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.

 

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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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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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Should the advisor leave the room for part of a student’s committee meeting?

Scrolling through twitter a couple of weekends ago, I saw this tweet:

At first, I misread it and thought it was indicating that the student had been sent out of the room (which is the norm for committees I’ve been on). It took me a second to realize that it was the advisor who had gone out of the room so that the student could have a discussion with their committee without the advisor present. I suspect my misreading wasn’t just a product of quickly scrolling through twitter on the weekend—rather, I think part of the reason why I misread it was because it was such a shift from how things are normally done in departments I’ve been in.*

After realizing what it said, though, I thought it was an interesting idea. I can think of cases where it might have helped to have a discussion without the advisor there to get a better sense of the student’s opinion on things, such as when they would prefer to defend or how excited they are about project 1 vs. project 2 or how they feel about traveling to remote location X to collect samples. And, in the rarer cases where there were major problems, it might have led to those becoming apparent to the committee sooner, which hopefully would lead to the student getting support sooner.

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Do you know a department/program/university/institute/etc. that is doing something worth emulating regarding graduate student mental health?

There is general agreement that too many graduate students experience poor mental health and that more needs to be done to address this problem. A recent well-controlled study found graduate students were at 2.4x greater risk of common mental health disorders. That number won’t surprise anyone in academia—it doesn’t take much time in academia to realize that poor mental health is unfortunately common.

There is still much work to be done to better understand the problem and the factors that contribute to it. But there is also a need to make changes that might help improve graduate student mental health. To list some of the specific things I’ve been thinking about:

  • developing a system for checking in on students who are at stages known to be stressful (e.g., qualifying exams, defending);
  • having a department point person who helps connect graduate students with mental health resources; and
  • how to ensure better access to mental health care and increased normalization of seeking mental health care.

There are also issues related more broadly to the culture in which graduate students carry out their research, including a need to fight against a culture of overwork and to reduce sexual harassment. (1 in 5 targets of sexual harassment will be diagnosed with a depressive disorder, and there is a positive correlation between the amount of sexual harassment a woman experiences and the degree to which she reports depression, stress, and anxiety.)

As I think about things that could be done to better promote and support graduate student, my hope is that there are already departments, programs, universities, institutes, societies, etc. that are already doing good things in this area that others could emulate. It could be something big—one person who responded when I asked about this on twitter talked about a rapid response coordinated care team that works with grad students in crisis and grad chairs—or it could be small:

(Bonus: the dogs are listed as staff on the Emory CAPS website!)

Please let us know in the comments about good things people, departments, institutions, etc. are doing related to graduate student mental health! 

Guest Post: iPads and digital data collection in the field

From Meghan: This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. I loved their comment on my post on our new lab notebook backup system and asked them if they could turn it into a guest post. I was very happy that they agreed! Isla and Gergana are off to the Arctic this summer with the Team Shrub field crew for another year of hopefully successful digital data collection. To find out more about their research check out the Team Shrub website and blog (https://teamshrub.com/).

Guest post:

Two things have really changed my academic life over the past five years: the first is embracing GitHub for version control of code, data, manuscripts and my research group’s individual and combined science, and the other is switching over to digital data collection. For ecologists who haven’t made the switch from paper field books to iPads and digital data collection it is not as scary as you might think!!!

Caption: Collecting plant phenology data – the recorder sitting in the back with an iPad! (photo credit: Jeff Kerby)

The benefits of going digital

Digital data collection can be more rigorous with error checking as data are collected to prevent mistakes. Data can be better backed up. And finally, it forces us to put thought into the structure of data before we collect it (significant digits, continuous or categorical data, are the data unrestricted or constrained to a particular range or particular set of values, etc.), which helps down the road when it comes time for analysis. Digital data collection has saved days, if not months, of data entry each year for my team and has allowed us to go from ecological monitoring in the field to analysis of results within hours instead of days. Our work flows are streamlined and our iPads are waterproof, so data collection can occur under any conditions – and we work in the Arctic, so we experience it all from wet to dry, hot to cold, rain, snow, you name it.

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Rough drafts, getting words on the page, and the pain – and pleasure – of writing

Intro: this is the first of a series of posts exploring some common themes in three books I’ve read recently that relate to writing: Anne Lamott’s Bird by Bird, Helen Sword’s Air & Light & Time & Space: How Successful Academics Write, and Tad Hills’ Rocket Writes a Story. (And, yes, one of those is not like the other.) This post focuses on getting started with a new writing project, rough drafts, and the pleasures of writing.

The post:

Everyone I know flails around, kvetching and growing despondent, on the way to finding a plot and structure that work. You are welcome to join the club.

     – Anne Lamott, Bird by Bird

Unfortunately, a universal experience of writing is that getting started can be hard. Rocket knows this:

The next day, Rocket returned to his classroom. It was time to begin. He looked down at the blank page and the blank page looked up at him. But no story would come.

From Tad Hills’ Rocket Writes a Story

This is something that all writers struggle with, but that can be especially problematic for new writers. The task can seem so big and daunting – and there’s a decent chance that you are feeling like an imposter who is about to be exposed.

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My lab’s new lab notebook backup system. Part 2: The system

In yesterday’s post, I talked about my motivations for seeking a new system for backing up lab notebooks and data sheets. Here, I describe the system we’re now using for backing up lab notebooks, data sheets, etc. I think it’s working well. At the end, I ask for suggestions of systems that work for backing up files on lab members’ laptops, which I think we could do a better job of.

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My lab’s new lab notebook backup system. Part 1: The backstory

Back when I was an undergrad, the fire alarm went off while I was working in the lab. As people gathered outside the building, it became clear that it wasn’t a drill – I don’t recall specifics, but I think it was actually an issue in a neighboring building that caused someone in our building to pull the fire alarm. In the end, it wasn’t a big deal (even for the neighboring building!) But while people were standing around outside, the conversation turned to how much data would be lost if there was a full-on fire. It was clear that lots of people did not have complete backups of their lab notebooks and other data files.

When I was telling about this experience to a friend, Brooks Kuykendall, he told me of his father Bill’s related horror story. In the fall of 1963, Bill had completed his PhD research at Johns Hopkins in Archaeology, and started teaching at Erskine College in rural South Carolina. In the summer and early fall of 1964, after his first year of teaching, he had managed to finish writing his dissertation. In early October, he had assembled the six copies (and these were the days of carbon copies) ready to be submitted to his committee. They were stacked on the floor of his office, needing only to be packed off to go in the mail.

And then that night Bill heard the sirens. Rushing to his office, he found that the building was on fire. The firemen had cordoned it off, but somehow two students—of whom he forever after spoke with gratitude—managed to get in through the window of his ground-floor office, recovering only 1) a single rough draft for the whole text, and 2) a box that had the originals for all of the illustrations. The copies on the floor—and virtually everything else in the office—were ruined by the water.

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