About Meghan Duffy

I am an ecologist at the University of Michigan. My research focuses on the ecology and evolution of infectious diseases, particularly in lake Daphnia populations.

Bonus Friday link: NSF’s Bio Directorate removes proposal submission limit for 2019

For the US folks, NSF’s Bio Directorate had an important announcement yesterday, removing the limit on the number of proposals someone can submit as PI or co-PI in 2019. Here’s part of the announcement:

Having listened to community concern and tracked the current low rate of submission, and following extensive internal consultation, BIO is lifting all PI or co-PI restrictions on proposal submission for FY 2019, effective immediately.

BIO recognizes that it is important to track the effects of the no-deadline policy on proposal submission patterns, to ensure that a high-quality review process is sustained. Therefore, we are seeking approval from the Biological Sciences Advisory Committee to establish a subcommittee to assist in developing the evidence base for any future policy changes that may be needed.

I think this is great news! And I completely agree with Mike Kaspari:

With public engagement, it’s also okay to start small

Yesterday, I had a post about how it’s okay to start small when it comes to learning R or any other new technical skill. Today’s post takes that same “it’s okay to start small” message and applies it to public engagement.

Sometimes, a colleague will ask about a recent public engagement activity my lab worked on. After I describe it, they sometimes say something like “I’d like to do more outreach work, but my lab isn’t as big as yours – I don’t have those people to help me!” Often, that is said with a sense of resignation that it won’t be possible for them to do outreach. Or perhaps the conversation centers around an upcoming NSF proposal, where a colleague is trying to figure out what they could propose for the broader impacts section, feeling like they want (or need) to propose something, but that there’s no way for them to do that if they are just starting out or haven’t done much public engagement in the past. In these conversations, my messages are:

  • it’s okay to start small, and
  • take advantage of existing opportunities.

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When learning R (or any other new task), it’s okay to start small: aim for improvement, not perfection

When I first thought about switching to R and doing reproducible data analysis, the idea was daunting. As a grad student, I couldn’t figure out how to even get my data into R. How would I figure out that plus mixed model analyses plus how to make figures in ggplot, with version control and a beautiful github rep for all of my work?! What I eventually accepted is: it’s okay to start small. Or, as a colleague of mine suggests: for any given project, aim to do one thing in R that you couldn’t before.

I’m not sure why I set the bar so high for initially learning R. When I was first learning how to knit (actually knit, with yarn and needles, not the R version of knit), I knit a square washcloth, not a sweater. So when learning R, why was I expecting I’d be able to start out with the coding version of knitting a sweater with multiple colors, a fancy pattern, and buttons?

File:Fair Isle knitwear geograph-3936603-by-Julian-Paren.jpg

Julian Paren / Fair Isle knitwear in the Shetland Museum / CC BY-SA 2.0 via wikimedia.org

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Guest post: Coding Club – trying to overcome the fear factor in teaching and learning quantitative skills

This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. In case you missed it, they wrote a wonderful guest post this summer on iPads and digital data collection in the field.

Ecology is a fast-paced science, with possibly hundreds of relevant papers published every week and new techniques and quantitative skills being developed all the time. It is easy to feel very behind and overwhelmed. The quantitative skills taught in undergraduate and graduate programs in ecology often lag behind those used in the literature. As ecologists at different stages of our academic careers, who have felt (and still do sometimes) pretty behind the eight ball in terms of quantitative skills, we wanted to do something about that for our students and peers. And that is how we came up with the idea of Coding Club.

How did it all begin?

Just about two years ago we had an idea. What if we set up an informal group and a website to teach key quantitative skills that could be useful to undergrads, grad students, postdocs, profs and ecologists working outside of academia? What if that website was built in a way that anyone could contribute tutorials or help to make the existing tutorials better? What if we taught people how to learn in their own working environment and how to develop their workflow using best practices in open science like version control from the very beginning? What if this content was aimed at people who felt afraid, anxious and behind in their own quantitative skills development. This was the beginning of Coding Club.

screen cap of the homepage for Coding Club; header says: Coding Club: A positive peer-learning community

The Coding Club website where we host all of our tutorials on data manipulation, data visualisation, modelling and more!

 

What is Coding Club?

Coding Club combines online and in-person resources to help teach quantitative skills to ecologists at all career stages. We have focused on trying to overcome “code fear” and “statistics anxiety”. Statistics anxiety – the worry about a lack of quantitative skills – and code fear – the fear of programming – can prevent people from learning. By building a sense of community around the development of skills, we hope to overcome the fear factor of ecology involving more code and math than people sometimes expect.

left panel shows six people posed, smiling at the camera; upper right panel shows a computer lab with people at work and someone at front; lower right shows three women talking and smiling

Part of the Coding Club team and snapshots of some of our workshops. Check out our team page for the full list of undergraduates, postgraduates and profs that have contributed to Coding Club! Photo credit for image on left: Sam Sills

 

Peer-to-peer teaching helps to reduce the fear factor

In Coding Club, we focus on peer teaching and interaction rather than having “trained experts” leading workshops as we feel people engage more when they are less intimidated. All of our teaching materials are developed by people who are actively learning data science skills at the same time as teaching them. We avoid hierarchy (though we love content on hierarchical modelling!) and encourage participation across different career stages from undergrad students through to PhD students, postdocs and staff. Moving away from the professor-student model and allowing everyone to engage as teachers and learners can be a pretty powerful way to break down barriers.

Coding Club covers a growing number of different quantitative skills

The Coding Club website contains a growing list of tutorials aimed at all levels of quantitative skills useful for ecologists and beyond. We cover topics from intro to advanced R tutorials, version control, data visualization to working with large datasets. We have a lot of R content but we don’t just do R! We are currently working on developing more tutorials using Python for process-based modelling and the Google Earth Engine for remote sensing analyses. We have been using the tutorials to teach in-person workshops at the British Ecological Society conference and at universities around the UK, but the tutorials are there online for everyone to use, provide feedback on or suggest revisions through GitHub. We are always looking for people to develop new content as well!

four badges, one for sharing quantitative skills, one for meta-analysis & bayesian statistics, one for spatial and population data, and one for pandas

A sample of the Coding Club tutorials, including a tutorial on how to make tutorials on GitHub. Data visualisation, mixed effects models, Stan models and more over here.

 

Quantitative learning should be active and not passive

We believe that the best way to teach coding and quantitative skills is through a problem-based approach that is question driven. We try to avoid approaches like ‘live coding’ as it encourages learners to be very passive with the subject matter and we believe this results in lower retention of the new material. To effectively learn a new skill, it is vitally important to know why you might want to learn that skill in the first place and to have a question that you want to answer to motivate you to learn. We also recognize that people learn in different ways and at different paces. In our in-person sessions, we encourage people to take as long or as little time as they wish to complete the tutorials. We believe this casual, non-compulsory and non-assessed nature of Coding Club also helps to reduce the fear and anxiety associated with quantitative skills.

 

Picture5

Coding our way towards finding out how population trends vary among different taxa, with cookies along the way. Not pictured: the standard error cookie. We forgot to make one, but of course we are all for reporting the uncertainty around effect sizes!

 

Quantitative skills are not hard – they just take some work to learn

We believe teaching quantitative skills is all about overcoming fear and building confidence. We try to avoid labeling skills as “hard” or “easy”, because we don’t want people labeling themselves as quantitative or not, or pre-judging the limits to their own capabilities. We aim to train people to be able to answer their own questions, resolve their own coding problems and seek out new skill sets independently. We are trying to teach people to train themselves beyond the timespan of a single workshop or course. Finally, we don’t think there is only one way to teach quantitative skills and promoting a diversity of approaches will reach the most people.

 

Coding Club has exceeded our expectations!

As of October 2018, the Coding Club website has received over 160,000 visits from over 73,000 unique IP addresses from over 180 countries. Our tutorials have been contributed by people from multiple universities (University of Edinburgh, University of Aberdeen, McGill University, Ghent University, Aarhus University) and used for quantitative training across several institutions so far (University of Edinburgh, University of Aberdeen, University of St Andrews, Queens University Belfast, Dartmouth College, Hebrew University, Calvin College, Centre for Ecology and Hydrology and more), and we are hoping to reach out further! If we can set up a network of people at universities and research institutes around the world who can work together to develop quantitative training from the ground up, then maybe we will all feel just a little less overwhelmed by our fast-paced discipline.

World map showing numbers of visitor, represented as blue dots. The dots are especially dark and big over the UK, but include visitors from around the world

The international audience of Coding Club – it’s been great to get feedback from people using our tutorials around the world!

 

The start of the new academic year feels like a fresh start. A chance to purchase some new office supplies, catch up on all the science missed over the summer, start a new work routine to enhance productivity and to set yourself some new challenges. Now that the term has started, maybe it is time for you to take the plunge and learn a new quantitative skill.

 

Are you a student or group of students wanting to increase your own quantitative skills? Are you someone who has a cool analytical technique that you want to share with your peers? Are you a prof. who wants to encourage your students and mentees academic development? Are you someone who feels like the quantitative training you got years ago is not enough for the ecological research today and want to brush up on your skills? Do you have thoughts on how we can improve quantitative training in ecology? If you answered yes to any of these questions, please comment below, check out the Coding Club website and get in touch if you are keen to join the team!

My goal as a reviewer: pass the Poulin test

As a graduate student, I attended my first infectious disease-themed meeting shortly after receiving the reviews on my first thesis chapter. I was excited about the work, and had sent it to Ecology Letters, which reviewed it but rejected it. I talked about the same study at that meeting. It was a small meeting, and one of the great things about the meeting was getting to interact with senior people in the field. This included Robert Poulin, someone whose work I really admired. I was really excited to get to talk to him! During our conversation, he asked about the status of the work I’d presented at the meeting. I said that it had just been rejected by Ecology Letters and then was about to launch into a vent about the reviewers. As soon as I said (in what I’m sure was an exasperated tone), “One of the reviewers”, he stopped me and said “I was one of the reviewers.” I will be eternally grateful for that.

That moment has stood with me throughout my career. In addition to preventing me from embarrassing myself (more!) in front of him, it taught me a really important lesson about peer review. We complain about Reviewer 2 and shake our fist at that mythical beast, but there’s a decent chance that Reviewer 2 is someone who carefully reviewed the manuscript and thought something was problematic. Or maybe it’s that, with a bit of distance from the work, Reviewer 2 thought the work wasn’t as novel as I did as an author, making rejection from a journal like Ecology Letters completely reasonable.

This interaction taught me an important lesson about how easy it is to think of an anonymous reviewer as an adversary, when there’s a good chance they’re a scientist whose work I admire and whose feedback I would value.

There’s an idea that anonymity leads to animosity. I think that’s more often discussed in terms of the person making the comments – for example, as a reason for the toxic nature of the comments on websites. But it also applies in the other direction – in an anonymous interaction, it can be easy to assume the person writing the comment is unreasonable (unless they think our work is brilliant – then clearly they are totally reasonable!) I think the way the scientific community discusses reviews (including on twitter) probably doesn’t help.

Personally, when I receive reviews, I have to work to put myself in the mindset that these reviews can help my paper, even if they’re negative. There are still occasions where my first reaction is something like “How is it possible for reviewers to be so clueless?!?!” but then, after coming back to the reviews a few weeks later, I realize that the reviewers were pointing out something that we didn’t explain very well or a part of the literature we really should have discussed more or an alternate explanation we hadn’t fully considered.

As I’ve blogged about before, I don’t sign most of my reviews. But I still write them with that interaction I had with Poulin in mind. My goal is to write reviews where, if I ended up in that same situation at a meeting, I would be okay with identifying myself as the reviewer, even in cases where my review was a critical one. In other words, I want to pass what I’ve come to think of as the Poulin test.

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Less obvious signs of reaching a new career stage

Something I’ve been thinking about lately are the less obvious signs of reaching a new career stage. I don’t mean the obvious things like being accepted to grad school, or defending your PhD, or signing your first job contract. I mean things that aren’t generally listed as major milestones but that felt important or noteworthy to you (e.g., the first time you bought coffee for someone who was at an earlier career stage than you were).

I’ll give some more examples:

As a graduate student, I remember other students talking about the first time they did an experiment without running it by their advisor first. The two particular stories I can recall were both senior grad students (one may have been a postdoc) who had a hunch about an interesting thing that might be going on in their system. In one case, the person did the experiment, then went to talk to their advisor, proposing the idea. The advisor said it would never work, leading the advisee to get the extreme satisfaction of dropping a figure showing it did work on the table.

As another example, for me, the point that I felt solidified that I was no longer early career was when I was reviewing the application file of a graduate student applicant and saw that one of the letters of recommendation had come from someone who had been an undergrad in my lab (and who now has a faculty position).

To use some I’ve seen recently on twitter:

Having someone seek you out at a meeting to talk science:

(And, since Rachel was my first PhD student, her experience also felt kind of significant for me!)

Your first paper is perhaps an obvious academic milestone, but your first last author paper also feels big!:

(Related: I remember being extremely happy about the first paper that contained data collected entirely in my lab.)

Receiving your first review request is an academic milestone; a less obvious one is reaching the point where you receive too many review requests to handle:

And here’s one based on a recent Eco-Evo, Evo-Eco blog post: being able to stand in one spot for a day and a half and have non-stop conversations seems to be a sign of having reached a particular (well-known!) career stage. (ht for this one goes to Jeremy!)

So, I’m curious: what were some of the less obvious milestones for you? (Update: If you want to tweet them, use #lessobviousmilestones)

Guest Post: What not to say to a pregnant colleague

Today, we have a bit of a hybrid post. It starts with a guest post from someone who wishes to remain anonymous about things colleagues have said to her during her pregnancy. Her post definitely resonated with me – I thought of writing a similar post when I was pregnant with my third child, because I was so annoyed by some of the comments I received at work. After the guest post, I’ve added some thoughts of mine, as well as some questions that I’d love reader opinions on. My hope is that this post will encourage people to think more carefully about what they say to pregnant colleagues and create a space where people can talk about their preferences.

The guest post:

I am a postdoc who also happens to be pregnant. Around the sixth month of my pregnancy something happened. I must have become large enough that it was obvious to everyone in the department that I was indeed, pregnant. Suddenly, I began receiving comments about my body, my impending delivery, and what my life would look like after having a baby. (This is my second child; I have no delusions as to what postpartum life is like).

Here are a few of the comments I received over the span of two weeks:

My body:

“Wow, you’ve really let yourself go”.

“If a baby weighs 8 lbs then where do the other 25 lbs come from?”

Misconceptions about maternity leave:

“It will be so nice for you to have a break while you’re on maternity leave”.

“Think of all the writing you’ll get done while the baby is sleeping!”

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More on what colleges must do to promote mental health for graduate students

Recently, a piece I wrote with my colleagues Carly Thanhouser and Daniel Eisenberg appeared at The Conversation. The piece focuses on things that can be done to promote graduate student mental health. Our aim was to move beyond the typical self-help things (get enough sleep, exercise, etc.) – those are important, but exercise can only go so far if there are systemic issues contributing to poor mental health.

I encourage you to read the full piece, but I also wanted to follow up on a few things here (tw: discussion of suicide below).

  1. We need to focus on mentoring, too!

Perhaps most notably, during the process of editing the piece from our original submission to what got published, a section focused on what graduate mentors can do to promote mental health got cut. On the one hand, I wish it was in there because a mentor’s advising style can significantly influence graduate student mental health, and there are things mentors can do to promote student mental health. On the other hand, it’s such an important topic that it probably deserves its own piece. I’m planning on writing that (and am open to suggestions about where to submit/publish it!)

  1. It’s good to think about what individual students can do, but we need to also address systemic barriers to mental health

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My strategies for mentoring undergraduate researchers

At this year’s ESA meeting, I was part of an Inspire session organized by Nate Emery on “Students As Ecologists: Collaborating with Undergraduates from Scientific Question to Publication”. It occurred to me that my talk would be good fodder for a blog post. So, here are (some of) my thoughts on some specific strategies for working with undergraduates in the lab. This post includes information both on types of projects that we’ve had undergraduates work on, as well as things that I think are important related to working with undergraduates in the lab.

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Put your take home message at the top of your slides!

Imagine you’re sitting in a talk. It’s Thursday morning at the ESA meeting and your brain is a little fried from sitting in lots of talks all week. You momentarily zone out, then try to turn your attention back to the talk. Which of these would be most useful to see on the slide as you tune back in?

Option 1:

Option 2:

Option 3:

You chose option 3, right? (If you are curious about the data, you can read a preprint here.)

Maybe you aren’t always giving a talk on Thursday morning during a jam-packed meeting, but there will always be people in your audience who are tired or get distracted. Make life easier for your audience by putting your take home message for each slide at the top!

Or, to quote Stanley Dodson*: “Make your top line your bottom line!”

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