When I started my first faculty position at Georgia Tech, I felt like I was juggling as fast as I could; every time it felt like I was starting to get a hang of things, a new ball would get tossed in. I mentioned this at some point to someone there who said: the key is to remember that some balls are glass and some are rubber.
I was thinking about that juggling metaphor again recently because I was involved in a discussion with other faculty about how we all have too much to do. There was some discussion of the root causes of this, including a major decline in administrative support and more expectations. Obviously those are huge issues that are worthy of much more thought and systemic solutions. But there was also a discussion of what we can do individually in the short term as we all struggle with this. At some point, someone said something to the effect of, “you need to accept that you are never going to be able to do it all, and you have to accept that some things are just going to go off the edge of the cliff”.
In November 2016, I did a poll and wrote a post about how overwhelming email can be. About a quarter of respondents to the poll said they rarely or never feel overwhelmed by email. I am not one of them. I’m in the majority that are overwhelmed by email at least some of the time. Other notable poll findings were:
- people with more emails in their inbox were more likely to feel overwhelmed by email, and
- faculty were more likely than grad students and postdocs to have a lot of work-related emails in their inbox.
At the time I wrote up the results of that poll, one of the main strategies I settled on for trying to be less overwhelmed by email was to batch my inbox, so that my emails only arrived once or twice a day. The idea is to treat email like regular mail – a thing that arrives at a given time and that you deal with in a batch (or, um, toss on the table and leave there for a while).
After that poll, I switched to using batched inbox to batch my mail. (It was free when I signed up, but I don’t think it is now.) It was amazing how much less overwhelming email was! I wasn’t getting distracted by emails as they arrived in my inbox, I found I actually got less email than I thought, and dealing with them in batches really reduced the amount of time and energy I spent on email. (I’m not alone. Arjun Raj has a post about how much email filtering helped his peace of mind.)
So, I was a fan. But then I started “cheating” and checking the folder where the batched emails hang out until they get dumped into the inbox. And, in the years since then, I have gone through cycles where I recommit to batching, think “OMG, why did I ever stop doing this?!?! Dealing with emails in bulk is so much better!!!”, then start sliding and going back to more of a system of dealing with emails as they come in (why? why do I do this?!? I know it’s counterproductive!), then get completely overwhelmed by emails, then at some point remember that batching is supposed to help with that, at which point I recommit to it and once again think “OMG, why did I ever stop doing this?!?!”
I recently did a poll asking readers about their experiences with manuscript rejections. This was based on thinking about different submission strategies, including wondering about what the “right” amount of rejection is. In this post, I lay out the big picture results, and then end by asking about what further analyses you’re interested in.
There are lots of figures below, but here’s my summary of the key results:
- respondents to this poll reported a lower acceptance rate at the first journal to which they submitted a manuscript (48.4%) than in the recent Paine & Fox survey (64.8%). They had vastly more respondents (over 12,000!!!), so I trust their number more; other potential factors that might also contribute are discussed below.
- it’s not uncommon for people to need to submit a paper to 3 or more journals before it’s accepted.
- it’s surprisingly common (at least to me) for people to take the “aim high, then drop if rejected” strategy
- people are submitting to stretch journals pretty often—and sometimes it pays off
- there’s a decent amount of uncertainty in terms of how well a manuscript fits a particular journal (on the part of authors, reviewers, and/or editors). This suggests that the concluding advice of Paine & Fox (“We therefore recommend that authors reduce publication delays by choosing journals appropriate to the significance of their research.”) is sometimes easier said than done.
- people aren’t totally giving up on manuscripts as often as I might have thought they might (but this might be explained by the demographics of the poll respondents)
The last experiment I did as a graduate student was one where I wanted to experimentally test the effect of predation on parasitism. To do this, I set up large (5,000 L) whole water column enclosures (more commonly called “bags”) in a local lake. These are really labor intensive, meaning I could only have about 10 experimental units. I decided to use a replicated regression design, with two replicates of each of five predation levels. These were going to be arranged in two spatial blocks (linear “rafts” of bags), each with one replicate of each predation level treatment.
Left: two experimental rafts; right: a close up of one of the rafts, showing the five different bag enclosures
As I got ready to set up the experiment, my advisor asked me how I was going to decide how to arrange the bags. I confidently replied that I was going to randomize them within each block. I mean, that’s obviously how you should assign treatments for an experiment, right? My advisor then asked what I would do if I ended up with the two lowest predation treatments at one end and the two highest predation treatments at the other end of the raft. I paused, and then said something like, “Um, I guess I’d re-randomize?”
This taught me an important experimental design lesson: interspersing treatments is more important than randomizing them. This is especially true when there are relatively small numbers of experimental units*, which is often the case for field experiments. In this case, randomly assigning things is likely to lead to clustering of treatments in a way that could be problematic.
When I was at the biology19 meetings recently, someone said something to me that I can’t stop thinking about: a student’s first manuscript should get sent to a journal where it will be accepted without much of a struggle; the second submission should be more of a struggle, but should get accepted at the first journal to which it was submitted; the third should go somewhere where it gets rejected. The person who said this, Hanna Kokko, acknowledged this was somewhat tongue-in-cheek, and that many factors will end up influencing where someone submits a given manuscript; her real approach is to respect the first author’s own wishes, after a discussion of the pros and cons of different options. But her tongue-in-cheek recommendation is motivated by the recognition that rejections can be a huge hit to one’s confidence, especially when someone is just starting out. I’ve seen (and personally experienced) the enormous confidence hit that can come from serial rejections of a manuscript, again, especially when one is just starting out. So, trying to figure out a strategy to reduce the potential for a big ego blow (while learning to deal with rejection too—but not before one has succeeded twice) makes a lot of sense to me.
Last Monday, I faced a post-travel inbox filled with emails that needed replies. Some of them were invitations for things that would take up my time, but that seemed interesting or important or valuable or all three. And, then, of course, there were all the other things I needed to do as part of my job – editing manuscripts, writing letters of recommendation, sending emails to get people access to the lab, analyzing data, etc. And it was also the day where my post on seeing a therapist appeared, which led to lots of interactions on social media, via text, and through email. All of that led me to revisit a question that I am constantly asking myself, and that I surely will never stop asking myself: how should I spend my work time?
I couldn’t get this out of my head, and, as I walked to daycare, I realized that there are three questions I should consider as I evaluate whether to do something:
- Is it officially part of my job?
- Am I particularly good at it?
- Do I enjoy doing it?
I thought about how, ideally, I should try to prioritize things where the answer would be “yes” for all three. And I thought about how I spend a lot of time on things where the answer to all three of those questions is “no”.
When I got to daycare, I knew I wanted to think about this more, and was worried I would forget it. So, I pulled out my notebook in the daycare lobby, propped it on top of the stroller, and drew this:
Last week, I had the honor of being a plenary speaker at the biology19 conference in Zurich. This is an annual meeting of Swiss organismal biologists, where most of the attendees are Swiss graduate students and postdocs. When I first thought about my talk, I debated whether to use the last part to talk about mental health in academia, especially since I am on sabbatical this year and some of my sabbatical projects relate to student mental health. But, when I prepared my talk, I decided to just stick with my normal research.
On the first day of the meeting, I had several conversations with people that veered towards student mental health, which made me wonder if I should have included mental health in my talk. Then, the afternoon plenary on the first day was given by Virpi Lummaa. She gave a really interesting talk about her research, but pivoted at the end to talk more about the human side of science. It was inspirational. So inspirational that I went back to the hotel and changed the end of my talk to focus on mental health in academia. When I decided to make that change, I made another decision: I would admit to a room full of hundreds of my colleagues that I see a therapist regularly, and that doing that is essential to my ability to do everything I do, including my science.
As I’ve blogged about a few times recently, I have been working with a couple of collaborators, Susan Cheng and JW Hammond, on a project aimed at understanding student views on climate change. As part of this, I’ve been thinking about what we teach and how we teach it, and also about a common challenge faced by instructors who teach about climate change: how do we convey the severity of climate change without leaving students feeling depressed and hopeless?
As I was working on the manuscript describing the first set of our results, I typed a sentence to that effect, and then just sat and stared at the computer for a bit, wondering “Is it my responsibility as a biology instructor to leave students empowered and with a sense of purpose?”
Some ecologists start their careers planning to study climate change, and others make a decision to pivot towards that line of research. But something I find fascinating is that there are ecologists, myself included, who didn’t necessarily set out to study climate change, but who are accidental climate change biologists. To give just one example: if you work on a time series on natural populations, communities, or ecosystems that extends more than a few years, chances are you’ve found that climate change is now a part of what you’re studying.
I’ve thought about this over the years as projects we work on that started out as basic research into host-parasite interactions end up relating to climate change. Some links are obvious—wanting to understand how temperature influences host-parasite interactions leads pretty naturally to thinking about how climate change will influence host-parasite interactions. Some links are less obvious—for example, we wondered whether the light environment might be influencing when and where we saw parasite outbreaks. As I recall, our initial interest in this was not related to climate change. But lakes are getting browner, in part due climate change, so any work we do on how lake light levels influence disease naturally links with climate change. And we now have some data on host-parasite interactions in lakes that spans 1-2 decades. Once you’re into decadal time scales, you have to consider the impact of climate change on what you’re seeing.
I’ve also thought about this in terms of some projects I didn’t work on. When I started grad school, one of the projects I was thinking of working on related to what was going on under the ice in lakes in winter, and how things like snow cover influenced that. So, when I saw news articles about a new study showing that there will be an “extensive loss of lake ice…within the next generation”, I thought back to those grad school plans to work on lake ice & snow cover. My recollection is that my interest in that project was mainly wanting to understand the basic biology of lakes, but clearly it would have ended up being a study of climate change if I’d pursued it.
Based on conversations with colleagues, I know I’m not alone in coming to realize that I am an accidental climate change biologist.
So, I’m curious: for my fellow accidental climate change scientists, when did you realize you were studying climate change?
Last week, I wrote a post where I talked about how my training in evolutionary ecology led me to try reaction norms (that is, paired line plots) for plotting paired Likert data. I had already tried a few other options, but didn’t include them in that post, and I got some feedback on that post that gave me more ideas. There was also a request for code on how to actually generate those plots. So, this post shows four different ways of visualizing individual-level responses to paired Likert-scale questions (paired line plots, dot plots, mosaic plots, and heat maps). It does that for two different comparisons, leading me to the conclusion that the type of plot that works best will depend on your data. I’d love to hear which ones you think work best — there are polls where you can vote for your favorite! And, if you’re working on similar data and want to see code, there’s an associated Github repo, but it comes with the disclaimer that my code is good enough, but definitely not elegant.