Resolutions for 2016

Having now been a postdoc for a couple of years, I think I’ve slowly developed more perspective about the day-to-day aspects of working as a researcher and the costs and benefits of various approaches. So this year, I am resolving to be proactive about the various challenges of academic life, and try some things meant to make my work life more productive and better: 

1) Carve out more time to read the literature. The busier I get, the more difficult it is to keep up with new papers that aren’t directly connected to my current projects. One of the best parts of being a grad student (without a heavy teaching load) was how much time I had to keep up with the literature. As my time is more scheduled and there are more concrete deadlines, it is harder to make time for activities like reading that don’t have an immediate pay off. I have a feed reader, but I find that I only check it monthly; I also come across interesting papers while doing lit reviews/etc and leave those open in my browser, planning to get to them eventually...

I know that braver souls than I are tackling this problem #365papers style, but I don’t think that’s what I want. Instead, I am scheduling three 1-hour slots per week, and I think that’s a manageable goal.

2) Continue to work on good project management practices (such as those described here). I use the suggestions for predictable directory structures, separation of code into different types of scripts and of course, version control, and find them very helpful. I wish that I had learnt best practices for coding and project management as a grad student, but it’s never too late.

3) Take vacations (and mean it!). Every academic I know who has good work-life balance takes vacations. That means not working—at all, including no responding to emails. This is one of the things I admire most about my European colleagues, and I look forward to enjoying the French holidays when I start a fellowship in Montpellier this spring :-)

4) Maintain relationships across distances. It can be difficult to connect with people during short postings here and there, and even harder to maintain those relationships after you move on to the next place. The tools are there (Skype, Facebook, Twitter, email, etc), so I shouldn’t forget to take advantage of them.

5) Learn a new skill. Still deciding what, given the many things I want to learn!

6) Emphasize the positive more often. In general, I think people (or at least me) can be overwhelmed by the negatives in academia – e.g. the rejections of manuscripts and applications, the difficulties in securing the next job, etc. Unlike in undergrad, we don’t get grades or quantitative measures of our success too often (and I haven’t gotten a sticker on my work in years). And when we do get praise, it is often informal (e.g. “good talk”, “I liked your paper”), or balanced with criticism (e.g. “accept but with major revisions”). This is all in pursuit of improvement, but it can be difficult to keep even constructive criticism in perspective because the brain is biased towards remembering the negative. So I’m considering keeping an explicit list of successes to help highlight the positive.

The hurdles and hardships of science in China

In my last post on China I discussed why China is becoming a scientific juggernaut. I focussed on all the things that seem to be working in its favour (funding, high expectations on scientists, etc.). While I do think that science in China is good and getting better, it is also important to point out some of the hurdles and limitations that hold back some aspects of scientific advance here.

In my previous post I noted that the expectations placed on students and researchers (i.e., to produce a minimum number of papers in journals with high impact factors, IFs) provided motivation to do good science. This is undoubtedly true, however, these strict expectations also reinforce a strategy of ‘paper-chasing’ where students are encourage to figure out how to get a paper. This is because the reward structure is so quantitative. While this type of evaluation systems has pros and cons, it does create a different sense of urgency than I’ve experienced elsewhere.

Pragmatic factors

The Great Fire Wall of China from "Cracks appear in the Great Fire Wall of China" posted by the China Daily Mail, Sep. 25th 2013.

I have never yelled at my computer or cursed the internet as much as I have in China. In the west we often hear about the ‘Great Firewall of China’ and probably do not think much about what this actually means. It sucks. The internet barely functions for significant proportions of the working day. I thought that this might have to do with the number of people and lack of infrastructure, but I no longer believe this to be true. Other countries in the region have great internet, and China has very advanced infrastructure. I’m pretty sure that when there is high traffic, the national security protocols and activity monitoring servers are the bottleneck.

Because the government policy is to block certain websites, most of the scientific internet websites and data sharing portals are not accessible here, but this may change at any given time. For example: Google Drive, Dropbox, Facebook, Blog sites, Twitter, Google Maps, and Google Scholar are all services routinely used by scientists and which are blocked in China. The reason for these to be blocked, as far as I understand it, is that they do not share users’ activities and the government cannot monitor what individuals share and download (which reinforces the value of these services to me). I also suspect that they are blocked to give local companies a chance to succeed without competition from global corporations, or perhaps simply because of disagreements with the companies.

I have had immense trouble trying to share files with my lab back in Canada (and to post this blog entry –which is why I’m doing it from Cambodia!). I am not currently engaging in social media –something that I saw as a legitimate activity for communicating science. I am having a very hard time searching for articles without Google Scholar. I also have trouble with other websites that should not be blocked, but that use third party encryption. For example, I can’t log in to my University library in Toronto, and I couldn’t connect my Canadian grant application to the Canadian Common CV (which we are required to do in Canada) because the CCV web interface was blocked (I had to get my post doc in Canada to do it for me). I have tried to go to researchers’ websites to find that they are blocked because they use a blogging site (e.g., Wordpress). The amount of time I spend doing basic online professional activities has increased 3 to 4-fold.

This is important because Chinese scientists are at a disadvantage when it comes to international collaboration and participating in online initiatives. I would encourage scientists outside of China to consider these imposed limitations to ensure that information and collaboration is barrier-free. Here are some tips:

1. Don’t link to your Google scholar publications on the publications page of your website
2. Don’t use a blog site to host your website (e.g., Wordpress)
3. Don’t use Dropbox or Google drive to collaborate on papers
4. Don’t use gmail as your work e-mail, Air China, for example, won’t send e-mails to gmail.
5. Social media has emerged as a great way to communicate with broader communities, it is important to recognize that these dialogues exclude Chinese scientists.
6. Ironically, as I write in this blog, blogs are blocked and while blogs provide a great platform to discuss ideas and issues, they are not available to Chinese scientists. 

These last two are interesting as journals increasingly require or request tweets or blog posts to help maximize exposure, but these forms of communication are not on scientists’ radar here.

Chinese science has been increasing by leaps and bounds despite these limitations. This is a testament to the hard work and dedication by Chinese scientists. I have no doubts that basic scientific research in China will continue to increase its stature and impact.

Postscript

One thing that is interesting to me is that many of the graduate students here use VPNs (Virtual Private Networks) to mask their IP addresses. They are able to access blogs, Google Scholar, etc. In conversations with people, VPN use is extremely widespread and successful at circumventing government filters, most of the time (there seems to be an arms race between the government and VPNs). It really makes me wonder how much longer these governmental controls can be realistically maintained.

Science in China –feeding the juggernaut*

For those of us involved in scientific research, especially those that edit journals, review manuscripts or read published papers, it is obvious that there has been a fundamental transformation in the scientific output coming from China. Both the number and quality of papers have drastically increased over the past 5-10 years. China is poised to become a global leader in not only scientific output, but also in the ideas, hypotheses and theories that shape modern scientific investigation.

I have been living in China for a couple of months now (and will be here for 7 months more), working in a laboratory at Sun Yat-sen University in Guangzhou, and I have been trying to identify the reasons for this shift in scientific culture in China. Moreover, I see evidence that China will soon be a science juggernaut (or already is), and there are clear reasons why this is. Here are some reasons why I believe that China has become a science leader, and there are lessons for other national systems.

The reasons for China’s science success:

1.      University culture.

China is a country with a long history of scholarly endeavours. We can look to the philosophical traditions of Confucius 2500 years ago as a prime example of the respect and admiration of scholarly traditions. Though modern universities are younger in China than elsewhere (the oldest being about 130 years old), China has invested heavily in building Universities throughout the country. In the mid-1990s, the government built 100 new universities in China, and now graduates more than 6 million students every year from undergraduate programs.

Confucius (551-479 BC), the grand-pappy of all Chinese scholars

This rapid increase in the number of universities means that many are very modern with state-of-the-art facilities. This availability of infrastructure has fostered the growth of new colleges, institutes and departments, meaning that new faculty and staff have been hired. Many departments that I have visited have large numbers of younger Assistant and Associate Professors, many having been trained elsewhere, that approach scientific problems with energy and new ideas.

My new digs

2.      Funding

From my conversations with various scientists, labs are typically very well funded. With the expansion of the number of universities seems to have been an expansion in funds available for research projects. Professors need to write a fair number of grant proposals to have all of their projects funded, but it seems that success rates are relatively high, and with larger grants available to more senior researchers. This is in stark contrast to other countries, where funding is inadequate. In the USA, National Science Foundation funding rates are often below 10% (only 1 in 10 proposals are funded). This abysmal funding rate means that good, well-trained researchers are either not able to realize their ideas or spend too much of their time applying for funding. In China, new researchers are given opportunities to succeed.

3.      Collaboration

Chinese researchers are very collaborative. There are several national level ecological research networks (e.g., dynamic forest plots) that involve researchers from many institutions, as well as international collaborative projects (e.g., BEF China). In my visits to different universities, Chinese researchers are very eager to discuss shared research interests and explore the potential for collaboration. Further, there are a number of funding schemes to get students, postdocs and junior Professors out of China and into foreign labs, which promotes international collaboration. Collaborations provide the creative capital for new ideas, and allow for larger, more expansive research projects.

4.      Environmental problems

It is safe to say that the environment in China has been greatly impacted by economic growth and development over the past 30 years. This degradation of the environment has made ecological science extremely relevant to the management of natural resources and dealing with contaminated soil, air and water. Ecological research appears to have a relatively high profile in China and is well supported by government funding and agencies.

5.      Laboratory culture

In my lab in Canada, I give my students a great deal of freedom to pursue their own ideas and allow them much latitude in how they do it. Some students say that they work best at night, others in spurts, and some just like to have four-day weekends every week. While Chinese students seem equally able to pursue their own ideas and interests, students tend to have more strict requirements about how they do their work. Students are often expected to be in the lab from 9-5 (at least) and often six days a week. This expectation is not seen as demanding or unreasonable (as it probably would be in the US or Canada), but rather in line with general expectations for success (see next point).

Labs are larger in China. The lab I work in has about 25 Masters students and a further 6 PhD students, plus postdocs and technicians. Further, labs typically have a head professor and several Assistant or Associate Professors. When everyone is there everyday, there is definitely a vibe and culture that emerges that is not possible if everyone is off doing their own thing.

The lab I'm working in -"the intellectual factory"

Another major difference is that there is a clear hierarchy of respect. Masters students are expected to respect and listen to PhD students, PhD students respect postdocs and so on up to the head professor. This respect is fundamental to interactions among people. As it has been described to me, the Professor is not like your friend, but more like a father that you should listen to.

What’s clear is that lab culture and expectations are built around the success of the individual people and the overall lab. And success is very important –see next point.

6.      Researcher/student expectations

I left the expectations on researchers for last because this needs a longer and more nuanced discussion. My own view of strict expectations has changed since coming to China, and I can now see the motivating effect these can have.

For Chinese researchers it is safe to say that publications are gold. Publishing papers, and especially the type of journal those papers appear determine career success in a direct way. A masters student is required to publish one paper, which could be in a local Chinese journal. A PhD student is required to publish two papers in international journals. PhD students who receive a 2-year fellowship to travel to foreign labs are required to publish a paper from that work as well. For researchers to get a professor position, they must have a certain number of publications in high-impact international journals (e.g., Impact Factor above 5).

Professors are not immune from these types of expectations. Junior professors are not tenured, nor are they able to get tenure until they qualify for the next tier, and they need to constantly publish. To get a permanent position as a full professor or group leader, they need to have a certain number of high impact papers. For funding applications, their publication records are quantified (number and impact factors of journals) and they must surpass some threshold.

Of course in any country, your publication record is the most important component for your success as a researcher, but in China the expectations are clearly stated.

While there are pros and cons of such a reward based system, and certainly the pressure can be overwhelming, I’ve witnessed the results of this system. Students are extremely motivated and have a clear idea what it means to be successful. To get two publications in a four year PhD requires a lot of focus and hard work; there is no time for drifting or procrastinating.

So why has Chinese science been so successful? It is because a number of factors have coalesced around and support a general high demand for success. Regardless of the number of institutional and funding resources available, this success is only truly realized because of researchers' desire to exceed strict expectations. And they are doing so wonderfully.  

*over the next several months I will write a series of posts on science and the environment in China

Scientists + Communication = ??

An academic is expected to be a jack of many trades – handling research, teaching, mentorship, administration, committee work, reviewing, grant-writing, and editorial duties. Science communication is increasingly being added to that list as well. Outreach, public engagement and science communication are all terms thrown around (e.g. the 'Broader Impacts' section of many NSF grants, for example, includes the possibility "Broaden dissemination to enhance scientific and technological understanding"). Sometimes this can include communication between academics (conferences, seminars, blogs like this one) but often it is meant to include communication with the general public. Statistics about low science literacy at least partially motivate this. For example, “Between 29% and 57% of Americans responded correctly to various questions measuring the concepts of scientific experiment and controlling variables. Only 12% of Americans responded correctly to all the questions on this topic, and nearly 20% did not respond correctly to any of them”. (http://www.nsf.gov/statistics/seind14/index.cfm/chapter-7/c7s2.htm).

Clearly improving scientific communication is a worthy goal. But at times it feels like it is a token addition to an application, one that is outsourced to scientists without providing the necessary resources or training. . This is a problem because if we truly value scientific communication, the focus should be on doing it in a thoughtful manner, rather than as an afterthought. I say this because firstly because communicating complex ideas, some of which may require specialized terms and background knowledge, is difficult. The IPCC summaries, meant to be accessible to lay readers were recently reported to be incredibly inaccessible to the average reader (and getting worse over time!). Their Flesch reading ease scores were lower than those of Einstein’s seminal papers, and certainly far lower than most popular science magazines. Expert academics, already stretched between many skills, may not always be the best communicators of their own work.

Secondly, even when done well, it should be recognized that the audience for much science communication is a minority of all media consumers – the ‘science attentive’ or ‘science enthusiast’ portion of the public. Popular approaches to communication are often preaching to the choir. And even within this group, there are topics that naturally draw more interest or are innately more accessible. Your stochastic models will inherently be more difficult to excite your grandmother about than your research on the extinction of a charismatic furry animal. Not every topic is going to be of interest to a general audience, or even a science-inclined audience, and that should be okay.

So what should our science communication goals be, as scientists and as a society? There is entire literature on this topic (the science of science communication, so to speak), and it provides insight into what works and what is needed. However, “....despite notable new directions, many communication efforts continue to be based on ad-hoc, intuition-driven approaches, paying little attention to several decades of interdisciplinary research on what makes for effective public engagement.”

One approach supported by this literature process follows 4 steps:

1) Identify the science most relevant to the decisions that people face;
2) Determine what people already know;
3) Design communications to fill the critical gaps (between what people know and need to know);
4) Evaluate the adequacy of those communications.

This approach inherently includes human values (what do people want or need to know), rather than a science-centric approach. In addition, to increase the science-enthusiast fraction of the public, focusing on education and communication for youth should be emphasized.

The good news is that science is respected, even when not always understood or communicated well. When asked to evaluate various professions, nearly 70% of Americans said that scientists “contribute a lot” to society (compared to 21% for business executives), and scientists typically are excited about interacting with the public. But it seems like a poor use of time and money to simply expect academics to become experts on science communication, without offering training and interdisciplinary relationships. So, for example, in the broader impacts section of a GRFP, maybe NSF should value taking part in a program (led by science communication experts) on how to communicate with the public; maybe more than giving a one-time talk to 30 high school students. Some institutions provide more resources to this end than others, but the collaborative and interdisciplinary nature of science communication should receive far more emphasis. And the science of science communication should be a focus – data-driven approaches are undeniably more valuable.

None of this is to say that you shouldn't keep perfecting your answer for when the person besides you on an airplane asks you what you do though :-) 

Post at Oikos + why do papers take so long?

This is mostly a shameless cross-post to a blog post I wrote for the Oikos blog. It's about an upcoming paper in Oikos that asks whether beta-diversity null deviation measures, which originated in papers like Chase 2010 and Chase et al. 2011, can be interpreted and applied as a measure of community assembly. These measures were originally used as null models for beta-diversity (i.e. to control for the effects of alpha diversity, etc), but increasingly in the literature they are used to indicate niche vs. neutral assembly processes. For anyone interested, the post is at the Oikos blog: http://www.oikosjournal.org/blog/v-diversity-metacommunities.

What I found most amusing, or sad, depending on your perspective was that I wrote a blog post about some of the original conversations I had with co-authors about this subject. I looked it up the other day and was shocked that the post was from 2013 (http://evol-eco.blogspot.com/2013/11/community-structure-what-are-we-missing.html). It's amazing how long the process of idea to final form actually takes. (No one phase took that long either - just idea + writing + coauthor edits + rewriting + submit + revise + coauthors + revise = long time...)

Science is a maze

If you want to truly understand how scientific progress works, I suggest fitting mathematical models to dynamical data (i.e. population or community time series) for a few days.

map for science?

You were probably told sometime early on about the map for science: the scientific method. It was probably displayed for your high school class as a tidy flowchart showing how a hypothetico-deductive approach allows scientists to solve problems. Scientists make observations about the natural world, gather data, and come up with a possible explanation or hypothesis. They then deduce the predictions that follow, and design experiments to test those predictions. If you falsify the predictions you then circle back and refine, alter, or eventually reject the hypothesis. Scientific progress arises from this process. Sure, you might adjust your hypothesis a few times, but progress is direct and straightforward. Scientists aren’t shown getting lost.

Then, once you actively do research, you realize that formulation-reformulation process dominates. But because for most applications the formulation-reformulation process is slow – that is, each component takes time (e.g. weeks or months to redo experiments and analyses and work through reviews) – you only go through that loop a few times. So you usually still feel like you are making progress and moving forward.

But if you want to remind yourself just how twisting and meandering science actually is, spend some time fitting dynamic models. Thanks to Ben Bolker’s indispensible book, this also comes with a map, which shows how closely the process of model fitting mirrors the scientific method. The modeller has some question they wish to address, and experimental or observational data they hope to use to answer it. By fitting or selecting the best model for they data, they can obtain estimates for different parameters and so hopefully test predictions from they hypothesis. Or so one naively imagines.

From Bolker's Ecological Models and Data in R,
a map for model selection. 

The reality, however, is much more byzantine. Captured well in Vellend (2010): 

“Consider the number of different models that can be constructed from the simple Lotka-Volterra formulation of interactions between two species, layering on realistic complexities one by one. First, there are at least three qualitatively distinct kinds of interaction (competition, predation, mutualism). For each of these we can have either an implicit accounting of basal resources (as in the Lotka-Volterra model) or we can add an explicit accounting in one particular way. That gives six different models so far. We can then add spatial heterogeneity or not (x2), temporal heterogeneity or not (x2), stochasticity or not (x2), immigration or not (x2), at least three kinds of functional relationship between species (e.g., predator functional responses, x3), age/size structure or not (x2), a third species or not (x2), and three ways the new species interacts with one of the existing species (x3 for the models with a third species). Having barely scratched the surface of potentially important factors, we have 2304 different models. Many of them would likely yield the same predictions, but after consolidation I suspect there still might be hundreds that differ in ecologically important ways.”

Model fitting/selection, can actually be (speaking for myself, at least) repetitive and frustrating and filled with wrong turns and dead ends. And because you can make so many loops between formulation and reformulation, and the time penalty is relatively low, you experience just how many possible paths forward there to be explored. It’s easy to get lost and forget which models you’ve already looked at, and keeping detailed notes/logs/version control is fundamental. And since time and money aren’t (as) limiting, it is hard to know/decide when to stop - no model is perfect. When it’s possible to so fully explore the path from question to data, you get to suffer through realizing just how complicated and uncertain that path actually is. 

What model fitting feels like?

Bolker hints at this (but without the angst):

“modeling is an iterative process. You may have answered your questions with a single pass through steps 1–5, but it is far more likely that estimating parameters and confidence limits will force you to redefine your models (changing their form or complexity or the ecological covariates they take into account) or even to redefine your original ecological questions.”

I bet there are other processes that have similar aspects of endless, frustrating ability to consider every possible connection between question and data (building a phylogenetic tree, designing a simulation?). And I think that is what science is like on a large temporal and spatial scale too. For any question or hypothesis, there are multiple labs contributing bits and pieces and manipulating slightly different combinations of variables, and pushing and pulling the direction of science back and forth, trying to find a path forward.

(As you may have guessed, I spent far too much time this summer fitting models…)

#ESA100 - EEB & Flow blogging 100 years of ESA

We haven't quite blogged 100 years of ESA (looks like we only started in 2009), but we'll definitely be blogging our daily highlights at the 100th annual meeting in Baltimore!

If you are attending, a few links from past years for those last minute preparations:
Presentations
Survival notes
Apparently this was my take-home in 2012

I think "postdoc trying to finish their talk" should be added to this...

Fear of missing out

There are so many tools, techniques, communication platforms and methods out there for ecologists that it can be hard to keep track of them all. Whether it be LaTeX, Linux, or Twitter, someone has probably suggested that your research life will improve if you use their favourite tool.

Online science has allowed fantastic networking and information exchange. However, one side effect of the increased usage of social media can be the feeling that you can’t keep up. The phrase ‘Fear of Missing Out’ in social psychology was coined to describe the anxiety arising from looking at social media and fearing that you have chosen how to spend our time poorly. But this can extend to academic working life too – that nagging fear that your colleagues are doing cooler things (going to more conferences, using more cutting edge techniques, publishing more papers) than you. It can be disconcerting to see that there’s a cool R package you know nothing about, or to receive yet another invite to a work-based social media platform you’ve never heard of.

There are people who are the early adopters and the technological trendsetters; people who derive their success from their methodological/technological skill sets. These people are fantastic, since they provide the examples for others in the field to follow. But for most researchers, it is practical to recognize that knowing every new technique and tool doesn’t promise success.

Graduate students may suffer this fear of falling behind most intensely, given how closely it relates to imposter syndrome and the general skills gap that grad students have to overcome. But I’d argue for most students, that balance is key. Otherwise it can be a slippery slope: maybe you learn R, then find that people recommend knitR and R Markdown, then you learn Shiny to show your results beautifully, then someone suggests things would be faster in a lower level language – and maybe months have gone by. That’s not necessarily bad, intellectual freedom and self-teaching are some of the best parts of graduate school, and if you are going to learn a bunch of new techniques, that is the perfect time. But aficionados will make arguments for everything from Python, Linux, Emacs (apparently the one true operating system), Github, to LaTeX/BibTeX, etc, etc. And every one of these is a useful tool, but maybe not always a necessary tool.

Researchers can never learn everything, and some things will fall out of fashion as quickly as they arose. Sure, it is less than ideal to be a researcher who hasn’t learned a new approach since the advent of SAS. But we need to balance necessity (is it fundamental to my research?), the time investment, and the utility of these skills for future endeavours (e.g. if you are applying for a data analyst job after grad school, learning a few languages might be smart - but if you will rarely use it again, perhaps it is not so important). The danger for many students is that either the time investment in learning a less-than-fundamental tool is very high, or else they learn a number of tools but never master any of them. This isn't to say you can shirk on quantitative skills - on the contrary, having those skills is far more important than mastering any specific program or language. I can think of fantastic ecologists who don’t know what Twitter is, who still use SAS, who have never learned R, and who produce brilliant work. The questions, the ideas, and the knowledge matter too, after all.

(*I should note that I'm a quantitative/theoretical ecologist, and I think every ecologist should have a strong quantitative education! Just that you can do that using lots of different methods and doing it well should always be the first priority.)

The problem with collaboration in the electronic era...

E-communication has revolutionized every aspect of our lives. From how we shop, find love, watch movies and do science, the ability to interact with others globally has virtually eliminated barriers to the flow of ideas. I have fruitful collaborations with researchers in many different countries, which are greatly enhanced by e-mail and Skype. However, a new problem has emerged -scheduling people for meetings in multiple timezones!

Green = optional working time for researchers in different timezones; yellow = suboptimal; red = perhaps we allow people to sleep.

I routinely have Skype meetings with my editorial team in the UK at 5 or 6 am, but as the above graphic shows -scheduling a meeting amongst people in the UK, North America and Australia is virtually impossible.

Are scientists boring writers?

I was talking with an undergrad who is doing her honours project with me about the papers she’s reading, and she mentioned how difficult (or at least slow going) she’s found some of them. The papers are mostly reviews or straightforward experimental studies, but I remember feeling the same way as an undergraduate. Academic science writing uses its own language, and until you are familiar with the terms and phrases and article structure, it can be hard going. Some areas, for example theoretical papers, even have their own particular dialects (you don’t see the phrase “mean-field approximation” in widespread usage, for example). Grad school has the advantage of providing total immersion into the language, but for many students, lots of time/guidance and patience is necessary to understand the primary literature. But is science necessarily a boring language?

A recent blog piece argues that academic science writing needs to fundamentally change because it is boring, repetitive, and uninspired. And as a result, the scientific paper needs to evolve. The post quotes a biologist at University of Amsterdam, Filipe Branco dos Santos: he feels that the problem is rooted in the conservative nature of scientists, leading them to replicate the same article structure over and over again. Journals act as the gatekeeper for article style too – submission requirements enforce the inclusion of particular sections (Introduction, Methods, Results, Discussion, etc), and determine every thing from word counts, figure number, text size, and even title structure and length. Reviewers and editors are within their rights to require stylistic changes. The piece includes a few tips for better article writing: choose interesting titles, write in the active tense, use short sentences, avoid jargon, include a lay summary. It’s difficult to disagree with those points, but unfortunately the article makes no attempt to suggest what, precisely, we should be doing differently. Still, it suggests that consideration of the past, present and future of scientific writing is necessary.

One glaring issue with the post is that the argument that scientists are stuck in a pattern established hundreds of years ago ignores just how much science papers have changed, stylistically. Scientific papers are a very old phenomenon – the oldest, Philosophical Transactions of the Royal Society, was first published in 1665. The early papers were not formatted in the introduction / methods / results / discussion style of today, and were often excerpts from letters or reports.

From the first issue, “Of the New American Whale-fishing about the Bermudas” begins:

“Here follows a relation, somewhat more divertising, than the precedent Accounts; which is about the new Whale-fishing in the West Indies about the Bermudas, as it was delivered by an understanding and hardy Sea-man, who affirmed he had been at the killing work himself.”

Ecological papers written in the early 1900s are also strikingly different in style than those today. Sentences are long and complex, words like “heretofore”, “therefore”, and “thus” find frequent usage, and the language is rather flowery and descriptive.

From a paper in the Botanical Gazette in 1913, the first sentence:

“Plant geographers and climatologists have long been convinced that temperature is one of the most important conditions governing the distribution of plants and animals, but very little has as yet been accomplished toward finding out what sort of quantitative relationships may exist between the nature of floral and faunal associations and the temperature conditions that are geographically concomitant therewith.”

While this opening makes perfect sense and establishes the question to be dealt with in the paper, it probably wouldn’t make it past review without comment.

Some of my favourite examples that highlight how much ecological papers have changed come from R.H. Whittaker’s papers. He is clearly an avid (and verbose) naturalist and his papers are peppered with evocative phrases. For example, “If, for example, one stands on a viewpoint in the Southern Appalachian Mountains in the autumn, one sees a complex varicoloured mantle of vegetation covering the mountain topography” and “The student of vegetation seeks to construct systems of abstraction by which relationships in this mantle of vegetation may be comprehended.” Indeed!

Today, in contrast, academic science writing is minimalist – it is direct, focused, and clarity is prized. Sentences are typically shorter, with a single focal thought, and the aim is for a clear narrative without the peripatetic asides common in older work. These shifts in style reflect the prevailing thoughts about how to balance the role of scientific papers as a communication device versus as a contribution to the scientific record. It seems that science papers may be boring now because authors and editors would rather a paper be a little dry rather than be unclear or difficult to replicate. (Of course, some papers manage to be both boring and confusing, so this is not always successful….) Modern papers have a lot of modern bells and whistles too. The move away from physical copies of papers to pdfs and online only colour versions and supplementary information has made sharing results easier and more comprehensive than ever.

If there is going to be a revolution in academic science writing, it will probably be tied to the ongoing technological changes in science and publishing. The technology is certainly already present to make science more interactive to the reader, which might make it less boring? It is already possible to include videos or gifs in online supplements (a great example being this puppet show explaining Diversitree). More seriously, supplements can include data, computer code used for analyses or simulations, additional results. It’s possible to integrate GitHub repositories with articles tied to a paper’s analyses, or link markdown scripts for producing manuscripts. The one limitation is that these approaches is that they aren’t included in the main text and so most people never see them. It’s only a matter of time before we move towards a paper format that includes embedded elements (extending on current online versions that include links to reference papers). One could imagine plots that could be manipulated, or interactive maps, allowing you to explore the study site through satellite images of the vegetation and terrain.

Increasingly interactive papers might make it more fun to work through a paper, but a paper must stand alone without them. For me, the key to a well-written paper is that there is always a narrative or purpose to the writing. Papers should establish a focus and ensure connections between thoughts and paragraphs are always obvious to the reader. The goal is to never lose the reader in the details, because the bigger picture narrative can be read between the lines. That said, I rarely remember if a paper is boringly written: I remember the quality of the ideas and the science. I would always take a paper with interesting ideas and average writing over a stylish paper with no substance. So perhaps academic science writing is an acquired (or learned) taste, and certainly that taste could be improved, but it's clear that science writing is constantly evolving and will continue to do so.

I'll take 'things that have nothing to do with my research' for $400

I guess I do have a couple papers with the word fire in their titles?
And to Burns and Trauma's credit, this is a nicely formatted email and the reasons to publish with them are pretty convincing :-)

The wonder(ful) years? Being a postdoc.

A surprising number of academics talk about their postdocs the same way some people talk about high school – as the best time of their life. It’s enough to make you feel like you’re doing something wrong if you don’t feel the same way :). In fact, postdoc jobs vary greatly and are usually a bit more complex than the “wonderful time to do the research you love and have few responsibilities” memories.

The truth is, every postdoc position and postdoctoral fellow is different, and each has its own strengths and weaknesses. Not every postdoc will be a glorious NCEAS position--self-directed, collaborative, community-oriented (and in Santa Barbara). And not every postdoc will be part of a funded, predetermined research project where you are just a cog in the lab-machine. (Though some people would legitimately prefer the latter to the former).

There are many great aspects to being a postdoc. After a PhD, you no longer need to take classes, you may not need to teach, and research is finally your primary focus. Even better, ‘the thesis’ isn’t looming over your head. And usually you make actual money, rather than poverty-level grad student wages, even if the actual amount is modest. Finally, you probably don’t have nearly as many responsibilities as the average tenured or tenure-track academic (and so, the assumption is, your stress levels are better). (And for those who need a little ego boost, you occupy a slightly better position in the lab hierarchy, maybe you get a better office and get a little more respect.) Best of all, you can finally stop saying you’re a student.

These are all wonderful in theory, but sometimes the reality is more complex. Because academic positions, including postdocs, are hard to find, not everyone will be able to land a position that is well-matched to their skills and research interests. This can feel frustrating, since academics in general want intellectual stimulation and skill development. Finding the perfect lab is difficult, and finding the perfect lab with money to pay you is even harder. As a result, that perfect path on the CV from PhD to postdoc lab where you expand your skills or fine-tune your interests is exceptional.

For many people, the postdoc is a time with a large set of associated stresses; first and foremost, “what’s next?” (for you, your family, your career, your geographic position on the earth...). This is the period when the next position and, more generally, your career, is at the forefront. And the timeframe in which you must sort everything out is short, since most postdoc positions last only 1-3 years. It is not uncommon to run out of funding before the next position has been acquired. Get a group of postdocs together in a room, and the undercurrent of worry will be palpable.

And of course, the short length of most postdoc jobs means that you will probably have to move more frequently than ever. Combined with the unorganized nature of postdoc labour, this can make for a lonely time. In smaller departments, postdocs are few and transient, making it difficult to feel part of a community. No longer a student, not quite a faculty, only temporarily in a place, it can be hard to find a sense of belonging.

None of this is to bemoan the postdoc life, just to note that as with all things, it has pros and cons. I like being a postdoc. I’ve also been lucky to have independent funding though, which no doubt has made things easier. Still, the upsides have included the ability to developing a research plan for the long term (and to make mistakes and fail while the stakes are still low), to supervise undergraduates, to develop a new skills or viewpoints, and definitely to have time for manuscript writing. But I do hope that these aren’t the *best* research years of my life, because—like high school—things can always improve. 

(or any job, really)