Note from Jeremy: this post is by Meg and originally ran in 2015 under the title “I have data, ESA, I promise!” I’m re-upping it because it’s timely.
A few years ago, I asked a senior colleague for feedback on something I’d written. He agreed, and a couple of days later, sent an email saying “Is there a good time to discuss this?” I immediately thought it must mean he’d really hated what I’d written. I replied, suggesting a few times in the next couple of days. In his reply, he choose the latest of those times, saying he needed more time to mull it over. That confirmed my worst fears – it was so bad he needed extra time to figure out how to tell me how bad it was! After spending some time getting no other work done because I was so distracted, I decided to write to say that, based on his emails, I was worried that there was a major problem with what I’d written. He replied immediately saying not to worry, that it read very well, and that he just had a few ideas that he thought would be easier to discuss in person.
I was thinking of this situation again recently when I was emailing a student in my lab. She’d emailed about a proposal she’s working on, laying out two different options for a fellowship proposal she’s working on. My thinking, when reading the ideas, was that both of them could work, but that there might also be other options, and that it would probably be best to discuss all the options in person. Looking at my schedule and comparing with hers, I could see that we wouldn’t be able to meet until the end of the week. So, I initially wrote a reply that said, “Can we meet Friday at 11 to chat about this?” In the brief pause before hitting send, I realized that, if I were in her shoes, I would spend the rest of the week trying to interpret what that email had meant, most likely assuming it meant something bad. I then realized that could be easily addressed by instead saying something like, “Both of these ideas look good to me, but there might be other options worth considering, too. Are you free to meet Friday at 11 to discuss the options more?”
After writing about being a scientist who deals with anxiety, one question I’ve been asked repeatedly is what faculty can do to make their labs friendlier to students with mental health issues. I’m generally unsure of how to respond to this – so much depends on each particular situation. But avoiding unnecessary vagueness in emails is one pretty straightforward, simple thing that people can do to make academia friendlier to everyone, but perhaps especially to those with underlying anxiety issues.
A couple of nights ago, I checked the weather forecast for the next day, in part to see how cold it would be for my morning run. I was surprised to see that the forecast was for 3-6 inches of snow overnight. (I hadn’t realized a storm was coming!) I had no interest in trying to slog through a run in 3-6 inches of wet, unshoveled snow in the dark, so decided I would work when I first got up in the morning (in that wonderfully quiet time when I’m the only one in the house who is awake) and go to the gym at the end of my work day. And that’s what I did. I got up, made myself some tea, sat down to check twitter, and then started working, which included replying to some emails that had been hanging around in my inbox.
That was when I remembered a conversation I’d recently had about whether it’s okay to send work emails outside of “typical” work hours. This is a topic that comes up on twitter sometimes, too, as well as on facebook. The concern is that, if you’re sending emails early in the day or in the evening or on weekends: 1) you have an unhealthy work/life balance and/or 2) you are sending a message to others that they should be working at those times, too. I fully, completely support having interests outside of work, and think that working long hours is unhealthy and unproductive. But I don’t think the way to achieve healthy work habits is to be proscriptive about when people work, or to shame others for working outside the hours that we deem acceptable.
Over the years, I’ve heard people talk about mentoring plans and individual development plans (IDPs), and always thought they sounded like they could be worth trying some time. But I never made it a high priority, and so never actually got to doing them with my lab. I got as far as starting to do an IDP for myself to test it out, but never got further than that. Then, last year, I had to do a mentoring plan with one of my students, as a requirement of her graduate program. As soon as I did that one with her, I realized I needed to be doing these with everyone in my lab, including grad students, postdocs, technicians, and undergrads. Here, I’ll describe what we include in our mentoring plans, talk about some of the ways they’ve been helpful, and will ask for ideas on some things I’d like to add or change.
A while back I argued that we’ll never get rid of salesmanship in science, and wouldn’t want to. But there are more and less effective of “selling” your work (i.e. conveying to others why it’s interesting or important).
Here’s perhaps the worst way to “sell” your work: just asserting how great it is. This is totally ineffective. If your work is great, telling the reader that it’s great is superfluous. If your work isn’t great, telling the reader it is won’t convince the reader otherwise. As the old adage goes, show don’t tell. And don’t just take my word for it, take NSF’s (see tip #3).
Indeed, merely asserting how great your work is is actually worse than ineffective. It turns readers against you, and rightly so. It’s the reader’s place to decide if your work is great, not yours. So if you assert your work is great, it comes off as you trying to usurp the reader.
Fortunately, it’s easy to avoid simply asserting that your work is great. Never use any of the following words to describe your own work or proposed work:
Note from Jeremy: This is a guest post from my friend, biologist Greg Crowther. Thanks very much to Greg for being brave enough to share some personal experiences and advice that I’m sure will resonate with many readers. Thanks as well to Greg for only sharing non-embarrassing anecdotes about our time together as undergrads. 🙂
Today I’d like to add another job-search saga to the pile – this one focused on teaching-focused positions – and to extract some lessons, if possible.
I recently attended a “lunch and learn” session at my university on how to talk to graduate students about non-academic careers. The session was led by Anne Krook, who spent seven years as a professor at Michigan before moving on to a successful career at Amazon and other companies. What follows are my edited notes from the session. Any errors, omissions, etc. are mine.
We also have a series of guest posts from ecologists who’ve gone on to non-academic careers, the first of which is here (and as an aside, if today’s post is at all of interest to you–and frankly, even if it’s not–you should do yourself a favor and read that old post as well. Seriously. Read it. Leading candidate for best post we’ve ever published.)
You’ve got mail. Lots of it, especially if you’re a faculty member. And it’s overwhelming. Those were some of the results of the email poll I did recently. I wrote the post because I am often overwhelmed by email. I was curious to know if others were, too. (My guess was yes.) I was also hoping that someone would have magically figured out how to make the email problem go away. Sadly, there doesn’t seem to be a magical solution, but there were some useful tips. In this post, I’ll first give the results of the poll, which I think were interesting. Then I’ll get to some of the suggestions that came in on the blog and via twitter.
In the poll, I asked:
- How many work-related emails are in your inbox now?
- What is your goal for the number of work-related emails you aim have in your inbox?
- How often do you feel overwhelmed by email?
and then asked for information on the respondent’s current position and age. (I was originally also planning on asking about gender, because I thought it would be interesting to see if there was a difference, but I forgot when I set up the poll. Whoops.)
Before getting to the poll results, a little more on the data, code, and analyses: If you’re interested in the full data set and/or the code I used to analyze it, those are available here. I especially want to focus in on the cross-factor analyses, which I think are the most interesting. These rely on the Likert package by Jason Bryer, which I first learned about from Rayna Harris. It makes really cool figures for this sort of data!
Now, the results:
Note from Jeremy: this is a guest post from my friend Greg Crowther. Who among other things has been a biochemist, and an instructor in various biology courses including ecology. He’s an unusually thoughtful and creative teacher, for instance using songs to teach anatomy and physiology. Oh, and he has three papers in Annals of Improbable Research (e.g.), which is like the science humor equivalent of having three Nature papers. Thanks to Greg for writing us a guest post on a handy teaching tip.
Most people who think hard about how to teach well accept that students should engage in “active learning,” which has been defined (by Freeman et al. 2014) as follows: “Active learning engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. It emphasizes higher-order thinking and often involves group work.”
Sounds good, right? In general, it is good. I enjoy challenging students with hard problems and helping them find their way toward an answer, and they are usually glad to be moving and talking, especially if the problems resemble ones they’ll encounter on tests.
Active learning is relatively easy to include in teaching about a specific research study. For example, after providing some appropriate context, one can simply work through the figures by asking students how and why the data in each figure were collected and what they mean (Round & Campbell 2013).
When teaching basic conceptual material, though, I slip into straight-up lecture mode more often than I’d like. It can be very time-consuming to add nontrivial interactivity to coverage of this material.
However, I do have one fall-back strategy for quickly turning a traditional lecture slide into a mini-discussion. I call this approach the “Dissection of the Imperfect Analogy.” Here’s how it works.
Scientists disagree with one another, about all sorts of things. Always have, always will. Because of that, discussion and debate are part of science. Which isn’t everyone’s cup of tea, but that’s fine. Scientists are hardly ever obliged to engage in discussion or debate. You can go your whole career in science without ever giving or receiving much criticism of scientific work outside the contexts of anonymous peer review and trainee advising.
But one thing I hate to see is people—especially junior people—avoiding discussion and debate for the wrong reasons. In particular, the fear that you’re putting your career at risk, at least in some small way, if you publicly disagree with a bigwig about science. Whether in face-to-face conversation, or on Twitter, or on a blog, or in a paper, or whatever.
I can appreciate where this fear might come from. Even though I’ve never shared it myself.* I have no idea how common this fear is (beyond “it exists”), or how important it is relative to the many other reasons for avoiding scientific debate. But understandable as it is, and whether it’s common or rare, it’s a shame that it exists at all. So if you ever hesitate to disagree with Dr. Famous because you’re afraid it might hurt your career, here’s why you shouldn’t worry: