Friday scribbles: more abstract ideas

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abstract, digital, ideas, video

This week, I wanted to draw your attention to an article in University Affairs that looks at the “video abstract”.

“We see younger researchers using video abstracts to scan literature quickly,” explains Cameron Macdonald, executive director of the Ottawa-based publisher Canadian Science Publishing (formerly NRC Research Press). The press has launched a video abstract option for authors who are publishing in its 15 journals.

If you read my post about graphical abstracts, you can probably guess where I stand on this issue: if you’re going to make a video abstract, make sure it doesn’t look like a poorly-dubbed high school science project.

 

Here’s an example of a researcher reading a script from a screen in front of a webcam.  It’s generally not engaging, there’s a humming in the background, the lighting is poor, the video is cropped, and it’s obvious she’s reading.

 

Here’s a good example – it’s produced by the university, the researcher is wearing a mic (on her right lapel), and is actually engaging with the audience rather than reading.

I should note that I took both of these from Canadian Science Publishing’s website.

These videos can be great promotional tools for universities and labs, but like graphical abstracts (and websites, which I argued years ago in University Affairs), you’ve got to spend the time to make them look professional.

Friday scribbles: an abstract abstract idea

Tags

abstract, digital, graphics

After yesterday’s post on abstracts, a colleague asked what I thought of “graphical abstracts“.  I’m sure we’ve all seen them – a single image that supposedly captures the essence of the paper that seems to be pushed by Elsevier, among other publishers.

Please. Don’t. Do. It.

I’m all for “TOC art” (the journal “Ecology and Evolution” tends to have a photograph accompany each article), but when it comes to graphical design, scientists leave much to be desired

Actual TOC art for an article in the ACS journal “Organometallics”. Click the image to read the actual paper.

First of all, it’s embarrassing.  Journal articles are, for better or worse, a significant part of how academics are judged by their peers.  Including a kitschy graphical abstract does nothing else but highlight the authors inadequacies in graphical design.  Which is not surprising since most scientists aren’t designers.

And, like it or not, aesthetics plays a major role in how journals themselves are seen.  If you read a bunch of journal articles, you’ll find that after a while, you can figure out which journal an article is published in just from the layout (typeface, kerning, organization, and general layout).  And whether we recognize it or not, we have a subconscious reaction to this design. It’s akin to consumers’ perceptions of a given brand because, let’s face it, journals are brands.  And when a brand “looks ugly”, I think that, at least subconsciously, our perceptions of the content.

More and more ecologists are becoming decent amateur photographers, especially since the price of high-resolution digital single-lens reflex cameras has dropped considerably, and the cost of taking many photographs is essentially zero (except computer storage space).  This has resulted in more scientist-produced images being used as journal covers or TOC art, and is generally a good thing.  The images are of high quality, and meet certain criteria.

Until more scientists learn to use Adobe Illustrator or understand the principles of graphic design, for the love of Darwin, leave the graphics to the graphics experts.

Should I include TOC art? Let’s think about it for a minute. [click to link to the article]

Friday Scribbles: abstract ideas

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abstract, digital, inverse pyramid, key words, writing

While I’m busy in the lab most days this week, I thought it would be a good time to talk about things that are supposed to save us time when reading papers – the abstract.  If you recall in my last Friday Scribbles, I usually write the abstract last.  This is exactly the opposite way my PhD supervisor suggested, but I think I have some good reasons for waiting until the end.

In “ye olden days of yore” before electronic Table of Contents (eTOC) alerts, Web of Knowledge, or Google Scholar (and also when I started my undergrad degree, so just over 10 years ago), the main way academics found new papers in their field was by 1) subscribing to relevant journals personally or browsing new issues in the Library, or 2) consulting Biological Abstracts.  Biological Abstracts is (it still exists!) a regular publication that, as the title may suggest, prints the abstracts of indexed biological journals.  These abstracts were categorized, so I might check out the ecology or zoology sections.  And essentially, I would browse the tome (hundreds or thousands of pages published each month) looking for any papers of interest, or use an annual index for certain topics.  Biological Abstracts is online now, and integrated into Web of Knowledge.

But in this pre-digital age, the abstract was incredibly important since it could make or break whether an academic tracked down and read the whole article.  Nowadays, the abstract isn’t even sent in eTOC alerts for many journals (e.g., those published online at BioOne, and some titles published by Wiley or Springer) – just the title and authors (and sometimes the page range).  But abstracts are still an important part of papers.  So how do I write them?

In the 3rd year of my undergrad degree, I took a course called “Theoretical and Evolutionary Ecology”.  This was my first experience writing an abstract.  Our prof gave us a photocopy of a complete paper, but the abstract was removed.  We had one week to write an abstract for the paper (on egg size vs. number in trout, if I recall).  We then turned these in, and with our graded abstract, she provided the actual abstract of the paper.  I think this is a great way to get students writing abstracts, and to introduce them to critical reading of the primary literature.

One of my papers with the abstract removed.

Abstracts at most journals have one thing in common: they have a maximum length.  This can range from 100-300 words depending in the journal or contribution type (short communication vs. full paper vs. review).  One of the reasons I like to write the abstract last is because by that time I have a pretty good idea of where I want to submit the paper.  Some journals (e.g., those like Journal of Animal Ecology published by the British Ecological Society) also have specific formats and requirements for their abstracts.  I’ll focus on the more common “free-form” type of abstract between 250-300 words.

I generally break the abstract into the same sections as my paper:

• Introduction: 2-3 sentences
• Methods: 1-2 sentences
• Results: 2-3 sentences
• Discussion: 2-4 sentences

I usually start my papers’ introduction with a broad idea, theory, or concept, so this usually forms the first sentence of my abstract.  The next two focus in on my problem, and state my actual objective.  I don’t think I can emphasize this last point enough.

The description of methods is brief, but sufficient to tell the reader what I did: “I measured floor tiles in 23 academic and 25 government buildings in April 2012”.  Unless my analysis is novel, or atypical, I generally leave out anything about the statistics since these details will interest only a small number of readers.

It’s nice to include some actual numbers in the abstract: “Government floor tiles (0.04 ± 0.01 m2) were significantly smaller than academic floor tiles (0.06 ± 0.01 m2)”.  Notice that I didn’t put in any p-values, F-ratios, AIC weights, etc.  Remember – this is supposed to be the “take home message”.

The last part of the abstract puts my results in the context of the introduction, and I usually wrap up with a concluding sentence.

Lastly, some journals also request between 4-10 key words.  These should be chosen to maximize the searchable terms so that others will find your paper.  If you include a species name in the title, there’s no need to include it in the key words since most searches are for terms in the title and key words – don’t use up a valuable key word repeating what’s already in the title.  Use higher taxonomic groups or English common names (if not in the title), geographic areas/study sites, or the major concept in the first part of the introduction.  They key (get it?) is to have your paper appear in search results for people writing papers that would cite your work.

The perils of free software

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digital, R, stable isotopes, tools

Despite my computer science colleagues complaining about how awkward, bloated, and annoying it is, most ecologists who dabble in command-line programming now use R, and it’s easy to see why.  It’s freely available, has a multitude of statistical and graphical packages, and though the initial learning curve is steep, it can be an invaluable learning experience.  When most statistical or graphing packages cost hundreds of dollars, this free alternative is an obvious choice.

For those not familiar with R, it’s a basic command-line program (like FORTRAN, BASIC, C++, etc.) that performs mathematical analyses and produced graphics.  The main way R accomplishes this is by using packages, which are subroutines designed for a specific task.  One of the most commonly used packages is called “MASS” (for “Modern Applied Statistics with S” by Venables and Ripley; S is another programming language).  This is where one can find the routines to run analysis of variance and other linear models, for example.

While an ANOVA is fairly simple (heck, I calculated ANOVA F-ratios by hand in undergrad, as I suspect many of us did), many packages perform more complex analyses.  This is what makes them so useful to “end users” who aren’t computer programmers – all we need is to format the data properly, and select a few options in the package, and it does the math for us.  Here’s an example:

model <- glm(RS ~ Island, data=crau, family="binomial")
summary(model)

This is an analysis of reproductive success of Crested Auklets, testing for differences among islands.  Since reproductive success is a binomial response (the pair did or did not raise a chick successfully), it’s a generalized linear model with binomial error.  Simple!  Nowhere did I have to code in how to calculate the result, the package MASS does it for me as long as I give it the appropriate input.

But there’s a downside to this crowdsourcing of computer code – not all of it is up to the same standard.  I work a fair bit with stable isotopes in ecology.  These are non-decaying isotopes of carbon and nitrogen that can be used to get information on foraging.  When we plot δ¹³C vs. δ¹⁵N (an “isotope biplot”), we can conceptualize it as analogous to the trophic niche concept, where δ¹⁵N relates to trophic position, and δ¹³C to foraging location.  Pretty cool stuff.

In 2011, a group in the UK basically put a number on the size of this axis of Hutchinson’s “n-dimensional hypervolume” by calculating the standard ellipse (a 2D analogy of SD).  Great!  Now we can compare niche sizes among species/groups/sites.  In their paper, they outlined 3 methods for calculating the standard ellipse area (SEA):

• Just figuring out the SEA
• Adjusting for small sample size, like we do in AIC analysis (SEAc)
• A Bayesian approach that produces a median and credibility intervals (the Bayesian equivalent of confidence intervals).

I like the 3^rd option, since it also includes some estimate of error.  In theory, all 3 numbers should be close.  But when I ran the analysis for a community of birds I worked on in my MSc, I noticed a problem: the estimate of SEAc didn’t fall within the 95% credibility interval of the Bayesian estimate.  So which is correct?  Because the mathematics behind the calculations is beyond me (having only completed 2 semesters of calculus in undergrad), I have to rely on the package’s author(s).

But herein lies the problem: while software companies have a dedicated staff to troubleshooting issues like this, most contributors of R packages have other jobs (professors, government researchers, or full-time graduate students in most cases).  If the author of the package can even be found*, chances are they’ve moved on to some other research project, or are teaching courses, and have little time to go back and mess around with code they wrote 1, 2, 3, or more years ago.  The result is a population of frustrated users, or in a worst-case scenario, users who don’t even know there’s a problem because they don’t possess the specific knowledge to evaluate the package critically.  As ecologists, many of us rely on our software to perform properly, and take for granted that it does.  Very few of us have the technical know-how to fix it if/when we find a problem.

So what’s the solution?  I don’t know of a practical one.  Besides the assumption that most people contributing R packages are of the open data-sharing ilk, and would feel a moral obligation to ensure their contribution works, what can be done?

 

*I thought I recalled a paper on the difficulty in contacting corresponding authors successfully by e-mail, but can’t seem to find it. If you know it, leave a note in the comments.