivity mapping, or MACM).
Tags:Multimodal · Connectivity · Structural covariance · Resting state · MACM · News
multimodal,connectivity,structural covariance,resting state,MACM,News
Four New ANIMA Studies
Published on 2016-03-18
by Andrew Reid
#6
Announcing four new submissions to the ANIMA database, which brings us to 30 studies and counting. Check them out if you get the time!
Tags:ANIMA · Neuroscience · Meta-analysis · ALE · News
ANIMA,neuroscience,meta-analysis,ALE,News
Exaptation: how evolution recycles neural mechanisms
Published on 2016-02-27
by Andrew Reid
#5
Exaptation refers to the tendency across evolution to recycle existing mechanisms for new and more complex functions. By analogy, this is likely how episodic memory — and indeed many of our higher level neural processes — evolved from more basic functions such as spatial navigation. Here I explore these ideas in light of the current evidence.
Tags:Hippocampus · Memory · Navigation · Exaptation · Musings
hippocampus,memory,navigation,exaptation,Musings
Reflections on multivariate analyses
Published on 2016-01-15
by Andrew Reid
#3
Machine learning approaches to neuroimaging analysis offer promising solutions to research questions in cognitive neuroscience. Here I reflect on recent interactions with the developers of the Nilearn project.
Tags:MVPA · Machine learning · Nilearn · Elastic net · Statistics · Stats
MVPA,machine learning,nilearn,elastic net,statistics,Stats
New ANIMA study: Hu et al. 2015
Published on 2016-01-11
by Andrew Reid
#2
Announcing a new submission to the ANIMA database: Hu et al., Neuroscience & Biobehavioral Reviews, 2015.
Tags:ANIMA · Neuroscience · Meta-analysis · ALE · Self · News
ANIMA,neuroscience,meta-analysis,ALE,self,News
Who Am I?
Published on 2016-01-10
by Andrew Reid
#1
Musings on who I am, where I came from, and where I'm going as a Neuroscientist.
Tags:Labels · Neuroscience · Cognition · Musings
labels,neuroscience,cognition,Musings