How to Give a Great Presentation

A few weeks ago now, I hosted the Biotweeps Twitter account for a week. It’s an account with rotating hosts: every week, a different biologist takes over and posts about, well, whatever they want, but usually at least partly about their research.

I had a lot of fun hosting. I talked about research in the Arctic and climate change; I talked about stream biodiversity and why freshwater conservation is important; I talked about what an ecosystem is and how ecosystems are connected in cool ways; and I also talked about work-life balance and what I do for fun. I had great discussions with the account’s followers about what other jobs they’ve had, if they always knew they were interested in science, and how life experience shapes us into scientists.

(You can see my whole week of posts here– it will show up with someone else’s name because someone else is hosting now, but these are my posts from June.)

But some of the threads that got the most attention were about organization and what we might consider “transferrable skills”: the non-research part of how to do good science (these skills are very important for many other jobs, too, not just science!).

I had the idea to post about this because last year, Kevin Burgio shared his organizational system when he was hosting BioTweeps. I have since adopted part of his system and it has been immensely helpful. I wondered if some of the things I’ve learned could help others.

So I posted about how to organize and manage data, from pre-experiment planning to analysis, both to make your life easier and to promote reproducibility. I talked about general organization, and how to get the most out of going to conferences. These posts generated a lot of discussion and I learned a few things too.

Hosting BioTweeps was a lot of work, and it took more of my time than I would have expected.

At the same time, I was in a bit of a blogging rut. I had resolved to blog more this year, and for a while I did, but then I sort of ran out of steam.

I realized that I was talking about a lot of things on Twitter that earlier I might have turned into blog posts. This wasn’t just true in BioTweeps week, although it certainly hit a peak then. I have been trying to think about which venue is more important to put time into in terms of communicating. There are a lot of stories, especially ones about travel or my outdoor adventures, that I don’t think I could tell well on Twitter. I want the space of a blog post to compose and tell them.

But there are other things that could be either. It’s a bit easier to make a Twitter thread than a blog post, so maybe I have been leaning in that direction, and I now have enough Twitter followers that I feel like I might reach more people that way.

But blog posts have their own plus sides. They are a bit more permanent, and they are easier to find with a search or refer back to. I don’t think I should stop blogging.

And so, here I am: I’m going to make a blog post out of one of my BioTweeps advice threads, about how to give a good presentation.

How to give a good talk

Why do I think I’m qualified to give advice about this? First, I really enjoy giving presentations. What’s more fun than talking about work you’re excited about? Second, I’ve won a few “best presentation” prizes at conferences, so I think I don’t suck at it.

Those prizes are not all because of me. Especially in the last 4 ½ years, I have gotten a lot of advice from colleagues. In my (now-former) research group, we had a culture of giving practice talks and getting extensive feedback on them. It was sometimes brutal but we all gave as good as we got. As a result, everyone in our lab gave great presentations.

But there are other things that go into making a talk that I do myself. I was motivated to share tips after I realized last summer that people thought it was easy for me to give a good talk. Haha. Nope.

I first saw the mismatch in expectations when attended a conference with my boss. He was shocked that the night before I was scheduled to give my talk, I locked myself in my room and practiced for hours. He thought that I was just good at presenting and didn’t need to practice.

I’m sure that there are people who can wing it and do great. But that’s not most of us, and it’s also not me. Quite the opposite. One way to make your delivery seem effortless is to practice it until it is, at which point, the hard work becomes partly invisible.

Practice is a basic, but very important, tip. Here are some others that will help make a great presentation.

  1. How should you structure your talk?

I’ve recently moved away from structuring my talks like research papers, with sections for introduction, methods, results, and then discussion. Now I just try to tell a good story. My talks are better for it – and I think this would be true for any kind of presentation, on any topic.

I try to think a bit about what I learned about constructing a story as a journalist: how do you draw the reader in, and then keep them reading?

Just as in a written story, start with a lead that grabs the the audience’s attention. This probably shouldn’t take more than a minute or two, although depending on how long the presentation is, it could vary.

Then, you get to what in journalism we would call the “nut graf”. This follows the lead and should be one slide, if you’re using PowerPoint. It’s the thesis and motivation of your whole presentation: what question are you trying to solve and what are you going to do? Give this to the audience early enough that they know where you’re going with the rest of the talk.

From here, there are lots of possible structures depending on the length of your talk and the material. You could have a bit more introduction after the nut graf, or you could get into the meat of your presentation.

Supplement your story with technical details, but not too many. You want to include enough that people trust that you are an expert and did things right, but you want them to remember the big picture, not that you had a lot of equations on your slides. Once you’ve demonstrated your expertise, just highlight the things the audience needs to know. Don’t distract the audience with some number or result that isn’t important.

(If you really think someone will want more detail, make extra slides that you can go to in Q&A. But don’t overwhelm or bore the 95% of your audience that doesn’t want that level of detail.)

For scientists, I find that using the traditional paper structure can lead to a lot of repetition, and if you have several analyses or result to present, the audience might have a hard time remembering what methods went with which results or why you are jumping from one topic to the next. Remember that this is a talk. It’s not a paper where they can flip back and remind themselves which method you used.

So if you have several sub-questions/results, explain each one separately. For three research questions, this would go, methods, results, methods, results, methods, results. Use narrative to link them together: “based on result x, we were interested to follow up with experiment y.”

Then, make sure to have a discussion and conclusion that ties all those sub-questions together.

  1. Content: what do you put on your slides?

This is not original advice, but it’s advice I swear by: don’t put too much on your slides. I like to have some that are just a photo, a figure, or a few words. I leave them up as an anchor/background while I talk about something. You want people listening to you, not reading your slides.

I also use visuals because then the audience has two ways of getting information. The text you write will probably be very similar to what you will say, so if they don’t understand your spoken explanation, additional text might not help very much.

There are a few more things you can do to make it easy for your audience to follow the story. For example, choose a consistent font throughout the presentation and make the text big enough to read from the back of the room. You think it’s big enough? Make it bigger.

Do you have charts or graphs? Make the text big. Don’t just recycle figures from a research paper or take them straight out of Excel. Make the labels and text bigger and easier to interpret from far away, and if possible use the same font as your slides’ text.

Choose a nice color scheme. I make my graphs in R (a statistical software) and I like to use the online tool ColorBrewer or the ‘viridis’ R package to choose color schemes that are more pleasing than the defaults. If you have multiple charts with the same set of variables, make sure the color scheme is consistent throughout all of them – this makes it easier to follow.

No matter what software you are using to produce figures, make sure that they are easy to interpret for people who are color-blind, of which there will probably be at least a few in your audience. ColorBrewer indicates whether a color scheme is colorblind-friendly.

In PowerPoint, you can use the same tools to choose a color scheme to apply to your layout.

There are also some good sources of artwork you can use to make your slides nice. Pixabay has some free images, or search Wikipedia or Creative Commons. Phylopic is great for free images of plants and animals. Government agencies often have free imagery too. IMPORTANT: attribute any images that are not your own!

Whether it’s a chart or diagram, I often go through visuals sequentially. For example, for the first graph, I will often start with a slide that has just the axes, and I will explain what they are before adding the data to the plot. I will sometimes add the data in a few different steps if it is a complicated figure. This can make your results much easier to understand. The same goes for a complicated diagram: adding elements sequentially can allow you to highlight and explain what’s important about each one.

Finally, don’t forget to have a slide thanking people who helped with your research (including funders). I don’t like to end on this slide, because during question time I want to leave a slide about my conclusions up. Recently I’ve tried putting my thank-you slide second, or in the middle.

  1. Delivery: you’ve made your slides, now how do you do the talking?

As mentioned above, my best advice is simple: practice, practice, then practice some more.

I’ve been working in Europe for seven years, and people often tell that presentations must be easy for me because I’m a native English speaker. And yes, it’s obviously an advantage to give a talk in your first language!

But they’re surprised when I say, “Well, I practiced this talk 10 times…”

I am incredibly lucky that I don’t have to do science in a foreign language, and I don’t want to downplay this advantage. But remember: we’ve all heard terrible talks by people presenting in their native language. This might be because they haven’t practiced, or they simply don’t care very much, or they are nervous and uncomfortable with public speaking.

Practice and enthusiasm can go a long way. If you aren’t quite done with your project but have to present about it anyway, you can absolutely give a good talk despite lacking a finished conclusion. If your results are disappointing, your talk can still be great – it’s all about how you present and deliver it. If you aren’t completely comfortable giving a talk in a non-native language, copious practice can help. If your slides are clear and your demeanor is positive and enthusiastic, the audience will almost definitely be on your side.

Practice also helps me fit a lot of content into a short time slot. My talks are dense with information, but most feedback I receive is that they are still clear and easy to understand. By practicing repeatedly, I can pare my language down, cut minutes off of the length of the talk, and settle on the most concise, clear wording.

So, practice. One colleague said he didn’t want to memorize his talk because he didn’t want to sound like a robot. I told him if you memorize a talk well, you can actually be quite dynamic. The trick is to practice until your delivery is more or less automatic, and then keep going. Once you know it 100%, you will become so comfortable you will begin riffing a little bit, and you won’t sound like a robot at all.

One you’ve practiced a bit on your own, practice in front of colleagues and friends. Even if they aren’t familiar with the content, they can still give great advice on delivery and slide design. Scheduling this ahead of time and some days/week(s) before your presentation will also force you to make your presentation before the last possible moment.

Two reminders that should go without saying, but apparently are needed. No matter whether you think your voice is loud or not, use a microphone. Your audience might not be able to hear you, and you actually aren’t the best judge of whether they can or not.

And finally, keep to time. If you don’t, you are inconveniencing everyone else, whether it’s by making a meeting run long or running into the next slot at a conference and possibly costing someone else the chance to communicate their project.

I hope these tips can help you nail it next time you need to talk to an audience about whatever you’ve been up to!

“I Contain Multitudes”: Microbiomes, Ecology, a Book Review, and Speculation

IMG_5214

Perfect with a negroni.

What is the measure of a good science writer?

Both my boyfriend and I – one of us a scientist, the other not – adore reading Ed Yong’s columns in The Atlantic. That might be a pretty good measure, and it is one that Yong passes with flying colors: both the experts in fields he writes about, as well as nearly everyone else, are happy when he puts words on the internet.

(His Valentines tweets are no exception. I’m trying not to get sidetracked, but it’s hard, and that link is worth clicking, I promise.)

Nevertheless, when Yong’s first book, I Contain Multitudes, was released, I wasn’t that thrilled.

It’s about the microbiome, and I just wasn’t that excited to read about the microbiome. It seemed very of-the-moment, very bandwagon-y. As an ecologist I was kind of sick of hearing about the microbiome, and of people asking me whether my study organism’s microbiome might explain X or Y thing I had found about it.

Like, I’m studying all these complicated non-microbial things about my organism’s ecology, and I’m supposed to somehow have all the skills, techniques, and equipment to also understand its microbiome? Please go away. That’s so too much to ask.

Anyway, this month I finally read the book, and boy was I wrong.

The book was great.

And the microbiome is fascinating.

It was a delightful read, and among the reasons is that Yong describes perfectly some fundamental things about being an ecologist. Take this passage, for example:

“Here is a strange but critical sentiment to introduce in a book about the benefits of living with microbes: there is no such thing as a ‘good microbe’ or a ‘bad microbe’. These terms belong in children’s stories. They are ill-suited for describing the messy, fractious, contextual relationships of the natural world…. All of this means that labels like mutualist, commensal, pathogen, or parasite don’t quite work as badges of fixed identity. These terms are more like states of being, like hungry or awake or alive…”

For any scientist who has found a result, then explored seemingly the same situation over again and got a completely different result, this passage will spark a laugh or sigh of recognition.

It can seem like everything in ecology is context-dependent. Sometimes we can demonstrate what context matters and how the mechanism operates; other times it’s just a nice way of saying, we have no idea what’s going on.

Anyway, through Yong’s typically-excellent storytelling and the way I could identify with the scientists he profiled – men and women, young and old, at universities and zoos and NGO’s and research institutes around the world – I became immersed in tales about microbes.

As Yong points out, microbes were everywhere when multicellular life evolved. So we multicellular beings evolved with them. They made our lives easier, in some ways; and we helped them get ahead. Sometimes the relationship is good for everyone, sometimes not. But with microbes all around us for millions upon millions of years, the relationship is inevitable.

And so we have incredible interactions.

Of course, there are all the microbes in our guts: the gut microbiome, which is discussed all the time, it seems. Ours are worse than they used to be, worse than hunter-gatherer societies, worse if we eat more highly-processed food. This influences our health in so many ways.

But there are also more seemingly-fantastical things.

Microbes that help squids glow, canceling out the shadow that predators might see from below against the night sky, and thus protecting them from death! That makes a brilliantly intelligent cephalopod which just happens to be bioluminescent.

Mice that have gut microbes that help them eat creosote without any ill effects! Cute little fairy tail creatures that can eat a poison pill and just keep on going.

Another charming example? Having a pet, and a dog in particular, is one of the best ways to have a healthy, robust, microbiome. The pet brings microbes into the house from its travels outdoors, and those microbes become your microbes. I’m tallying up all the possible justifications for why we should get a dog, and this is a great one to add to the list! We need a dog because, science.

(Also on the list is that a dog can help you decide author order on papers. I mean, there are other ways, but let’s get a dog.)

Some stories are discouraging, like how a microbes help mountain pine beetles process and disarm the chemical defenses of trees, and thus to kill vast swathes of forest in North America. I thought I knew a fair bit about pine beetle devastation, but this was new to me.

Others stories are hopeful, however.

One of my favorites was a story about researchers trying to combat dengue fever. They raise mosquitos with a bacteria living inside them which makes them resistant to the dengue virus. And now they are letting those mosquitoes loose: they started in Australia, and went door to door to convince neighborhood residents to foster the new mosquitoes, even though most people would say “no way” if you asked whether you could drop some extra mosquito larvae next to their house. Bzzzzzz.

Just by carrying a bacteria, mosquitoes as a vector of this particular disease might be a thing of the past, at least in some places.

Yong also highlighted the work of Dr. Jessica Green, who was a new-ish professor in my department at University of Oregon back when I worked as a technician there.

Green studies the microbiome of buildings. It’s fascinating stuff, and even more interesting when you get to hospitals: leaving the window open to let natural microbes in might help fight off the bad microbes that give so many hospitalized people infections.

Thanks to microbes, there are simple interventions that might make a big difference in people’s lives. Our modern way of living and germ-phobic worldview has broken many of the relationships we used to have, but we are learning more and more about which ones we should preserve or restore. And it’s leading us to create new ones, too.

Along the way, I also began to think about a lot of things not covered in the book.

(I Contain Multitudeswas published in 2016, so a lot of science has happened since then in this rapidly-advancing field.)

For example, how might the microbiome alter human performance? The first thing that popped to mind was sleep. I have pretty much always been terrible at sleeping. I have a hard time falling asleep at night, and sometimes my sleep is restless.

In many aspects of life, sleep is vitally important. I think back to my time as an athlete: rest is one of the most important aspects of training, but if you aren’t sleeping well, you’re missing some of it. I definitely was.

Since 2016, some science has come out suggesting that lack of sleep alters your gut microbiome, and that the relationship also goes the other way, that your microbiome affects the process leading to sleep. But it’s hard to parse this research and assess its quality. I need someone like Yong to do that for me, and condense the reliable findings down into something digestible (see what I did there?).

In fact, I wondered about how the microbiome might affect athletes more generally. People doing a lot of training would benefit from all sorts of specific adaptations, including to diet and metabolism. Do microbes help in that? Does having the wrong microbiome hold you back?

Here, too, there has been a bit of research. For example, Outside wrote up a piece where they had seven elite athletes get their gut microbiomes sequenced. They found plenty of deviation from the average American, but as you can read in the piece, what did that actually mean? Hard to say. There is still so much we don’t know about microbes and which ones do what.

Another recent paper found that rugby players had increased prevalence of microbes that with functions that increased muscle turnover. This approach, looking at “metabolic phenotyping” and metabolomics rather than only the composition of the kinds of microbes, might be more informative. However, because the athletes in the study ate different diets than the non-athletes, it’s hard to totally understand the implications of such differences.

Still, I’m interested in work like this. What would it say about endurance athletes?

Something to remember, though, is that even if we figured out that the human gut microbiome could be used to get better athletic performances or to maintain a better training load, it might be hard to act on that information.

In humans, there are still few silver bullets for the microbiome. Knowing that a microbe is good isn’t enough. In many cases, a microbe can be helpful or protective in one context but harmful in another (for instance if it reaches too-high abundance).

And it’s also hard to deliver a microbe into the gut and have it take hold.

One of the first stories I heard about the microbiome was about fecal transplants, which were used with great success to reset some people’s guts and solve major, seemingly-unsolvable health problems. It was on a podcast, although I now can’t remember which one. It was a wild story.

Yong writes about this, too. But he points out something researchers have learned in the years since the first fantastic results using fecal transplants to cure people of aggressive diseases.

The reason that fecal transplants work so well with some diseases is that the native gut flora has been pretty much wiped out by the combination of the disease and the antibiotics used to treat it in its initial stages.

“This pharmacological carpet-bombing clears many of the native bacteria from their guts,” Yong writes of patients with Clostridium difficileinfections who receive fecal transplants. “When a donor’s microbes arrive in this wasteland, they find few competitors, and certainly few that are as well adapted to the gut as they are. They can easily colonise… ‘you can’t just infuse microbes into people and expect a transplant to happen’, says [gastroenterologist Alexander] Khoruts.”

So even if we knew of a silver-bullet microbe that would help you metabolize or do something else to perform better, could we get it to colonize an athlete’s gut? Unclear.

In the end, if you want a healthier microbiome, a lot of it probably just comes down to eating a healthy, diverse diet, and having healthy habits. And that’s what athletes should be doing anyway.

I wonder if the best way to have your microbiome help your athletic career is just to do a bunch of things that you already know you should do. Eat well. Sleep. Hug the people you care about.

Here’s my final take about the measure of a science writer. A good writer can make you understand things you’re already familiar with in a whole new light.

An entire research group in my department studies the aphid-Buchnerasystem.

Aphids are small insects that like to live on, for example, pea and bean plants. Buchnera are bacteria that live inside the aphids; they got there over 200 million years ago, and each strain of aphid has its own strain of Buchnera. The bacteria produce amino acids that they don’t get from their main food source, phloem. And there’s another microbe, Hamiltonella defense, that protects the aphids against parasitoid wasps, which lay their eggs in an aphid and whose larvae gradually consume them from the inside out, turning them into “mummies”. (Yeah, it’s gross.) Different Hamiltonella strains have different protective abilities and costs.

I can’t tell you how many research talks I have listened to about aphids. Usually, it has just seemed complicated and confusing – even when my friends and close colleagues are explaining it.

But when I read Yong’s description of the study system in his book, all of a sudden, the whole thing made sense to me. First of all what was going on, and secondly why it was fascinating. I will look at my colleagues’ projects differently, and with a lot more interest.

If that isn’t the measure of science writing success, I’m not sure what is.

You can purchase I Contain Multitudes at Powell’s or your favorite independent bookseller.

Women Get Cited Less. What Can We Do About It?

A few weeks ago, a paper came out about the fate of research papers in ecology and evolution (my field!) pre- and post-publication, comparing outcomes between male and female authors.

I want to focus on just one aspect of their nuanced analysis (you can read the whole paper here for free). In this section, the authors gathered citation data on over 100,000 papers published in 142 journals in our field.

They found a slight, but significant, difference in how often papers by men and by women got cited. On average, controlling for the impact factor (a proxy for quality[1]), papers by women accrued 2% less citations.

A hundred thousand papers! That’s a lot, and it’s why the authors could detect this small difference. The sample size allowed them to do a quite powerful analysis.

It also revealed some interesting interactions. For example, papers with female last authors (which often indicates seniority or leadership of the research group) were cited less than those with male last authors at high impact journals, but the effect was reversed at low-impact journals.

Because more people cite papers in high-impact journals, this meant that overall, women were cited less often. [2]

I guess this shouldn’t have been a surprise, but it’s not something I had seen data about before, and consequently something I hadn’t really thought about.

But it is something that matters. Like it or not, citations are a metric of success that is easy to measure, and therefore whether others cite your work is a good piece of evidence that you’re a valuable scientist when you’re up for a job position, tenure, or an award. It’s not great for women if there is bias preventing them from doing well on this metric. [3]

Sounds About Right

Like a lot of research and headlines about challenges facing women in science, this got me mad. I’m a feminist, and this stuff pisses me off.

I see the experiences of my female friends and colleagues, and see when they are treated differently than their male peers. Not always, of course, but enough to make a pattern of our own anecdotal experience.

Then there’s the fact that in my study topics, almost every giant in the field is a man. The ones that defined the discipline and published the equations in a top journal? Men. There are women there, doing great work, but they are less famous.

When data on various aspects of academic life backs up our experience it’s like, “yup, sounds about right.”

Academia is harsh on most people; it’s a place with a lot of rejection, high standards, low pay, job insecurity, and so many power dynamics. The fact that academic science is even harder on women is just not fair.

And that’s not getting into the more complex and devastating nature of the structural problems in academia, which are even worse for other minorities, especially minority women. This paper found that on average women are cited 2% less than men; how much less often are minority women cited?

Anyway, I read this paper, and I was mad.

What I Did Next

Being mad doesn’t accomplish all that much. I tried to think about what we, in our daily lives as scientists, could do to work against this problem.

As an individual early-career scientist, I can only do a very little bit about peer review outcomes or paper acceptances.

But citations? That’s different. I am always writing papers, and I am always citing others’ work. I realized that this was a small step I could take to try to contribute to supporting female scientists. It sounds trivial, but I can cite their work. Could we all do this?

I brought this idea to our lab group, and was curious what they would think.

Some colleagues immediately recognized that this was a problem, and something we don’t think about enough. There are a handful of big names in our subfield, and we mostly cite them over and over. But do we need to do that? Are their papers that we cite every time actually the most relevant? Maybe not. There is probably a lot of work being done around the world we could cite that instead, or at least in addition to the now-traditional canon.

Another common reaction was for someone to say that they don’t think about the gender of authors when they search for papers, read them, or cite them. Here it diverged: a few people said, “I don’t think about it and now I realize that I should.” Others said, “I don’t think about the gender of authors when I’m citing them, therefore I’m not part of this problem.”

I challenged this statement. Does that really mean you’re not part of the problem? Maybe not, I said – and if not, that’s great for you, good work! But instead of assuming that not consciously citing more papers by men means no harm is done, check your reference list on the paper you’re writing. What’s the author breakdown? Do you think this strategy is really working?

I don’t think I was popular for making this callout.

Rubber Meets the Road

As I mentioned in a tweet a few weeks ago, “caring means walking the walk.”

In my paper-reading project, I track the gender of the authors I read, and I found that I am reading fewer papers by women than by men. I was curious about what the ratio might be of the papers I end up citing.

I went through the references section of my current manuscript with a blue and a pink highlighter (I know, supporting stereotypes, etc., it was lazy). The results were not pretty.

I was frankly surprised at how few of the papers I had cited were by women. I knew it wouldn’t be 50/50 for a lot of reasons (discussed below), but I didn’t think it would be that bad.

I basically proved to myself that in order to cite women, you have to do it on purpose. Just not intentionally excluding them isn’t enough.

It’s like how colorblind policies don’t work. Not intentionally doing harm is not the same thing as not doing harm. As Evelyn Carter writes about claiming to be colorblind with respect to race, “if you ‘don’t see’ race, but you say you care about inclusion, how can you advance inclusion efforts that will effectively target communities of color?”

There is a lot of unconscious bias and systematic barriers that lead us to contribute to inequality. Working for equality and to recognize contributions made by women and minorities means actively working to overcome those unconscious biases and systematic barriers.

Shifting the Balance

After my discouraging experiment with the highlighters, I went through the paper and looked at the places I had cited different work. In some places, I was able to find a paper by a woman that I could cite instead – and often even one that supported my point even better than the paper I had cited originally.

That was the most delightful aspect of this task I had set out for myself: I discovered papers in my core area of study, that were by women and that I had never read. And they were really good and very interesting!

The point of reading and citing work by women isn’t just to check boxes and give women a fair shot at career metrics. The main reason is to do better science. Science is creative; it involves having ideas, being exposed to new things, thinking outside whatever box you’re in. Reading work by more different people will necessarily help that process.

I’m really glad that I found these new-to-me papers.

As I discussed with a different colleague later, a lot of this issue comes down in part to poor citation practice, which is endemic across academia. We cite something because everyone else cites it, even though maybe we haven’t read the whole paper. Or we cite something because it’s already in our reference library and we are in a hurry. I’m completely guilty of this, and I’m quite sure everyone else I work with is, too.

If we spent more time reading, and more time looking for and getting familiar with other work that’s related to what we’re doing, I think all of our papers would have much more diverse author lists – at least in terms of evenness and not being dominated by the famous people in our field.

Our papers might also just simply be better, because of all the ideas we would be having.

Don’t Worry, I’m Still Citing Men

After this citation overhaul, my reference list was still majority male first authors. You need to cite what is relevant, and in a lot of cases this work is by men. One reason is that over the history of ecology, there is a lot more work published by men than by women, especially the farther back you go. [4]

Plus, if you need to cite a classic paper, it doesn’t matter who it’s by. That’s the one you need to cite. That’s a second thing. [5]

And likewise, if you need to cite something about your specific study organism or system, there might be only a handful (or a few handfuls) of people in the world who publish on this very specialized niche. They are who they are. If you need to cite peer-reviewed literature, you may have limited choices, and you need to cite the best and/or most relevant work out of that array. [6]

So there are a lot of reasons you can’t just take your reference list and manipulate it towards 50/50 gender equality. I want to make it completely clear: I am not advocating for a departure from citing good and relevant science!

When I mentioned this idea to colleagues – that citing women was a seemingly small, basic thing that we could do in our everyday lives as scientists to make a difference in structural biases – some were deeply uncomfortable that if they sought out work by women, then they would have to leave out other papers.

Listen: there is so much research out there, it boggles the mind. It’s growing exponentially. It’s insane. And in no paper do we cite all the possibly important research on a topic. We’re going to leave things out anyway. It’s just that now, we seem to be structurally leaving out work by women. Again, to overcome this bias is going to require intentionality. This is not a problem that we can just hope will go away because we are good people and don’t mean to cause harm.

No matter what we do, we’re going to keep citing a lot of great research by men.

Tokenism?

Among women, there was another layer. Many of the women I talked to did not want to be cited just because they were women and someone needed to move their reference list towards gender parity. They wanted to be cited because someone genuinely thought their research was the best and most relevant.

And I get that. I was invited to give a talk once because the organizer was looking for a replacement female speaker after the originally-invited woman couldn’t participate. I was so excited for this opportunity, but at the same time it felt weird to get it explicitly because they were looking for a woman.

I’m not sure what to do with this concern, but I think it comes back to proper citation practice. Is your work relevant to the topic, and being cited correctly? Then you deserve to be cited. If papers by women are accepted less often and cited less often, then part of the reason you are not currently being cited might be simply because someone isn’t familiar with your work.

And The Inevitable Question, Does Quality Limit Equality

Finally, we had some conversations about whether this might be because women’s work just wasn’t as good as men’s.

It’s pretty hard to assess that (although the paper I started this blog post with tried to, by using journal impact factor as a covariate).

I have two thoughts. One is that I doubt it’s true. There was an interesting graphic and paper that went around Twitter – about economics, not ecology – showing that papers by women are better written than those by men, that women incorporate reviewer comments more often, and that they improve at presenting information over the course of their careers.

It’s provocative and I’m not sure if it’s also true in my field or not, but I believe it. I think we are culturally trained from a young age to try to please, and so we might be likely to try to pacify reviewers, and to make a revision extra perfect if it was rejected the first time around.

Also, in the sciences, women have to be better to be evaluated as being as qualified as men.

Second, about the content. If it is possible that the data and experiments presented by women are less strong, why might that be?

It is interesting to think about how structural problems would lead to this. Could it be because women get less funding to do their research, and thus might have less resources or support? Could it be that women are asked to do more departmental service and teaching, and have less time to do research?

If the research really is worse – which I’m not convinced it is, but there’s little way to objectively assess, at least not for a dataset of any size – this very well might be a result of the same structural issues that cause the citation patterns.

What Next

What do you think about this? Are there any other ways to work on this problem?

Notes

[1] Impact factor is not a perfect measure of journal quality. It is an estimate of how often work there gets cited, which is a traditional metric for how important it is. For any individual paper, the impact factor of the journal it is published in doesn’t say much about the quality of that paper. However, I think it was the best covariate the authors could find to control for the differences in quality between papers when comparing men’s and women’s outcomes. Also, for better or for worse scientists pay attention to impact factors, so it may affect citation practice even if it’s not an actual metric of “quality” per se.

[2] I have an interesting, harebrained idea about this: if papers by women are less likely to be accepted, do good papers with women as last authors end up in lower-impact journals? That could explain why the better-cited papers from those journals are by women. Totally spitballing here.

[3] Citations shouldn’t define one’s value as a scientist, colleague, or employee anyway, but… that’s a discussion for another day.

[4] Is it really though? Women probably contributed to many important ideas. They typed the manuscripts. Maybe they did some of the work. There are tons of cases in science where people had the same idea independently, and one person got famous, and the other didn’t. Sometimes both these people were white men. I’m guessing there’s a lot of times when some of these people were women, minorities, scientists from outside Europe and North America, and people who were/are otherwise excluded from the elite science community.

[5] Given what I just wrote, I think I – and we all – need to put more effort into finding papers that were written around the same time as the famous ones that “came up with” ideas, and represent contributions of the community-building of those ideas by other people.

[6] When you’re looking for very specific things, there might be a lot of important and relevant data in theses and lower-impact papers by students who did not continue in science. This is valuable literature to search for, and might expand what you think the contributions of women and minorities are, given that these people are less likely to continue in academic careers either by choice or by exclusion.

From #fieldworkfail to Published Paper

Amphipods are, unfortunately, not very photogenic. But here you can see some of my study organisms swimming around in a mesocosm in the laboratory, shredding some leaf litter like it’s their business (because it is).

It can be intimidating to try to turn your research into an academic paper. I think that sometimes we have the idea that a project has to go perfectly, or reveal some really fascinating new information, in order to be worth spending the time and effort to publish.

This is the story of not that kind of project.

One of my dissertation chapters was just published in the journal Aquatic Ecology. You can read it here.

The project originated from a need to show that the results of my lab experiments were relevant to real-world situations. To start out my PhD, I had done several experiments with amphipods – small crustacean invertebrates common to central European streams – in containers, which we call mesocosms. I filled the mesocosms with water and different kinds of leaves, then added different species and combinations of amphipods. After a few weeks, I saw how much leaf litter the amphipods had eaten.

We found that there were some differences between amphipod species in how much they ate, and their preferences for different kinds of leaves based on nutrient content or toughness (that work is here). But the lab setting was quite different than real streams.

So I worked with two students from our limnoecology course (which includes both bachelors and masters students) to develop a field experiment that would test the same types of amphipod-leaf combinations in streams.

We built “cages” out of PVC pipe with 1-mm mesh over the ends. We would put amphipods and leaf litter inside the cages, zip tie them to a cement block, and place the cement block in a stream. We did this in two places in Eastern Switzerland, and with two different species of amphipod.

After two weeks, we pulled half the cement blocks and cages out. After four weeks, we pulled the other half out. Moving all those cement blocks around was pretty tough. I think of myself as strong and the two students were burly Swiss guys, but by the time we pulled the last cement block up a muddy stream bank I was ready to never do this type of experiment again.

Elvira and our two students, Marcel and Denis, with an experimental block in the stream. This was the stream with easy access; the other had a tall, steep bank that was a real haul to get in and out of.

Unfortunately, when I analyzed the data, it was clear that something had gone wrong. The data made no sense.

The control cages, with no amphipods in them, had lost more leaf litter than the ones with amphipods – which shouldn’t be the case since they only had bacteria and fungi decomposing them, whereas the amphipod cages had shredding invertebrates. And the cages we had removed after two weeks had lost more leaf litter than the ones we left in the stream for four weeks.

These are not the “results” you want to see.

We must have somewhere along the way made a mistake in labeling or putting material into cages, though I couldn’t see how. I tried to reconstruct what could have gone wrong, if labels could have gotten swapped or material misplaced. I don’t have an answer, but the data weren’t reliable. I couldn’t be sure that there was some ecological meaning behind the strange pattern. It could have just been human error.

I felt bad for the students I was working with, because it can be discouraging to do your first research project and not find any interesting results. It wasn’t the experience I wanted to have given them.

My supervisor and I agreed, with regret, that we had to redo the experiment. I was NOT HAPPY. I wasn’t mad at him, because I knew he was right, but I really didn’t want to do it. I’ve never been less excited to go do fieldwork.

But back out into the field I went with my cages and concrete blocks (and no students this time). In case we made more mistakes, we designed the experiment a bit differently. We had one really well-replicated timepoint instead of two timepoints with less replicates, and worked in one stream instead of two.

Begrudgingly, we hauled the blocks to the stream and then hauled them back out again.

Cages zip-tied to cement blocks and deployed in the stream. You can see the brown leaf litter inside the enclosure.

And then for 2 ½ years I ignored the data, until my dissertation was due, at which point I frantically analyzed it and turned it into a chapter.

The draft that I initially submitted (to the journal and in my dissertation) was not what I would call my best work. My FasterSkier colleague Gavin generously offered to do some copy-editing, and I was ashamed at how many mistakes he found. I hope he doesn’t think less of me. A fellow PhD student, Moritz, also read it for me, and had a lot of very prescient criticisms.

But through all of that and peer review, the paper improved. Even though it is not going to change the course of history, I’m glad that I put together the analyses and published it, because we found two kind of interesting things.

The first was about species differences. I had used two amphipod species in the experiment (separately, not mixed together). Per capita, one species ate a lot more/faster than the other… but that species was also twice as big as the other! So per biomass, the species had nearly identical consumption rates.

The metabolic theory of ecology is a powerful framework that explains a lot of patterns we see in the world. One of its rules is that metabolism does not scale linearly with body size (here’s a good blog post explainer of the theory and data and here’s the Wikipedia article). That is, an organism twice as big shouldn’t have twice the metabolic needs of a smaller organism. It should need some more energy, but not double.

This relates to my results because the consumption of leaf litter was directly fueling the amphipods’ metabolism. They may have gotten some energy and resources from elsewhere in the cages, but we didn’t put any plant material or other food sources in there. So we could expect to roughly substitute “consumption” for “metabolism” in this body size-metabolism relationship.

Metabolic theory was originally developed looking across all of life, from tiny organisms to elephants, so our twofold size difference among the two amphipod species isn’t that big. That makes it less surprising that the two species have the same per-biomass food consumption rates. But it’s still interesting.

The second interesting result had to do with how the two species fed when they were offered mixtures of different kinds of leaves. Some leaves are “better”, with higher nutrient contents, for example. Both species had consumed these leaves at high rates when they were offered those leaves alone, and had comparatively lower consumption rates when offered only poor-quality leaves.

In the mixtures, one species ate the “better” leaves even faster than would be expected based on the rates in single-species mixtures. That is, when offered better and worse food sources, they preferentially ate the better ones. The other species did not exhibit this preferential feeding behavior.

I thought this was mildly interesting, but I realized it was even cooler based on a comment from one of our peer reviewers. (S)he pointed out that this meant that streams inhabited by one species or the other might have different nutrient cycling patterns, if it was the species that preferentially ate all of the high-nutrient leaves, or not. We could link this to neat research by some other scientists. It was a truly helpful nudge in the peer review process.

So, while I had hated this project at one point, it’s finally published. And I think it was worth pushing through.

It was not a perfect project, but projects don’t have to be perfect for it to be worth telling their stories and sharing their data.

Planoiras Part 2: Seeking Confidence and Resilience

Note: This is the second of two posts about my racing in Lenzherheide, Switzerland, this weekend. For the first post, click here.

Saturday morning I woke up to one of those emails you don’t want to get at the start of the weekend. A paper I had submitted was rejected. Argh!

This happens all the time if you are an academic, and I think I have generally gotten slightly better at dealing with it. I was able to find some positives: the paper did go out for review (rather than getting rejected by the editor without review, something that is quite common), and all of the reviewers and editors agreed the premise was interesting. It’s not like they were telling me I, or my science, was garbage.

But it was still very disappointing. It was the chapter of my dissertation that I felt the most ownership over: the thing I felt like I had come up with all by myself and then convinced my supervisor and co-author to pursue, and that had turned out to have really interesting results. I had sent it to one of the journals I admire most in my field, and to have it published there would have felt like an incredible milestone.

Luckily, I was meeting some friends for a ski that morning, so after reading the reviewer comments over breakfast I hopped on the train and got some beautiful, sunshiney snow time. Glide. Good therapy.

Later in the day, I skyped with Steve, who is traveling for work. We chatted about a bunch of different things before I even remembered to mention the paper rejection. Then he asked if I was ready for my ski race the next day.

“I’m trying to be,” I said. “But it’s hard. The weather is going to be pretty terrible. It’s just blah.”

“You’re paper got rejected and now everything is painted gray,” he responded. “I know how you will be. The weather is gray and I don’t like it. The skiing is gray. This breakfast is gray, yuck. Gray gray gray.”

I laughed, because he was right, kind of. I definitely get that way. Sometimes when one bad thing happens, it leads me right down a chain of negativity until everything seems overwhelming, bad, and unsolvable. I can’t seem to see anything good in the world.

But I also laughed because it’s something I’m working on. For Christmas I bought myself Kara Goucher’s new book, “Strong.” It’s about building confidence. Some of the presentation is a little too girly for me, but there are aspects of the book that I love. It all works because Goucher is completely honest about her struggles, and she’s easily convincing when she relates how mental training helped her.

One section is about reframing negative thoughts and turning them into strengths, and this is something I really liked.

Here’s an example. These days when I go to a ski race, I’m aware that I probably don’t train as much as most of the people who are around me – people who look all pro in their shiny suits, who own the newest skis and boots and poles, and who probably poured a couple hundred Francs into their wax jobs. I certainly don’t have as much time on snow, because I live in Zurich, and most of them live much closer to the mountains, if not actually in the mountains.

As I see all these people warming up and putting their skis on the line, sometimes I feel like a complete imposter. What am I doing here!? These people are so much better prepared than me! Look how fit they all look!

And, well, some of them are better trained. But physical preparation is not the only thing that makes you go fast. You could have done the best training this year, but if you show up at a race and don’t work hard, you’re probably not going to reach your goals.

I work really, really hard in races in order to make up for my lack of ski-specific (or some years even total…) training. I try to target my effort in the ways that will help the most, take advantage of my love of downhills and corners, and attempt to finish the race having spent every bit of energy I have.

And so when there was an exercise in “Strong” to write down a common negative thought you have and reframe it, this is what I picked.

“Everyone here has done better training than you,” I wrote down for the negative thought.

“You know how to get the most out of the training you’ve done,” I wrote down as a new mantra.

I hadn’t really thought about things that way before, but it felt good.

Did it help me in my race on Sunday? I don’t know. The race still wasn’t that fun, but I did stay focused even though I was performing worse than I had hoped. N=1. Maybe I would have anyway.

A few days later, I was listening to the Science of Ultra podcast when an episode came on about mental training. The host describing the RISE approach: recognize, identify, switch, and execute. His example for recognizing your emotions hit home.

“First, recognize the thoughts you’re having. Be aware of negative, unhelpful, and destructive thoughts…. maybe you’re going much slower than expected, and disappointed that you’re not going to make your goal time, or embarrassed that so many people are passing you.”

As I wrote in part 1 of this blog post, I need to clarify why it is that I race. Skiing doesn’t really have goal times (one of the things I love about it!) and you never know who will show up at a given marathon. Setting results-based goals seems particularly futile when you’re in a field of competitors you don’t know anything about, and I wouldn’t say that I am driven to race because I think I’ll do “well”. I don’t train full time. I’m getting worse at skiing. I know that.

And yet, that embarrassment when lots of people pass me is real. That’s something I need to recognize. Even though results are not the main reason I do this, it feels bad.

What’s funny about all of this is that I have been thinking about mental resilience a lot lately, but not because of sports. Instead, I’ve been thinking about it in my life as a scientist.

Finishing my dissertation was really hard, and I still don’t feel like I’m fully recovered. It took a lot out of me intellectually and emotionally. Two months after handing it in, I sit down at the computer to write on one of the other papers I owe my boss and I just can’t. The words don’t come out. The ideas I had disappear.

And even before that, sometimes I get into these negative spirals. Everything gets painted gray. Science has highs and lows and sometimes I feel like I’m swinging wildly between them from one day to the next. Going through something like a dissertation doesn’t help you deal with all the “normal” lows like getting a paper rejected.

I love science, and I want to keep doing it. But I need to do everything I can to be healthy.

And so when I was at the British Ecological Society annual meeting in Birmingham, England, in December, I headed to a lunchtime workshop about mental resilience in academia.

I was relieved to see that the room was full of people. I wasn’t weak for thinking I needed help in this department. Apparently, this was something that everyone thought sounded like a lifeline. Including people I recognized and admired.

Some things we talked about I already knew. Others I hadn’t thought about, or not in the same way. One of the latter was the instruction to recognize and accept your emotions.

“Sometimes we think that resilience is bouncing back, getting over it and soldiering on,” the workshop organizer said. “But there’s a danger in that. You need to recognize and deal with your emotions, with how you feel about the bad things you’re experiencing. If you bury them in an effort to just ‘soldier on’, that’s not going to work in the long run. That’s not resilience.”

All of these things – confidence, recognition, resilience – seem tied together for me, even though I’m not doing a good job of explaining why. But even though I’m exhausted by my PhD and frequently overwhelmed, I think that thinking about all these things has made me more balanced in the last month or so.

Kara Goucher’s book is about keeping a confidence journal. The premise is that every day, you write down something specific, that you will remember immediately, and that will make you feel more confident when you go back and read it later.

I’ve enjoyed keeping a confidence journal so far. I always write something about the training/exercise session I did each day (or what was good about resting instead of training), and some days I write about science, too. Both sides of my life are places where I need to go back and find some extra confidence sometimes.

My weekend started off with a rejection, but it didn’t have to end that way. I recognized my disappointment and frustration with racing, but found the positive side in my journal entry.

My Ford Sayre ski coach, Scottie Eliassen, always had us talk about one thing that went well and one thing to improve on for next time after every race. This is what I channeled.

“I didn’t go fast, but dang I worked hard. My threshold HR is 177 and my average for the 25 k race was 175. Despite the snowstorm and feeling bad, I hit my process goal of not getting complacent and giving up. I kept pushing.”

Next time I’m about to race and I begin worrying that everyone is more fit than I am, maybe reading that message will help. I’ve been doing this for a long time and I know how to get the best out of the training I’ve done.

Book Review: Spying On Whales by Nick Pyenson

My aunt sends us books for Christmas every year, a.k.a. the best kind of holiday presents. In this year’s box was Spying on Whales by Nick Pyenson, and I sat down to start reading it that very night.

In some ways, I’m the perfect audience for this book. I’m interested in nature, natural history, and evolution, but I actually didn’t know much of anything about whales.

img_3489

Whale territory. You just can’t see them because they are under the water – as Pyenson discusses, this is one of the things that make these giant animals so mysterious and compelling.

In November I had gone on a whale-watching boat ride in northern Norway, and more or less everything I knew came from the Ukrainian guide on the boat. It wasn’t much, but before and after the trip, I thought whales were cool, when I thought about them at all.

If you are already a little obsessed with whales, then I think you’ll like the book too. All the facts are there for you to nerd out on.

9780735224568But Pyenson writes impressively well, and the book is never bogged down in this nerdery. Instead, it’s fun and adventurous. Chapters sometimes end in cliffhangers – I certainly couldn’t stop reading. And at times, it’s quite a moving read.

In the prologue, Pyenson describes how the Voyager 1 and Voyager 2 spacecraft are carrying whalesong into the outer reaches of the solar system. We may not understand what whales are saying, but we intuitively feel that they are intelligent and powerful – and that their communication is not only beautiful but probably important.

“They’re so compelling that we imagine aliens might find them interesting,” Pyenson writes of whales.

And yet, he continues, we know very little about whales, either the ones that live in our oceans today or the whales of the past.

I’ll admit: I didn’t actually really realize that.

Pyenson fills in details that I never knew I didn’t know, and certainly didn’t realize were so recently discovered. He discusses what things we still don’t know, and what mysteries will be exciting to solve. This book is filled with behavior, anatomy, and evolutionary history, as well as discussions of how all three connect to make a modern whale.

“Nothing in biology makes sense except in the light of evolution”, foundational biologist Theodosius Dobzhansky wrote as the title of one of his essays.

In my fields – ecology and evolutionary biology – this is tossed back and forth. “Nothing in ecology makes sense except in the light of evolution,” an evolutionary biologist will say, scoffing at ecology as boring (yes, this has happened to me, in person).

“Well, nothing in evolution makes sense except in the light of ecology,” a ecologist might retort.

Pyenson’s book is a great demonstration that both are true. In trying to piece together – literally, based on fossil fragments – how whales went from living on dry land to being the biggest animals in the oceans and indeed on the planet, ever, both ecology and evolution are needed. The two are woven together throughout the book as Pyenson describes working with one collaborator after another.

“The trip was an opportunity to push at the questions sitting on the edges of our disciplines, at the intersection of Ari’s understanding of behavior and local ecology, Jeremy’s grasp of physiology and biomechanics, and my background in paleontology and Earth history,” Pyenson writes at one point. “The basic questions about how whales do that they do require all these fields of understanding, and I’ve always thought the best way to answer them was through this Venn diagram of disciplines and personalities.”

Now that’s a scientific approach I admire, and he demonstrates that it’s fun, too. Minus the part at the whale slaughterhouse.

How does this approach deliver answers? Here’s an example question. If whales have gradually evolved to be bigger and bigger, why aren’t they even bigger still? That evolutionary question has an ecological answer.

The first two-thirds of the book are about the past and the present of whales. But my favorite part was the last third, about the future of whales, and of us. Whales live a very long time, sometimes two hundred years. There is a beautiful description of all that a 200-year-old whale has seen in its lifetime.

And then, a description of what today’s whales might see in the future, things that you and I will not live to see.

“Any bowheads living today do so in a liminal gap between a familiar past and a potentially unrecognizable future,” Pyenson writes of climate change in the Arctic. “A bowhead calf born today will live in an Arctic that, by the next century, will be a different world than that experienced by all of its ancestors.”

Maybe that sounds trivial – like, duh, climate change is happening fast – but after reading a hundred pages about all of the different environments that whales have encountered through their long evolutionary history, it is powerful when you reach this point.

And that is one of the things that kept me reading this book. It is peppered with fun facts – large baleen whales grow at a rate of a hundred pounds a day before reaching maturity, dang, that seems impossible! – but also with profound observations and good writing.

Finally, as a field ecologist, I really enjoyed Pyenson’s descriptions of fieldwork trips. He takes us to Chile, Panama, Iceland, and Antarctica, as well as more familiar-to-Americans spots like California and North Carolina. All of his descriptions of the challenges and successes of fieldwork, the camaraderie between colleagues, and even that one wonderful-but-definitely-weirdo collaborator (we all have at least one), feel authentic.

If you like science and nature writing, and compelling nonfiction in general, I think you’ll like this book. I did. Thanks Lizzie!

My #365papers Experiment in 2018

This year, based on initiatives by some other ecologists in the past, I embarked on the #365papers challenge. The idea of the challenge is that in academia, we end up skimming a lot of material in papers: we jump to the figures, or look for a specific part of the methods or one line of the results we need. Instead, this challenge urged people to read deeper. Every day, they should read a whole paper.

(Jacquelyn Gill and Meghan Duffy launched the initiative and wrote about their first years of it. But #365papers is now not just in ecology, but in other academic fields. Some of the past recaps I read were by Anne Jefferson, Joshua Drew, Elina Mäntylä, and Caitlin MacKenzie. Caitlin’s was probably the post that catalyzed m to do the challenge.)

I knew that 365 papers was too ambitious for me, and that I wouldn’t (and didn’t want to!) read on the weekends, for example. I decided to try nevertheless to read a paper every weekday in 2018, which would be 261 days total.

In the end, I clocked in at 217 papers (I read more than that, but see below for what I counted as a “paper” for this challenge) – not bad! I tweeted links to all the papers, so you can see my list via this Twitter search. I can confidently say that I have never read so many papers in a year.

In fact, I am guessing that this is more papers than I have read in their entirety (not skimming or extracting, as mentioned above), in my total career before 2018. That’s embarrassing to admit but I am guessing it’s not that unusual. (What do you think, if we’re all being honest here?)

This was a great exercise. I learned so much about writing, for one thing – there’s no better way to learn to write than to read a lot.

But the thing that was most exciting was that I read a lot more, and a lot of fun pieces. I had gotten to a place where there were so many papers that I felt I had to read for my own work, that I would just look at the pile, blanche, and put it off for later. Reading had become a chore, not something fun.

Titles_wordle

A Wordle of the paper titles. On my website it says I am a community and ecosystem ecologist, and I guess my reading choices reflect that! (I’d be interested to make a Wordle based on the abstracts, to see if there are more diverse words than the ones we choose for titles – but I didn’t make time to extract all the abstracts for that.)

That’s not a great way to do research, and luckily the challenge changed my reading status quo. If I was reading every day, I reasoned, then not every paper had to be directly related to my work as a community ecologist. There would be ample time for that, but I could also read things that simply looked interesting. And I did! I devoured Table of Contents emails from journals with glee and read about all sorts of things – evolution, the physical science of climate change, remote sensing.

These papers, despite seeming like frivolous choices, taught me a lot about science. Just because they were not about exactly what I was researching does not mean they did not inform how I think about things. This was incredibly valuable. We get stuck in our subfields, on our PhD projects, in our own little bubbles. Seeing things from a different angle is great and can catalyze new ideas or different framing of results. Things that didn’t make sense might make sense in a different light.

But I also did read lots of papers directly related to what I was working on. I think I could only do that because it no longer felt like a chore, like a big stack of paper sitting on the corner of my desk glaring at me. This challenge freed me, as strange as that sounds given the time commitment!

And finally, I tweeted each paper, and tagged the authors if possible. This helped me make some new connections and, often, learn about even more cool research. It helped me put faces to names at conferences and gave me the courage to strike up conversations. The social aspect of this challenge was fun and also probably pretty useful in the long run.

For all of the reasons I just mentioned, I would highly recommend this challenge to other academics. (It’s not just in ecology – if you look at the #365papers hashtag on Twitter, there are a lot of different people in different fields taking up the challenge.) Does 365 or 261 papers sound like too many? Set a different goal.  But make it ambitious enough that you are challenging yourself. For me, I found that making it a daily habit was key, because then it doesn’t feel like something you have to schedule (or something you can put off) – you just do it. Then sit down and read a whole paper, with focus and attention to detail. If you like it, why is that? Is the topic of interest to you? The writing good? The analyses particularly appropriate and well-explained? Is it that the visuals add a lot to the paper? Are the hypotheses (and alternative hypotheses) identified clearly, making it easier to follow? Or, if you don’t like it, why is that? Is it the science, or the presentation? What would you do differently?

One thing I didn’t nail down was how to keep notes. I read on paper, so I would highlight important or relevant bits or references to look up. But I don’t have a great system for how to transfer this to Evernote (where I keep papers’ abstracts linked to their online versions, each tagged in topic categories). In the beginning I was adding photos of each part of the paper I had highlighted to its note, but this was too time-consuming and I gave up. In the end it was like, if I had time, I would manually re-type my reading notes into Evernote, and if not, I wouldn’t. I do think the notes are valuable and important to have searchable, so this probably limits the utility of all that reading a little bit. It’s something I will think how to improve for next year. The biggest challenge is time.

In addition to reading a lot, I kept track of some minimal data about each paper I read. I’ll present that below, in a few sections:

  • Where (journals) and when the papers were published
  • Who wrote them – first authorship (gender, nationality, location)
  • A few thoughts about last authorship
  • Grades I assigned the papers when reading – potential biases (had I eaten lunch yet!?) and the three papers I thought were best at the time I read them

I plan to try this challenge again next year, and the data that I summarize will probably inform how I go about it. I’ll discuss that a little at the very end.

What Did I Count as One of My 365  261  217 Papers?

First, some methodological details. For this effort, I didn’t count drafts of papers that I was a co-author on, although that would have upped the number or papers quite a bit because I have been working on a lot of collaborative projects this year. I also didn’t count reading chapters of colleagues’ theses, or a chapter of a book draft. And I didn’t count book chapters, although I did read a few academic books, among them Dennis Chitty’s Do Lemmings Commit Suicide, Mathew Leibold & Jonathan Chase’s Metacommunity Ecology, Andy Friedland and Carol Folt’s Writing Successful Science Proposals, and a book about R Markdown. I started but haven’t finished Mark McPeek’s Evolutionary Community Ecology.

I did count manuscripts that I read for peer review.

Where The Papers Were Published

I didn’t go into this challenge with a specific idea of what I wanted to read. I find papers primarily through Table of Contents alerts, but also through Twitter, references in other papers, and searches for specific topics while I was working on my dissertation or on research proposals. This biases the papers I read to be more likely to be by people I’m already aware of or in journals I already read. Not entirely, but substantially.

We also have a “journal club” in our Altermatt group lab meeting which doesn’t function like a standard one, but instead each person is assigned one or two journals to “follow” and we rotate through each person summarizing the interesting papers in “their” journals once every few months (the cycle length depends on the number of people in the lab at a given time). That’s a good way to notice papers that might be good to read, and since we are a pretty diverse lab in terms of research topics, introduces some novelty. I think it’s a clever idea by my supervisor, Florian.

Given that I wasn’t seeking out papers in a very systematic way, I wasn’t really sure what the final balance between different journals and types of journals would be at the end of the year. The table below shows the number of papers for each of the 63 (!) journals that I read from. That’s more journals than I was expecting! (Alphabetical within each count category)

In addition, I read one preprint on BioRxiv.

I don’t necessarily think that Nature papers are the best ecology out there; that’s not why it tops the list. Seeing EcologyOikos, and Ecology Lettersas the next best-represented journals is probably a better representation of my interests.

But, I do think that Nature (and Science, which had just a few fewer papers) papers get a lot of attention and must have been chosen for a reason (am I naive there?). There are not so many of them in my field and I do try to read them to gauge what other people seem to see as the most important topics. I also read them because it exposes me to research tangential to my field or even entirely in other fields – which I wouldn’t find in ecology journals, but which are important to my overall understanding of my science.

I’m pleased that Ecology & Evolution is one of my top-read journals, because it indicates (along with the rest of the list) that I’m not only reading things for novelty/high-profile science, but also more mechanistic papers that are important to my work even if they aren’t so sexy per se. A lot of the journals pretty high up the list are just good ecology journals with a wide range of content.

There are a lot of aquatic-specific journals on the list, which reflects me trying to get background on my specific research. But there are also some plant journals on the list, either because I’m still interested in plant community ecology despite being in freshwater for the duration of my PhD, or because they are about community ecology topics that are useful to all ecology. It will be interesting to see if the aquatic journals stay well-represented when I shift to my next research project in a postdoc.

Society journals (from the Ecological Society of America, Nordic Society Oikos, British Ecological Society, and American Society of Naturalists, among others) are well represented. Thanks, scientific societies!

When The Papers Were Published

The vast, vast majority of papers I read were published very recently. Or, well, let’s be honest, because this is academic publishing: who knows when they were written? I didn’t systematically track this, but definitely noticed some were accepted months or maybe even a year before final paginated publication. And they were likely written long before that. But you get the point. As for the publication year, that’s below.

year_published

This data was not a surprise from me as a fair amount of my paper choices come from seeing journal table of contents alerts. I probably should read more older papers though.

Who Wrote the Papers: First Authors

Okay, on to the authors. Who were they? As I mentioned for journals, I didn’t systematically choose what I was reading, so I was curious what the gender and geographic breakdown of the authors would be. Since I didn’t very consciously try to read lots of papers authored by women, people of color, or people outside of North America and Europe, I guess I expected that the gender of first authors to be male-skewed, white, and from those continents. I wasn’t actively trying to counteract bias in this part of the process, so I expected to see evidence of it.

I did my best to find the gender of all first authors. Of those for which this was deducible based on homepages, Twitter profiles listing pronouns, in-person meetings at conferences, etc.,:

  • 59 first authors were women
  • 155 first authors were men
  • 2 papers had joint first authors
  • 1 paper I peer-reviewed was double-blind (authorship unknown to me)

I’m fairly troubled by this. I certainly wasn’t going out of my way to read papers by men, and I didn’t think it would be this skewed when I did a final tally. If I want to support women scientists by reading their work – and then citing it, sharing it with a colleague, contacting them about it, starting discussions, etc. – I am going to have to be a lot more deliberate. I want to learn about other women scientists’ ideas! They have a lot of great ones. I’m going to try harder in the future. Or, really, I’m going to try in the future – as mentioned, I was not intentionally reading or not reading women this past year.

I initially tried to track whether authors were people of color, but it’s just too fraught for me to infer from Googling. I don’t want to misrepresent people. But I can say that the number of authors who were POC was certainly quite low.

I did, however, take some geographic stats: where (to the best of my Googling abilities) authors originally came from, and where their primary affiliation listed on the paper was located.

For the authors for whom I could identify nationality based on websites, CVs, etc., 31 countries were represented.

FA nationality

The authors were numerically dominated by native English speakers, but those had relative geographic diversity, coming from the US, Canada, the UK, Ireland, Australia, and New Zealand (I’m not sure if English is the first language of the South African author). 15 different European nationalities were represented. There were a number of authors from Brazil, and one each from Chile, Colombia, and Ecuador, as well as Central America being represented by a Guatemalan. Maybe a surprise was that Chinese authors were underrepresented, either from Chinese institutions (see below) or those outside China; there were just five. There are many countries from which there are great scientists which are not represented in this dataset.

When it came to institutional country, the field narrowed to 24 countries plus the Isle of Man.

FA_inst

While there were 78 American first authors, 90 first authors came from American universities/institutions. In Europe, Denmark, Sweden and Switzerland gobbled up some of the institutional affiliations despite having low numbers of first authors originally from those countries (this is very consistent with my experience in those places).

(Note: it would have been really nice to make a riverplot showing how authors moved between countries, but I was too lazy to build a transition matrix. Sorry.)

This isn’t really surprising, the consolidation into fewer countries. It reflects that while small countries have great scientists, they often don’t have as many resources to have great research funding or many universities. Some places, even those with traditionally strong academic institutions, are simply going through austerity measures. I think of many Europeans I know who decided that leaving their countries – Portugal, Spain, the Baltics and Balkans, and other places – was their best bet to be able to do the research they wanted to do, and have a job. I think of others, notably a friend in Slovenia, who is staying there because he loves it, but whose opportunities are probably curtailed because of that.

I’d like to read more widely in terms of institutional location and author nationality, but it’s a bit overwhelming to make a solid plan. Reading more women is fairly straightforward. But when I think of all the places with good science but where I didn’t read a single paper, there are a lot of them. I can only read so many papers! So part of it will be recognizing that I can make an effort to read more diversely but I’m not going to solve bias in science just with my reading project. I need to make an effort that is meaningful, and then be okay with what it doesn’t accomplish.

Also, I don’t always know the gender, race, or nationality of an author before I Google them – this past year, I only did that after I read the papers. I might need to sometimes reverse that process, perhaps?

Do you have other ideas of how to tackle this? I’d love suggestions if anyone has them.

One thought is to more deliberately read from the non-North American, non-European authors in the journals I already read from. I already know I like the papers those editorial teams select. This would probably be the least amount of extra work required to diversify my reading, because I could stick to the same method of choosing papers (table of contents alerts), but execute differently on those tables of contents.

And a Bit About the Last Authors

I did not collect as detailed information about the last authors of each paper, but I did collect some. A big topic in academia is that women get fewer and fewer the higher you go in the academic hierarchy. I wondered if that was true in the papers I was reading.

There were fewer last authors because some papers were single-author. Of those that were multi-author, I filtered the dataset to look at only those where last-authorship seemed to denote seniority (based on author contribution statements, lab composition and relationships between authors, etc.) rather than being alphabetical or based on something else (on some papers with very many authors, all the senior authors were listed at the front of the author list). Of these,

  • 19 senior last authors were women
  • 105 senior last authors were men

Yikes!

That’s all one can say! Yikes!

Like the first authors, the last authors came from 31 different countries… but some different ones were represented (Venezuela, Serbia, India). They represented institutions in a few more places than the first authors, 28 different countries vs. 24 for first/single authors. I’m not sure what to make of that, especially since this is from a smaller subset of papers (since the single-author papers were removed), but obviously collaborative research and writing is alive and well.

Ratings and Favorite Papers

Right after I read each paper, I assigned it a letter grade. Looking back through my record keeping, I am less and less convinced that this is really meaningful. I think it had to do a lot with my mindset at the time, among other things. Did I just have a stressful meeting? Was I impatient to finish my reading and go home? Was I tired? Maybe I was less receptive to what I was reading. Or conversely, maybe if I was tired and a little distracted I was less likely to notice flaws in the paper. Who knows. Anyway, “B” was the grade I most frequently assigned.

grades

I didn’t keep detailed notes of why I felt different grades were merited, but I can make a few generalizations. Quite a number of the papers I gave poor grades were because I didn’t find methods to be well enough explained. I either couldn’t follow what the authors did, or maybe important statistical information wasn’t even included (or only in the supplementary information when I thought it was so essential to understanding the work that it really needed to be brought to the center). In particular this included some papers using fancy and cutting edge methods… just using those statistics or analysis techniques doesn’t make your paper magic. You still need to say what those analyses show and what they mean ecologically, and convince me that the fancy stats actually lead to a better understanding of what’s going on!

In some ways this is not authors’ faults – journals are often pressing for shorter word counts, and some don’t even publish methods in the main text, which is a total pain if you’re a reader. Also, it’s one of the biggest things I struggle with when writing – you know perfectly well what you did, and it can be hard to see that for an outsider your methods description seem incomplete. I get it! Reading papers where you don’t understand the methods is always a good cue to think about how you present your own work.

I assigned three papers grades of “A+”. Were they better than the ones I deemed “A”s? I’m not sure, but at the time, whether because of my general mood or their true brilliance, I sure thought they were great. They were:

I read a lot of other great papers too! But looking back, I can say that these were among my favorites, all for different reasons. I could go and add more papers to a “best-of” list but I’ll just leave it at that.

Recap!

Besides all the great reasons to do this challenge that I mentioned in the opening, this was pretty interesting data to delve into. I think I will try to keep doing the challenge in 2019, and I am currently thinking about how I choose which papers to read and if there are good strategies to read more diverse authors. I’m happy with the diversity of research that I read, but I would be happier if the voices describing that research were more diverse, to reflect the diversity of scientists in our world.

Do you have ideas about that? Comment below.

This was the final year of my PhD, and so in some ways a great time to do a reading challenge. It probably would have been more helpful if I had done this in the first year of my PhD, but hey, too late now. This year I wasn’t doing lab work, just writing and analyzing, so it was easy to fit in a lot of reading. It’s not good to stare at a screen writing all day, and I prefer to read on paper, so it was often a welcome break.

I don’t know what my work life will be like next year, so I will see how many papers I end up reading. It could be more, as I start a new project and need to get up to speed on a new subfield. Or it could be less as my working habits change. I’ll just do my best and adapt.

Finally, I’m thinking about whether there’s additional data I should track for next year’s challenge. Whether there is a difference between first and corresponding authors might be interesting. I’d welcome other suggestions too, but only if they don’t take much work to extract!