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.

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)

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.

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.

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:

• Allison Barner et al. 2018 Ecology, “Fundamental contradictions among observational and experimental estimates of non‐trophic species interactions“: this is just a phenomenal paper, combining statistics and a field study that would have been major even just on its own. This paper has important implications for how we all analyze our data and understand ecology. Go read it.
• Nicholas Graham et al. 2018 Nature, “Seabirds enhance coral reef productivity and functioning in the absence of invasive rats“: this is a cool paper about how an invasive species disrupts nutrient flows between ecosystems, so uniting several research themes I’m really interested in. It was lots of fun to read.
• Jennifer Tank et al. 2010 Journal of the North American Benthological Society, “A review of allochthonous organic matter dynamics and metabolism in streams“: I came back to this paper over and over. It’s a well-organized and well-written overview of a bunch of stuff I need to know, and despite being eight years old now it has so many useful references and details. Essential reading for stream ecologists.

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!