Blurred lines in academia–what is work?

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While navigating the unemployment system in Sweden, I’ve discovered that I need to report every month what I’ve been doing to find a job. It includes applying for jobs of course but also training. I should also include working on my CV, networking and other activities that improve my employability. I’ve also been warned that one shouldn’t “work” during this time and all work has to be reported (you can work for up to 75 days and keep your unemployment status).

All of this has me reflecting on what work is in academia.

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It seems to me that few other professions have the same structure as academic research. Continue reading

Review unto others as you would have them review unto you?

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I am going to go ahead and assume we all want quality reviews of our journal submissions, however you define ‘quality’. Reviewers that take time to seriously evaluate your work, provide constructive feedback and ultimately improve the paper should always be appreciated. But as reviewers ourselves, we know that sometimes we don’t always give each paper our full attention. In general, I try to give good and helpful (to the author and editor) reviews. I try not to take on reviews when I know I don’t have the time to do a good job. Perhaps I am naïve but the impression I get from my colleagues and reviews of my papers is that in general most people are also trying to give good reviews. Continue reading

Social media: what is it good for?

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For better or worse, I am the only person in my department who engages regularly in social media. Blogging here, reading other blogs (and occasionally commenting), chatting on twitter…over the last year or so these have become regular activities for me. So for our informal seminar series, I decided to talk about using social media as a scientist. Continue reading

The conference hangover

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This week I definitely had a ‘hangover’. Two weeks of meetings* left me a strange mixture of excited, enthusiastic, invigorated and completely drained. I have lots of new ideas and enjoyed both making new connections and reconnecting with others. But I can forget how drained I can feel after such intense social activity, even if I don’t travel far. Continue reading

Writing a review: thoughts from the trenches.

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Somehow I’m in the middle of writing three review papers so I am gaining some perspective on writing them. The first one is basically my own fault; I started thinking a lot about nectar rewards and how they fit into my research. That thinking lead to a talk last year on some of my ideas to a bunch of like-minded folk at the Scandinavian Association of Pollination Ecologist’s meeting. Main lesson from my experience: never end a talk asking if you should write a review (and/or for interested co-authors) unless you really want to. Continue reading

Academic self care

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The semester has begun and everyone is returning back to campus. It means my commuter bus is full and I rarely get a preferred seat. Bike parking in Uppsala is a lot harder too. For me this means that I’m returning to my office and there are people walking around in the corridors. I spent my summer doing a mix of work travel, fieldwork, housework, vacation and lots of mad writing at home. It was a nice break from the routine and a hopefully productive summer. Mostly it has meant that I’ve only dropped by the lab every once in a while to run samples but otherwise I haven’t spent much time there.

So when I started coming back into the office, I’ve been catching up on all those things I’d ignored during the summer. There is juggling the samples I’ve accumulated, meeting with students, catching up with my PhD student about her work this summer, chatting with colleagues, digging out my desk, and trying to finish up writing on a deadline.*

When I get into a rhythm of working at home/in the field, I often find that I don’t transition well to being back in my office. I’m not sure why really but I tend to get distracted by all the things that need doing. I don’t drink enough water. I eat my lunch late and I generally push myself in ways that are unhealthy. It only takes heading home with a headache to reset my mindset and remind myself that I don’t need to do all the things. And if I ignore my body it comes with a cost.

In the ‘back to school’ season it is good to remind myself to take care of myself and remember to listen to my body. I think that academia can be quite bad at creating healthy work environments. Although there is the issue of taking care of your mental health, and I know they are connected, but in this post I’m going to focus on physical constraints of a job in academia. I think the job can lend itself to all kinds of bad for you behaviours. I’m definitely guilty of a few.

In my experience, one of the problems of research can be that you never do any particular task (accept maybe computer work) for long enough periods of time to ensure they are ergonomic and not damaging. Now before you start thinking about those long days in the field or lab doing some horribly repetitive task for hours on end and disagree, I’m not talking about hours, days or even weeks here. I’ve done some tasks in physically awkward ways (or witnessed them) simply because it isn’t such a long term thing. You just need to get through these 100, 1000, etc samples/computer files/whatever. If it were your job to do that thing and only that, you’d never be able to sustain it if you didn’t have a good work station. But we often only work on short-term assembly line tasks so they are often not set up in the most ergonomic way. Of course some situations are beyond your control. It is difficult to measure flowers on a plant at an awkward height but you can’t change how the plant grows. You can however, varying your position, use a camping stool, sit on the ground and otherwise make accommodations so you don’t strain your body. The same is true in the lab or at the computer. I know many examples of grad students who developed some kind of repetitive stress injury while doing their research. It a real and can be debilitating thing.

Most of us spend a lot of time at our computers so it is a good idea to create a good desk situation. Separate keyboards from your laptop, raised screens, a good chair… all these things can help long hours at the computer. Meg Duffy has also talked about her treadmill desk and its benefits and limitations. I have an adjustable desk for standing, which I try to do much of the day, but haven’t ventured to a treadmill. But it isn’t just posture at your desk that can cause problems, typing and mouse work can lead to repetitive stress injury so setting up your work station can be crucial to successful computing (some ideas for avoiding bad computer setups and injury here).

Similar principles apply to your lab and fieldwork. The more conscious you are about the way you have to do the activity and think about it before hand, the more healthy you can be. I also find that those few moments of thinking about how to do a job in a healthy way also improves efficiency. It is hard to be efficient at a task if it is physically awkward in someway. So whether you are processing 10, 100, 1000 or 10000 samples, making it easy on your body is worth a few moments of contemplation.

I try to be mindful of the tasks I do and set things up in a way that are ergonomic, even if I’m not going to be doing that activity for extended periods. But it is easy to forget about your body, get caught up in a task. For me it is always the rush to the finish line that gets me; it is precisely because I see the end of the task that I tend to push myself too hard.

I’m definitely not writing from some moral high ground. I am currently battling frozen shoulder, which was probably made a lot worse by spending too many hours painting windows this summer. I’m sure the inactivity of desk work doesn’t help me either. But the experience has got me more conscious of what I’m doing with my body and I hope after some physiotherapy I might be able to lift my arm above shoulder level again some day soon. Now I just need to also remember to take breaks, drink water, don’t over-caffeinate and generally take care of myself at the office.**

 

 

*Who in their right mind accepts to co-author a review due at the end of the summer? So glad I said yes, and more so now that it is submitted, but it definitely made for a crazy summer.

**Thanks to @CMBuddle and @Julie_B92 who got me thinking more about the topic.

What happens in the canopy stays in the canopy.

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For a few years, I’ve harbored a very cool (at least to me) natural history idea. But it’s a big technical challenge. The required fieldwork is never going to happen by me. So, I should write a blog post about it, right?

Bullet ants (Paraponera clavata) are one of the most charismatic creatures in Neotropical rainforests. My lab has done some work with them recently. These often-seen and well-known animals are still very mysterious. Continue reading

Field courses: a blessing and a curse

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Since I began my position at Uppsala, my summers begin frantically. Although my teaching load is relatively light, the majority of it comes in the spring just when I am getting ready for my own and my PhD’s fieldwork.

I teach in a course on Ecological Methods. Students learn mainly about sampling and survey techniques for a broad range of organisms but the focus is on birds, insects and plants (for which I’m responsible). The course starts in March and runs until the first week of June (therein lies some of my problems but more on that later). Continue reading

Submitting abstracts for conferences without having the data

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I’ve developed a mechanism to make sure that I stay productive: when I submit abstracts for meetings, I promise data that I haven’t finished collecting. Of course when I give a talk, I can say almost whatever I want. Nobody’s going to cut me off if my talk doesn’t match the program.

I just realized that I always have been in the habit of submitting abstracts for projects that are so fresh, I haven’t even gotten all the numbers, much less run analyses. In grad school, that was the only option, because at one point I didn’t have anything else to say. Now, even when I have other newish finds that I’ve yet to present, I submit abstracts for projects that still lack a rudimentary answer. I do this at least once a year, writing a check for results that aren’t yet in the bank. Continue reading

More backyard reflections: connections between farming and fieldwork

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The weekend was beautiful and I spent a good portion of it in the backyard digging up grass. The plan is to have a small raised garden for vegetables, nothing too extensive but enough to plant a few things and enjoy them straight from the earth. You can’t get more local than that. As happens when doing something physical, my mind wandered. I had some “help” from my 4 year old but she would quickly bore of the repetitive nature of the task at hand so I was often left to my own devises.

Beginning of the vegetable plot. Looks easy but my body can attest to the amount of digging it took!

Beginning of the vegetable plot. Looks easy but my body can attest to the amount of digging it took!

Not surprisingly, digging in the dirt got me thinking about the summer I turned 20 and spent 5 months on an organic farm. It was an interesting summer, where I learned a lot but I had no idea I was preparing for a future as a field ecologist. That summer I was a bit lost. I had gone to university for a single semester before dropping out (finances being a major factor) and spent the next year or so working at various service jobs in Vancouver. I knew those weren’t things I wanted to do forever but I wasn’t sure what it was that I wanted. So I headed back across the country to Nova Scotia to live and work on a farm very near where I had spent some of my childhood. The memories of exactly how this plan came to be are foggy for me now (think my mother subtlety encouraged the Nova Scotia angle) but however it came about I ended up living on an organic farm, working for $50/week with three other exploring (or lost depending on how you want to look at it) young women.

Before working on a farm I had a romantic notion that maybe farming was one of things I’d want to do with my life. Farming cured that even though I absolutely loved the summer doing it1. What I saw though was the stress of worrying about the weather, the pests and all the other things that can go wrong. The funny thing is that I face lots of the same problems these days, just in a different context. I’ve lost experiments to deer browsing, mowing and bad weather. One major lesson I took from those farming days is to diversify and protect the truly important “crops” (experiments). I usually have a few field experiments/a few more replicates/etc running ‘just in case’2. A lot of the ‘just in case’ also makes good ecological sense. It is important to know, for example, if the patterns you see are consistent in different populations. It also helps when the deer eat all your plants in one of the populations; at least you still have some data to work with. Protection like fencing is also sometimes a critical part of ecological experiments. If you want to examine plant-insect interactions for example then it doesn’t help if the deer eat everything. If you want to eat the tasty vegetables you plant and know there is at least one hare that prowls your yard, fencing it is.

In plant ecology, often experiments require planting out particular populations or communities. There is the raising of the seeds, planting of the individuals, harvesting of the data and the stress of choosing the right time to do all these things. Sometimes you get it wrong. I always loved this story of a large planting that got hit by a frost; smart and experienced researchers don’t throw up their hands when the frost kills half your plants. If they’re lucky there is variation in survival and they write a paper about that instead.3 However, these decisions aren’t without consequence. While I was a grad student, I witnessed another’s unfortunate loss of an entire experiment to frost shortly after planting one summer.4 So the stress that I thought I was turning away from when I finished at the farm is actually a regular part of my summers. Maybe my income isn’t so directly tied to the harvest as on a farm but if experiments and papers are the currency that allows me to keep going as a scientist, then I’ve definitely paid the price of random events throughout the years.

I learned a lot that summer but probably most things were really about me. I learned I had stamina and that I could push my body and mind to keep going. I learned that I could tolerate bad weather and good to get the job done.5 I learned to laugh at rain and hailstorms and freezing weather and heat that makes you feel like passing out every time you get up.6 I learned that no matter how well you prepare, sometimes you just need to drop everything and change directions. Perhaps most importantly I learned that I liked being out there each day and being proud of what we accomplished. And I learned that some of the best friendships come from sharing the good and the bad of fieldwork (/farm work).

These days I don’t spend 5 months outside maintaining plants and collecting data but when I get to get outside, it is often reminiscent of those farm days. But perhaps that is only since I’ve found myself doing a lot of work in old fields…

And perhaps since I’m not outside toiling in the fields all summer, I have the opportunity/energy to grow my own garden. I know my little garden isn’t enough to even provide for our family. It is really a luxury hobby. But I am growing it because I also want my daughter to have a sense of what it takes to grow food. I want her to be able to recognise what the plants many vegetables come from look like, not just what vegetables look like presented in the store. She’ll probably not grow up to be an ecologist but I want her to appreciate the living world around her, both the wild bits and the tamed.

 

Ecological Life Lessons:

1Try something before you decide! Seriously, think you want to be an ecologist? Then go work in a lab, if you can’t do that, volunteer. Or if volunteering/work aren’t options, take as many courses as you can that expose you to research experience and get on board for a research project/honours/whatever they call it at your institution. The important thing is to get exposure to what ecologists are really doing on a day-to-day basis. Of course, this advice applies to anyone looking to invest a lot of time in training for a job, not just ecologists. But familiarity of the process of research is a really good thing before you start a masters/PhD program.

2The opposite lesson is to avoid spreading yourself too thin. My PhD student has been collecting data like mad and has a lot of really good hints at what is going on in her system but this year we’ve decided that she needs to do less of the different kinds of things and concentrate on a few key studies that will wrap up her experiments nicely. Right now there is a lot of data but often not sufficient to truly say what is going on. Sometimes this is hard to avoid (e.g. we didn’t know that the variation in the things we’re looking at is so great that it is making it hard to detect whether there is a signal in the data) and she’s also had her fair share of run-ins with the deer and mowers.

3I haven’t yet had the opportunity to turn a disaster into an opportunity at this scale but I certainly look at my failed experiments to see if anything is there.

4Learn from other’s misfortune, as well as your own. As a grad student, you’re actively learning how to run your own research but you’re also surrounded by a bunch of people doing the same thing. Talk to them! Hearing about their successes and failures can be just as important as doing the things yourself. This can apply to teaching, writing, analyses, fieldwork, labwork and the list goes on. These days if I know someone who’s done something that is new to me I ask them for advice. There is always so many tricks that make life simpler, once you’ve figured them out.

5Fieldwork is often not for the faint of heart. Know your limitations. I know I need sleep and I don’t function very well without it. More than that, I work pretty poorly at night. So I won’t ever take up a project looking at night pollination. Cool stuff but I know that it would drain me in ways that super strenuous work during the day never would.

6When things get tough you basically have two options: laugh or cry (or get really sour and unpleasant and take it out on those around you). I prefer to laugh (or at least try to), makes for a better field season.

The first days of a new tenure-track faculty job

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This is the season when some lucky ones preparing for new jobs in the fall. A few people have asked me what to expect, so I imagine even more are wondering. I’m writing from my own experience (starting 2.5 new faculty jobs), and yours have been different, so please do comment. What can you expect from the start, and what might you want to keep in mind? Here are some observations and some suggestions.

  • It’s more quiet and lonely than you might expect. There is a lot to do, but many tasks are solitary. September is a crazy time for everyone who is recombobulating from summertime adventures. Everybody will be glad to introduce themselves to you, but it won’t take very long before you’re sitting at the desk in your office, alone.

  • It’s busy. If you’re teaching more than you have in the past, be prepared to be overwhelmed. This is normal. It takes a while to figure out how to teach efficiently. At the outset, you can’t afford to not be an effective teacher, so learning how to be efficient is a work in progress, as you learn the acceptable standards in your new environment.

  • Define your boundaries for students at the outset, because your rep will spread quickly. If you want to get to know your students really well outside class, then be sure to leave your office door wide open and chat frequently with students. On the other hand, it’s easy to establish a reputation as a caring, fair and hard-working professor who doesn’t spend much time with students outside of class and office hours, if you set this at the outset. Time spent well with students can be the purpose of the job and the highest pleasure, but some other time spent with students could be a fruitless time sink. Find that line. The range of acceptable positions for that line varies hugely among institutions. So, listen and watch carefully.

  • From day one, decide how you will manage your classroom. The proliferation of communication devices has changed how students spend time in the classroom. Once the digital monster escapes from the box, you can’t put it back in without causing some degree of petulance. However, you can establish a clear pattern of expectations on the first day of class, which will be the structure that you need to help others deal with their addictions. This requires being proactive and isn’t something that you can effectively deal with mid-semester.

  • There is a huge amount of freedom. You have your ID, your email set up, your class schedule, supplies on the way to the lab. And then, you have absolutely nobody telling you what to do. This is, I argue, the most critical moment in your career – how do you spend the limited amount of time that you have? Are you focusing on writing grants, getting projects started, training new students, developing some curriculum, getting new experimental setups running, figuring out which grocery story to shop in, and how to make new friends in a new city? You can’t do all of these things at once, even if they all have to happen at some point. Your priorities will be based on your own circumstances, but don’t fall into a routine or a rut without planning. If you fall into a hole in which 100% of your work time is focused on the classroom, you might never be able to dig your way out. Manage your time at the outset. Of course you’re teaching more your first semesters as you are figuring things out. But it should not be all of the time, even at the start.

  • The most important person in the world can be your departmental admin person. Missing some office furniture? Direct deposit messed up? No book ordered for your course? Copier eating paper? Lab techs are often just as critical, too. Fortunately, I’m blessed with the most spectacular crew ever in my own department. I usually see these people because I need something, and I’m ever so thankful for the help I receive. Be sure to start off on a good foot because at crunch time, having these people in your corner is definitely priceless.

  • It takes years to understand university politics. This stuff affects you, but discussing the prospect for change might not be helpful. Most issues have long histories connected to big personalities, and until you know the stories and the individual players, don’t get involved.

  • If you’re a parent, and particularly if you’re a mom, then you’ve got to make sure that your spouse does his fair share of parenting. Even if you’re not a parent, but if you’re coupled, then you want to make sure that you aren’t doing more than your fair share of the duties at home. Oftentimes, domestic arrangements re-equilibrate with moving. If your career is as important as your spouse’s career, then less pleasant stuff done at home is an equal responsibility, too.

  • Identify senior faculty that you like and can trust, and not necessarily just in your own department. The working conditions and expectations of new faculty are different than those that have been on campus for a while. However, experience sometimes results in wisdom. When you need to learn context, it’s worthwhile to talk with someone who has already been there. Let’s say a couple students in your class are causing problems for you, or you don’t know how to ask the chair about leaving for a week to attend a conference. Or you need to find fresh undergrads to train in your lab, or you want to tap into campus funding for students but don’t know criteria the university-level committee uses when ranking applications. These are topics for your senior faculty mentors.

  • Maintain the time to keep yourself healthy. Make sure you still make the effort to prepare and eat real food, and be physically active however you have in the past. The time you put into exercising doesn’t cut your productivity, but increases it. When you feel good, you’ll work more efficiently and your mind will be more focused.

  • It’s okay to ask for help. You might be anxious about driving people crazy with a variety of minor inquiries, but you’re a newbie and it’s normal to try to figure things out. You were hired because the department already was confident that you’d do a good job, so it’s okay to ask questions that will help you out. Actually, as you make the rounds asking minor questions of people who could be of help, this can be a way to figure out who might evolve to become a trusted mentor.

This was not intended to be a comprehensive list, so additional input would be great, especially from those who have started a new job more recently than I have.

I also like all of the advice of Karen Kelsky about starting out your first year. I wrote this before I saw her piece, and the similarities are more than conincidental.

Maybe I am a writer after all

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I’ve been head down, focusing on writing grants lately. These days I spend a good deal of my time writing and thinking about writing, which isn’t what I imagined life as a scientist to be.

When I was much younger, I wanted to be a writer. I read voraciously. Mainly fantasy novels and classics like Jane Austen and Lucy Maud Montgomery. I spent a lot of time out in the fields and woods around the places we lived and in my head in worlds far from my own. Being a writer sounded so romantic. But along the way that idea faded. Writing in my English classes was uninspiring and the one thing I didn’t do was write, which is of course what makes one a writer. I continued to read with my tastes broadening (but I still enjoy a good fantasy novel when I get the chance) but honestly I didn’t write that much and most of that was because I had to.

Fast-forward to my first undergraduate research project, I was working on sex-allocation in plants. The measurements came fairly easy (besides all the time they took) but once I had a complete and analyzed dataset, then came the writing. It was my first experience writing and rewriting and rewriting something. And then there was submitting it to a journal and rewriting again. I never had worked so hard at writing something but I definitely done so since then.

As my career in science has progressed, I’ve needed to take writing seriously. As an undergrad, I really had no idea how much writing was involved in most scientific fields. Unfamiliar with such things as peer-review, I was ignorant about the process between doing research and published papers.

These days I’ve published a modest number of papers but the stories behind them have really helped me grow as a writer. There was that paper that we decided to cut a significant number of words (I can’t remember the number but maybe a quarter of the paper) to try for a journal with a strict word limit (where it was rejected from). It meant looking at every single sentence to see if every word was truly necessary. The process was kind of fun and became a little like a game or puzzle. I’m still overly wordy at times but now I’m better at slashing in the later drafts. Then there was that time our paper kept getting rejected and we realized (read: my co-author because I didn’t even want to think about it anymore) that the entire introduction needed to be reframed. So we basically tossed the intro and discussion and started again. It was painful but ultimately what needed to be done. What was there before wasn’t bad writing but was setting up expectations that weren’t fulfilled by our data.

Through all of this and especially writing here, I realised that I became a writer with out even realizing it. My science has taught me more about the craft of writing than any of the English classes I took ever did (but to be fair I stopped taking these after first year of my undergraduate degree). I’m not sure if I’ll ever tackle a fiction story, and that is ok. I turned into a different kind of writer than my childhood self imagined. And I know there is a whole other craft of understanding how to construct a story, which is very different than writing a paper or a grant proposal or a blog post. I’m not arrogant enough to think my writing is a universal skill but if I did want to write a novel I now have a better idea of what that might take (writing and rewriting and rewriting and repeat).

There are lots of scientists who also write books for more general audiences suggesting that the transition from scientist to what most would consider a writer isn’t that farfetched. This Christmas I enjoyed the writing of one of my favourite people from my graduate school days, Harry Greene. “Tracks and Shadows” is a lovely, often poetic read about life as a field biologist, snakes and much more. And I haven’t picked it up yet but another Cornellian I knew has gone on to do science television and write “Mother Nature is Trying to Kill You”. It looks fun. These examples of scientists I know writing books also speak to the possibility of writing beyond scientific papers. And as the Anne Shirley books taught me, you should write what you know.

Maybe someday I’ll decide to write a book, but for now, back to those grants.

I own my data, until I don’t.

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Science is in the middle of a range war, or perhaps a skirmish.

Ten years ago, I saw a mighty good western called Open Range. Based on the ads, I thought it was just another Kevin Costner vehicle. But Duncan Shepherd, the notoriously stingy movie critic, gave it three stars. I not only went, but also talked my spouse into joining me. (Though she needs to take my word for it, because she doesn’t recall the event whatsoever.)

The central conflict in Open Range is between fatcat establishment cattle ranchers and a band of noble itinerant free grazers. The free grazers roam the countryside with their cows in tow, chewing up the prairie wherever they choose to meander. In the time the movie was set, the free grazers were approaching extirpation as the western US was becoming more and more subdivided into fenced parcels. (That’s why they filmed it in Alberta.) To learn more about this, you could swing by the Barbed Wire Museum.

The ranchers didn’t take kindly to the free grazers using their land. The free grazers thought, well, that free grazing has been a well-established practice and that grass out in the open should be free.

If you’ve ever passed through the middle of the United States, you’d quickly realize that the free grazers lost the range wars.

On the prairie, what constitutes community property? If you’re on loosely regulated public land administered by the Bureau of Land Management, then you can use that land as you wish, but for certain uses (such as grazing), you need to lease it from the government. You can’t feed your cow for free, nowadays. That community property argument was settled long ago.

Now to the contemporary range wars in science: What constitutes community property in the scientific endeavor?

In recent years, technological tools have evolved such that scientists can readily share raw datasets with anybody who has an internet connection. There are some who argue that all raw data used to construct a scientific paper should become community property. Some have the extreme position that as soon as a datum is collected, regardless of the circumstances, it should become public knowledge as promptly as it is recorded. At the other extreme, some others think that data are the property of the scientists who created them, and that the publication of a scientific paper doesn’t necessarily require dissemination of raw data.

Like in most matters, the opinions of most scientists probably lie somewhere between the two poles.

The status quo, for the moment, is that most scientists do not openly disseminate their raw data. In my field, most new papers that I encounter are not accompanied with fully downloadable raw datasets. However, some funding agencies are requiring the availability of raw data. There are a few journals of which I am aware that require all authors to archive data upon publication, and there are many that support but do not require archiving.

The access to other people’s data, without the need to interact with the creators of the data, is increasing in prevalence. As the situation evolves, folks on both sides are getting upset at the rate of change – either it’s too slow, or too quick, or in the wrong direction.

Regardless of the trajectory of “open science,” the fact remains that, at the moment, we are conducing research in a culture of data ownership. With some notable exceptions, the default expectation is that when data are collected, the scientist is not necessarily obligated to make these data available to others.

Even after a paper is published, there is no broadly accepted community standard that the data that resulted in the paper become public information. On what grounds do I assert this? Well, last year I had three papers come out, all of which are in reputable journals (Biotropica, Naturwissenschaften, and Oikos, if you’re curious). In the process of publishing these papers, nobody ever even hinted that I could or should share the data that I used to write these papers. This is pretty good evidence that publishing data is not yet standard practice, though things are slowly moving in that direction. As evidence, I just got an email from Oikos as a recent author asking me to fill out a survey to let them know how I feel about data archiving policies for the journal.

As far as the world is concerned, I still own the data from those three papers published last year. If you ask me for the data, I’d be glad to share them with you after a bit of conversation, but for the moment, for most journals it seems to be my choice. I don’t think any of those three journals have a policy indicating that I need to share my dataset with the public. I imagine this could change in the near future.

I was chatting with a collaborator a couple weeks ago (working on “paper i”) and we were trying to decide where we should send the paper. We talked about PLOS ONE. I’ve sent one paper to this journal, actually one of best papers. Then I heard about a new policy of the journal to require public archiving of datasets from all papers published in the journal.

All of sudden, I’m less excited about submitting to this journal. I’m not the only one to feel this way, you know.

Why am I sour on required data archiving? Well, for starters, it is more work for me. We did the field and lab work for this paper during 2007-2009. This is a side project for everybody involved and it’s taken a long time to get the activation energy to get this paper written, even if the results are super-cool.

Is that my fault that it’ll take more work to share the data? Sure, it’s my fault. I could have put more effort into data management from out outset. But I didn’t, as it would have been more effort, and kept me from doing as much science as I have done. It comes with temporal overhead. Much of the data were generated by an undergraduate researcher, a solid scientist with decent data management practices. But I was working with multiple undergraduates in the field in that period of time, and we were getting a lot done. I have no doubts in the validity of the science we are writing up, but I am entirely unthrilled about cleaning up the dataset and adding the details into the metadata for the uninitiated. And, our data are a combination of behavioral bioassays, GC-MS results from a collaborator, all kinds of ecological field measurements, weather over a period of months, and so on. To get these numbers into a downloadable and understandable condition would be, frankly, an annoying pain in the ass. And anybody working on these questions wouldn’t want the raw data anyway, and there’s no way these particular data would be useful in anybody’s meta analysis. It’d be a huge waste of my time.

Considering the time it takes me to get papers written, I think it’s cute that some people promoting data archiving have suggested a 1-year embargo after publication. (I realize that this is a standard timeframe for GenBank embargoes.) The implication is that within that one year, I should be able to use that dataset for all it’s worth before I share it with others. We may very well want to use these data to build a new project, and if I do, then it probably would be at least a year before we head back to the rainforest again to get that project done. At least with the pace of work in my lab, an embargo for less than five years would be useless to me.

Sometimes, I have more than one paper in mind when I am running a particular experiment. More often, when writing a paper, I discover the need to write different one involving the same dataset (Shhh. Don’t tell Jeremy Fox that I do this.) I research in a teaching institution, and things often happen at a slower pace than at the research institutions which are home to most “open science” advocates. Believe it or not, there are some key results from a 15-year old dataset that I am planning to write up in the next few years, whenever I have the chance to take a sabbatical. This dataset has already been featured in some other papers.

One of the standard arguments for publishing raw datasets is that the lack of full data sharing slows down the progress of science. It is true that, in the short term, more and better papers might be published if all datasets were freely downloadable. However, in the long term, would everybody be generating as much data as they are now? Speaking only for myself, if I realized that publishing a paper would require the sharing of all of the raw data that went into that paper, then I would be reluctant to collect large and high-risk datasets, because I wouldn’t be sure to get as large a payoff from that dataset once the data are accessible.

Science is hard. Doing science inside a teaching institution is even harder. I am prone isolation from the research community because of where I work. By making my data available to others online without any communication, what would be the effect of sharing all of my raw data? I could either become more integrated with my peers, or more isolated from them. If I knew that making my data freely downloadable would increase interactions with others, I’d do it in a heartbeat. But when my papers get downloaded and cited I’m usually oblivious to this fact until the paper comes out. I can only imagine that the same thing could happen with raw data, though the rates of download would be lower.

In the prevailing culture, when data are shared, along with some other substantial contributions, that’s standard grounds for authorship. While most guidelines indicate that providing data to a collaborator is not supposed to be grounds for authorship, the current practice is that it is grounds for authorship. One can argue that it isn’t fair nor is it right, but that is what happens. Plenty of journals require specification of individual author contributions and require that all authors had a substantial role beyond data contribution. However, this does not preclude that the people who provide data do not become authors.

In the culture of data ownership, the people who want to write papers using data in the hands of other scientists need to come to an agreement to gain access to these data. That agreement usually involves authorship. Researchers who create interesting and useful data – and data that are difficult to collect – can use those data as a bargaining chip for authorship. This might not be proper or right, and this might not fit the guidelines that are published by journals, but this is actually what happens.

This system is the one that  “open science” advocates want to change. There are some databases with massive amounts of ecological and genomic data that other people can use, and some people can go a long time without collecting their own data and just use the data of others. I’m fine with that. I’m also fine with not throwing my data in to the mix.

My data are hard-won, and the manuscripts are harder-won. I want to be sure that I have the fullest opportunity to use my data before anybody else has the opportunity. In today’s marketplace of science, having a dataset cited in a publication isn’t much credit at all. Not in the eyes of search committees, or my Dean, or the bulk of the research community. The discussion about the publication of raw data often avoids tacit facts about authorship and the culture of data ownership.

To be able to collect data and do science, I need grant money.

To get grant money, I need to give the appearance of scientific productivity.

To show scientific productivity, I need to publish a bunch of papers.

To publish a bunch of papers, I need to leverage my expertise to build collaborations.

To leverage my expertise to build collaborations, I need to have something of quality to offer.

To have something of quality to offer, I need to control access to the data that I have collected. I don’t want that to stop after publication.

The above model of scientific productivity is part of the culture of data ownership, in which I have developed my career at a teaching institution. I’m used to working amicably and collaboratively, and the level of territoriality in my subfields is quite low. I’ve read the arguments, but I don’t see how providing my data with no strings attached would somehow build more collaborations for me, and I don’t see how it would give me any assistance in the currency that matters. I am sure that “open science” advocates are wholly convinced that putting my data online would increase, rather than constrict opportunities for me. I am not convinced, yet, though I’m open to being convinced. I think what will convince me is seeing a change in the prevailing culture.

There is one absurdity to these concerns of mine, that I’m sure critics will have fun highlighting. I doubt many people would be downloading my data en masse. But, it’s not that outlandish, and people have done papers following up on my own work after communicating with me. I work at a field site where many other people work; a new paper comes out from this place every few days. I already am pooling data with others for collaborations. I’d like to think that people want to work with me because of what I can bring to the table other than my data, but I’m not keen on testing that working hypothesis.

Simply put, in today’s scientific rewards system, data are a currency. Advocates of sharing raw data may argue that public archiving is like an investment with this currency that will yield greater interest than a private investment. The factors that shape whether the yield is greater in a public or private investment of the currency of data are complicated. It would be overly simplistic to assert that I have nothing to lose and everything to gain by sharing my raw data without any strings attached.

While good things come to those who are generous, I also have relatively little to give, and I might not be doing myself or science a service if I go bankrupt. Anybody who has worked with me will report (I hope) that am inclusive and giving with what I have to offer. I’ve often emailed datasets without people even asking for them, without any restrictions or provisions. I want my data to be used widely. But even more, I want to be involved when that happens.

Because I run a small operation in a teaching institution, my research program experiences a set of structural disadvantages compared to colleagues at an R1 institution. The requirement to share data levies the disadvantage disproportionately against researchers like myself, and others with little funding to rapidly capitalize on the creation of quality data.

To grow a scientific paper, many ingredients are required. As grass grows the cow, data grows a scientific paper.

In Open Range, the resource in dispute is not the grass, but the cows. The bad guy ranchers aren’t upset about losing the grass, they just don’t want these interlopers on their land. It’s a matter of control and territoriality. At the moment, the status quo is that we run our own labs, and the data growing in these labs are also our property.

When people don’t want to release their data, they don’t care about the data itself. They care about the papers that could result from these data. I don’t care if people have numbers that I collect. What I care about is the notion that these numbers are scientifically useful, and that I wish to get scientific credit for the usefulness of these numbers. Once the data are public, there is scant credit for that work.

It takes plenty of time and effort to generate data. In my case, lots of sweat, and occasionally some venom and blood, is required to generate data. I also spend several weeks per year away from my family, which any parent should relate with. Many of the students who work with me also have made tremendous personal investments into the work as well. Generating data in my lab often comes at great personal expense. Right now, if we publicly archived data that were used in the creation of a new paper, we would not get appropriate credit in a currency of value in the academic marketplace.

When a pharmaceutical company develops a new drug, the structure of the drug is published. But the company has a twenty year patent and five years of exclusivity. It’s widely claimed – and believed – that without the potential for recouping the costs of work in developing medicines that pharmaceutical companies wouldn’t jump through all the regulatory hoops to get new drugs on the market. The patent provides incentive for drug production. Some organizations might make drugs out of the goodness of their hearts, but the free market is driven by dollars. An equivalent argument could be wagered for scientists wishing for a very long time window to reap the rewards of producing their own data.

In the United States, most meat that people consume doesn’t come from grass on the prairie, but from corn grown in an industrial agricultural setting. Likewise, most scientific papers that get published come from corn-fed data produced by a laboratory machine designed to crank out a high output of papers. Ranchers stay in business by producing a lot of corn, and maximizing the amount of cow tissue that can be grown with that corn. Scientists stay in business by cranking out lots of data and maximizing how many papers can be generated from those data.

Doing research in a small pond, my laboratory is ill equipped to compete with the massive corn-fed laboratories producing many heads of cattle. Last year was a good year for me, and I had three papers. That’s never going to be able to compete with labs at research institutions — including the ones advocating for strings-free access to everybody’s data.

The movement towards public data archiving is essentially pushing for the deprivatization of information. It’s the conversion of a private resource into a community resource. I’m not saying this is bad, but I am pointing out this is a big change. The change is biggest for small labs, in which each datum takes a relatively greater effort to produce, and even more effort to bring to publication.

So far, what I’ve written is predicated on the notion that researchers (or their employers) actually have ownership of the data that they create. So, who actually owns data? The answer to that question isn’t simple. It depends on who collected it, who funded the collection of the data, and where the data were published.

If I collect data on my own dime, then I own these data. If my data were collected under the funding support of an agency (or a branch of an agency) that doesn’t require the public sharing of the raw data, then I still own these data. If my data are published in a journal that doesn’t require the publication of raw data, I still own these data.

It’s fully within the charge of NIH, NSF, DOE, USDA, EPA and everyone else to require the open sharing of data collected under their support. However, federal funding doesn’t necessarily necessitate public ownership (see this comment in Erin McKiernan’s blog for more on that.) If my funding agency, or some federal regulation, requires that my raw data be available for free downloads, then I no longer own these data. The same is true if a journal has a similar requirement. Also, if I choose to give away my data, then I no longer own them.

So, who is in a position to tell me when I need to make my data public? My program officer, or my editor.

If you wish, you can make it your business by lobbying the editors of journals to change their practices, and you can lobby your lawmakers and federal agencies for them to require and enforce the publication of raw datasets.

I think it’s great when people choose to share data. I won’t argue with the community-level benefits, though the magnitude of these benefits to the community vary with the type of data. In my particular situation, when I weigh the scant benefit to the community relative to the greater cost (and potential losses) to my research program, the decision to stay the course is mighty sensible.

There are some well-reasoned folks, who want to increase the publication of raw datasets, who understand my concerns. If you don’t think you understand my concerns, you really need to read this paper. In this paper, they had four recommendations for the scientific community at large, all of which I love:

  1. Facilitate more flexible embargoes on archived data
  2. Encourage communication between data generators and re-users
  3. Disclose data re-use ethics
  4. Encourage increased recognition of publicly archived data.

(It’s funny, in this paper they refer to the publication of raw data as “PDA” (public data archiving), but at least here in the States, that acronym means something else.)

And they’re right, those things will need to happen before I consider publishing raw data voluntarily. Those are the exact items that I brought up as my own concerns in this post. The embargo period would need to be far longer, and I’d want some reassurance that the people using my data will actually contact me about it, and if it gets re-used, that I have a genuine opportunity for collaboration as long as my data are a big enough piece. And, of course, if I don’t collaborate, then the form of credit in the scientific community will need to be greater than what happens now, which is getting just cited.

The Open Data Institute says that “If you are publishing open data, you are usually doing so because you want people to reuse it.” And I’d love for that to happen. But I wouldn’t want it to happen without me, because in my particular niche in the research community, the chance to work with other scientists is particularly valuable. I’d prefer that my data to be reused less often than more often, as long as that restriction enabled me more chances to work directly with others.

Scientists at teaching institutions have a hard time earning respect as researchers (see this post and read the comments for more on that topic). By sharing my data, I realize that I can engender more respect. But I also open myself up to being used. When my data are important to others, then my colleagues contact me. If anybody feels that contacting me isn’t necessary, then my data are not apparently necessary.

Is public data archiving here to stay, or is it a passing fad? That is not entirely clear.

There is a vocal minority that has done a lot to promote the free flow of raw data, but most practicing scientists are not on board this train. I would guess that the movement will grow into an establishment practice, but science is an odd mix of the revolutionary and the conservative. Since public data archiving is a practice that takes extra time and effort, and publishing already takes a lot work, the only way will catch on is if it is required. If a particular journal or agency wants me to share my data, then I will do so. But I’m not, yet, convinced that it is in my interest.

I hope that, in the future, I’ll be able to write a post in which I’m explaining why it’s in my interest to publish my raw data.

The day may come when I provide all of my data for free downloads, but that day is not today.

I am not picking up a gun in this range war. I’ll just keep grazing my little herd of cows in a large fragment of rainforest in Sarapiquí, Costa Rica until this war gets settled. In the meantime, if you have a project in mind involving some work I’ve done, please drop me a line. I’m always looking for engaged collaborators.

Getting back to the routine.

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Last year was a pretty big one for me, both personally and professionally. We bought a row house and moved to the city where my husband works, meaning a significantly different commute for me. I also interviewed for two permanent faculty jobs here in Sweden but was offered neither. I started chatting on twitter and writing here. All in all, despite some disappointments, it was a good year for learning and exploring. I’m really excited with the direction both my personal and professional life is going. But by the end of this busy year of challenges and changes, my whole family was exhausted.

This Christmas/New Year holiday, we decided to stay at home. We had some friends visit and share celebrations but we stayed put. Having a 4 year old means that we are also pleasantly forced into taking a real holiday. When the daycare closes, it is family time. In Sweden, Jan 6th is also a holiday, so today is the first day back to reality.

Living without a schedule for a couple of weeks has been relaxing. We enjoyed lazy mornings and unstructured days. Without setting out to do it, this break also became a ‘get fit’ holiday. The weather was depressing here in Sweden; no white Christmas for us. Given that we’re so far north that also means that it is dark and the rainy grey weather really hasn’t helped. But the relatively warm weather was good for getting us out for regular runs. I’m hoping we can continue regular exercise as the semester gears up but I know that it will be harder when balancing few daylight hours, commuting and working.

Academia is a funny place for schedules. Although when you are teaching there is little flexibility for those hours and as the semester gets back under way departmental seminars and meetings start to fill up your schedule, much of our time is quite flexible. There are lots of demands on that time but how you arrange it is often up to you. As a grad student, I used to be much more irregular in my working time, working early or late as it suited. Now with the twin pressures of a commuting schedule and daycare opening hours, my schedule is pretty much set each day. That always makes it challenging to get back in the rhythm after some time away. So this week we all face the challenge of getting back into the regular routine.

How scheduled are your days? Whether you took real holidays or just got away from the scheduled pressures of the semester this break, how do you get back into the routine? I always find that it takes me a bit to get back to being efficient and productive after setting things aside. So I usually tackle small tasks and try to cross off as many things off of a to do list as possible to get me back into work. It helps me feel productive and get a handle on what needs to be done. My first day back includes really reading the review from our recently rejected paper and seeing what to change before submitting elsewhere, pursuing a few papers for a meeting about potential collaborations later this week, finishing up commenting on a student’s work, writing a few emails to collaborators and planning more seriously for the coming days. I’m off to North America in less than two weeks so that should give me just enough time to get back into the swing of things before completely throwing off my schedule again!

Journal prestige and publishing from a teaching institution

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Finally. There are journals publishing quality peer-reviewed research, but leave it to the reader to decide whether a paper is sexy or important. Shouldn’t this be better than letting a few editors and reviewers reject work based on whether they personally think that a paper is important or significant?

This newish type of journal uses editors and reviewers to assure quality and accuracy. The biggie is PLoS ONE. A newer one on the block is PeerJ. Another one asked me to shill for them on this site.

The last few years have seen a relatively quick shift in scientific publishing models, and there has been a great upheaval in journals in which some new ones have become relatively prestigious (e.g., Ecology Letters) and some well-established journals have experienced a decline in relative rank (e.g., American Journal of Botany). These hierarchies have a great effect on researchers publishing from small ponds.

Publishing in selective journals is required to establish legitimacy. This is true for everybody. Because researchers in small teaching institutions are inherently legitimacy-challenged, then this is the population that most heavily relies on this mechanism of legitimacy.

Researchers in teaching institutions don’t have a mountain of time for research. Just think about all of the time that could be spent on genuine research, instead of time wasted in the mill of salesmanship that is required to publish in selective journals. (I also find that pitching research as a theory-of-the-moment to be one of the most annoying parts of the business.)

With new journals that verify quality but not the sexiness, we can hop off the salesmanship game and just get stuff published. Sounds great, right?

After all, the research that takes place at teaching institutions can be of high quality and significant within our fields. But, on average, we just don’t publish as much. That makes sense because our employers expect us to focus on teaching above all else.

Since we’re less productive, then every paper counts. We want to get our research out there, but we also need to make sure that every paper represents us well. What we lack for in quantity, we need to make up for in (perceived) quality.

How do people assess research quality? The standard measure is the selectivity of the journal that publishes the paper. It’s natural to think that a paper in American Naturalist (impact factor 4.7) is going to be higher quality than American Midland Naturalist (impact factor 0.6).

People make these judgments all the time. It might not be fair, but it’s normal.

And no matter how dumb people say it might be, no matter how many examples are brought up, assessments of ‘journal quality’ aren’t going away. No matter how much altmetrics picks up as another highly flawed measure of research quality, the name of the journal that publishes a paper really matters. That isn’t changing anytime soon.

The effect of paper on the research community is tied to the prestige of the venue, as well as the prestige of the authors. Fame matters. If any researcher – including those of us at teaching institutions – wants to build an influential research program, we’ve got to build up a personal reputation for high quality research.

Building a reputation for high quality research is not easy at all, but it’s even harder while based at a teaching institution. Just like having a paper in a prestigious journal is supposed to be an indicator of quality research, a faculty position at a well-known research institution is supposed to be an indicator of a quality researcher. Since our institutional affiliations aren’t contributing to our research prestige, we need to make the most of the circumstances to establish the credibility and status of the work that comes out of our labs.

If journal hierarchies didn’t exist, it would be really hard for researchers in lesser-known institutions, who may not publish frequently, to readily convince others that their work is of high quality. Good work doesn’t get cited just because it’s good. It needs to be read first. And work in non-prestigious journals may simply go unread if the author isn’t already well known.

If journal hierarchies somehow faded, it’s not as if the perception of research quality would evolve into some perfect meritocracy. There are lots of conscious and unconscious biases, aside from quality, that affect whether or not work gets into a fancy-pants journal, but it is true that people without a fancy-pants background still can publish in elite venues based on the quality of their work. This means that people without an elite background can gain a high profile based on merit, though they do need to persevere though the biases working against them.

If journals themselves merely published work but without any prestige associated with them, then it would be even more difficult for people without well-connected networks to have their work read and cited. It wouldn’t democratize access to science; it would inherently favor the scientists with great connections. At least now, the decisions of a small number of editors and reviewers can put science from an obscure venue into a position where a large audience will see it. On the other hand, publishing in a journal without any prestige, like PLoS ONE, will allow work to be available to a global audience, but actually read by very few.

If I want my work to be read by ecologists, then publishing it in a perfectly good journal like Oikos will garner me more readers than if I publish it in PLoS ONE. Moreover, people will look at the Oikos paper and realize that at some point in its life, there was a set of reviewers and an editor who agreed that the paper was not only of high quality but also interesting or sexy enough to be accepted. It wasn’t just done well, but it’s also useful or important to the field. That can’t necessarily be said of all PLoS ONE papers.

Not that long ago, I thought that these journals lacking the exclusivity factor were a great thing because it allowed everybody equal access to research. What changed my mind? The paper that I chose to place in PLoS ONEI chose to put a paper that I was really excited about in this journal. It was a really neat discovery, and should lead to a whole new line of inquiry. (Also, the editorial experience was great, the reviewers were very exacting but even-handed, and the handling editor was top notch.)

Since that paper has come out just over a year ago, there have been a number of new papers on this or a closely related topic. But my paper has not been cited yet, even though it really should have been cited. Meanwhile they’re citing my older, far less interesting and useful, paper on the same topic from 2002.

Why has nobody cited the more recent paper? Either people think that it’s not relevant, not high enough quality, or they never found it. (Heck, the blog post about it has been seen more times than the paper itself.) Maybe people found it and then didn’t read it because of the journal. It’s really a goddamn great paper. And it’s getting ignored because I put in PLoS ONE. I have very little doubt that if I chose to put it in a specialized venue like Insectes Sociaux or Myrmecological News, both good journals that are read by social insect biologists, that it would be read more heavily and have been cited at least a few times. This paper could have been in an even higher profile journal, because it’s so frickin’ awesome, but I chose to put it in PLoS ONE. Oh well, I’ve learned my lesson. There are some papers in that venue that get very highly cited, but I think most things in there just get lost.

I would love for people to judge a paper based on the quality of its content rather than the name of the journal. But most people don’t do this. And I’m not going to choose to publish in a venue that may lead people to think that the work isn’t interesting or groundbreaking even before they have chosen to (not) read it. I’ll admit to not placing myself on the front of reform in scientific publishing, even if I make all of my papers immediately and universally available. I have to admit that I’m apt to select a moderately selective venue when possible, because I am concerned that people see my research as not only legitimate but also worthwhile. I’m not worried that my stuff isn’t quite good, but I want to make sure it’s not done in vain. Science is a social enterprise, and as a working scientist I need to put my work into the conversation.

A snapshot of the publication cycle

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I was recently asked:

Q: How do you decide what project you work on?

A: I work on the thing that is most exciting at the moment. Or the one I feel most bad about.

In the early stages, the motivator is excitement, and in the end, the motivator is guilt. (If I worked in a research institution, I guess an additional motivator would be fear.)

Don’t get me wrong: I do science because it’s tremendous fun. But the last part – finessing a manuscript through the final stages – isn’t as fun as the many other pieces. How do I keep track of the production line from conception to publication, and how do I make sure that things keep rolling?

At the top center of my computer desktop lives a document entitled “manuscript progress.” I consult this file when I need to figure out what to work on, which could involve doing something myself or perhaps pestering someone else to get something done.

In this document are three categories:

  1. Manuscript completed
  2. Paper in progress
  3. In development projects

Instead of writing about the publication cycle in the abstract, I thought it might be more illustrative to explain what is in each category at this moment. (It might be perplexing, annoying or overbearing, too. I guess I’m taking that chance.) My list is just that – a list. Here, I amplify to describe how the project was placed on the treadmill and how it’s moving along, or not moving along. I won’t bore those of you with the details of ecology, myrmecology or tropical biology, and I’m not naming names. But you can get the gist.

Any “Student” is my own student – and a “Collaborator” is anybody outside my own institution with whom I’m working, including grad students in other labs. A legend to the characters is at the end.

Manuscript completed

Paper A: Just deleted from this list right now! Accepted a week ago, the page proofs just arrived today! The idea for this project started as the result of a cool and unexpected natural history observation by Student A in 2011. Collaborator A joined in with Student B to do the work on this project later that summer. I and Collab A worked on the manuscript by email, and I once took a couple days to visit Collab A at her university in late 2011 to work together on some manuscripts. After that, it was in Collab A’s hands as first author and she did a rockin’ job (DOI:10.1007/s00114-013-1109-3).

Paper B: I was brought in to work with Collab B and Collab C on a part of this smallish-scale project using my expertise on ants. I conducted this work with Student C in my lab last year and the paper is now in review in a specialized regional journal (I think).

Paper C: This manuscript is finished but not-yet-submitted work by a student of Collab D, which I joined in by doing the ant piece of the project. This manuscript requires some editing, and I owe the other authors my remarks on it. I realize that I promised remarks about three months ago, and it would take only an hour or two, so I should definitely do my part! However, based on my conversations, I’m pretty sure that I’m not holding anything up, and I’m sure they’d let me know if I was. I sure hope so, at least.

Paper D: The main paper out of Student A’s MS thesis in my lab. This paper was built with from Collab E and Collab F and Student D. Student A wrote the paper, I did some fine-tuning, and it’s been on a couple rounds of rejections already. I need to turn it around again, when I have the opportunity. There isn’t anything in the reviews that actually require a change, so I just need to get this done.

Paper E: Collab A mentored Student H in a field project in 2011 at my field site, on a project that was mostly my idea but refined by Collab A and Student H. The project worked out really well, and I worked on this manuscript the same time as Paper A. I can’t remember if it’s been rejected once or not yet submitted, but either way it’s going out soon. I imagine it’ll come to press sometime in the next year.

Manuscripts in Progress

Paper F: Student D conducted the fieldwork in the summer of 2012 on this project, which grew out of a project by student A. The data are complete, and the specific approach to writing the paper has been cooked up with Student D and myself, and now I need to do the full analysis/figures for the manuscript before turning it off to StudentD to finish. She is going away for another extended field season in a couple months, and so I don’t know if I’ll get to it by then. If I do, then we should submit the paper in months. If I don’t, it’ll be by the end of 2014, which is when Student D is applying to grad schools.

Paper G: Student B conducted fieldwork in the summer of 2012 on a project connected to a field experiment set up by Collab C. I spent the spring of 2013 in the lab finishing up the work, and I gave a talk on it this last summer. It’s a really cool set of data though I haven’t had the chance to work it up completely. I contacted Collab G to see if he had someone in his lab that wanted to join me in working on it. Instead, he volunteered himself and we suckered our pal Collab H to join us in on it. The analyses and writing should be straightforward, but we actually need to do it and we’re all committed to other things at the moment. So, now I just need to make the dropbox folder to share the files with those guys and we can take the next step. I imagine it’ll be done somewhere between months to years from now, depending on how much any one of us pushes.

Paper H: So far, this one has been just me. It was built on a set of data that my lab has accumulated over few projects and several years. It’s a unique set of data to ask a long-standing question that others haven’t had the data to approach. The results are cool, and I’m mostly done with them, and the manuscript just needs a couple more analyses to finish up the paper. I, however, have continued to be remiss in my training in newly emerged statistical software. So this manuscript is either waiting for myself to learn the software, or for a collaborator or student eager to take this on and finish up the manuscript. It could be somewhere between weeks to several years from now.

Paper I: I saw a very cool talk by someone a meeting in 2007, which was ripe to be continued into a more complete project, even though it was just a side project. After some conversations, this project evolved into a collaboration, with Student E to do fieldwork in summer 2008 and January 2009. We agreed that Collab I would be first author, Student E would be second author and I’d be last author. The project is now ABM (all but manuscript), and after communicating many times with Collab I over the years, I’m still waiting for the manuscript. A few times I indicated that I would be interested in writing up our half on our own for a lower-tier journal. It’s pretty much fallen off my radar and I don’t see when I’ll have time to write it up. Whenever I see my collaborator he admits to it as a source of guilt and I offer absolution. It remains an interesting and timely would-be paper and hopefully he’ll find the time to get to it. However, being good is better than being right, and I don’t want to hound Collab I because he’s got a lot to do and neither one of us really needs the paper. It is very cool, though, in my opinion, and it’d be nice for this 5-year old project to be shared with the world before it rots on our hard drives. He’s a rocking scholar with a string of great papers, but still, he’s in a position to benefit from being first author way more myself, so I’ll let this one sit on his tray for a while longer. This is a cool enough little story, though, that I’m not going to forget about it and the main findings will not be scooped, nor grow stale, with time.

Paper J: This is a review and meta-analysis that I have been wanting to write for a few years now, which I was going to put into a previous review, but it really will end up standing on its own. I am working with a Student F to aggregate information from a disparate literature. If the student is successful, which I think is likely, then we’ll probably be writing this paper together over the next year, even as she is away doing long-term field research in a distant land.

Paper K: At a conference in 2009, I saw a grad student present a poster with a really cool result and an interesting dataset that came from the same field station as myself. This project was built on an intensively collected set of samples from the field, and those same samples, if processed for a new kind of lab analysis, would be able to test a new question. I sent Student G across the country to the lab of this grad student (Collab J) to process these samples for analysis. We ran the results, and they were cool. To make these results more relevant, the manuscript requires a comprehensive tally of related studies. We decided that this is the task of Student G. She has gotten the bulk of it done over the course of the past year, and should be finishing in the next month or two, and then we can finish writing our share of this manuscript. Collab J has followed through on her end, but, as it’s a side project for both of us, neither of us are in a rush and the ball’s in my court at the moment. I anticipate that we’ll get done with this in a year or two, because I’ll have to analyze the results from Student G and put them into the manuscript, which will be first authored by Collab J.

Paper L: This is a project by Student I, as a follow-up to the project of Student H in paper E, conducted in the summer of 2013. The data are all collected, and a preliminary analysis has been done, and I’m waiting for Student I to turn these data into both a thesis and a manuscript.

Paper M: This is a project by Student L, building on prior projects that I conducted on my own. Fieldwork was conducted in the summer of 2012, and it is in the same place as Paper K, waiting for the student to convert it into a thesis and a manuscript.

Paper N: This was conducted in the field in summer 2013 as a collaboration between Student D and Student N. The field component was successful and now requires me to do about a month’s worth of labwork to finish up the project, as the nature of the work makes it somewhere between impractical and unfeasible to train the students to do themselves. I was hoping to do it this fall, to use these data not just for a paper but also preliminary data for a grant proposal in January, but I don’t think I’ll be able to do it until the spring 2014, which would mean the paper would get submitted in Fall 2014 at the earliest, or maybe 2015. This one will be on the frontburner because Students D and N should end up in awesome labs for grad school and having this paper in press should enhance their applications.

Paper O: This project was conducted in the field in summer 2013, and the labwork is now in the hands of Student O, who is doing it independently, as he is based out of an institution far away from my own and he has the skill set to do this. I need to continue communicating with this student to make sure that it doesn’t fall off the radar or doesn’t get done right.

Paper P: This project is waiting to get published from an older collaborative project, a large multi-PI biocomplexity endeavor at my fieldstation. I had a postdoc for one year on this project, and she published one paper from the project but as she moved on, left behind a number of cool results that I need to write up myself. I’ve been putting this off because it would rely on me also spending some serious lab time doing a lot of specimen identifications to get this integrative project done right. I’ve been putting it off for a few years, and I don’t see that changing, unless I am on a roll from the work for Paper N and just keep moving on in the lab.

Paper Q: A review and meta-analysis that came out of a conversation with Collabs K and L. I have been co-teaching field courses with Collab K a few times, and we share a lot of viewpoints about this topic that go against the incorrect prevailing wisdom, so we thought we’d do something about it. This emerged in the context of a discussion with L. I am now working with Student P to help systematically collect data for this project, which I imagine will come together over the next year or two, depending on how hard the pushing comes from myself or K or L. Again it’s a side project for all of us, so we’ll see. The worst case scenario is that we’ll all see one another again next summer and presumably pick things up from there. Having my student generating data is might keep the engine running.

Paper R: This is something I haven’t thought about in a year or so. Student A, in the course of her project, was able to collect samples and data in a structured fashion that could be used with the tools developed by Collab M and a student working with her. This project is in their hands, as well as first and lead authorship, so we’ve done our share and are just waiting to hear back. There have been some practical problem on their side, that we can’t control, and they’re working to get around it.

Paper S: While I was working with Collab N on an earlier paper in the field in 2008, a very cool natural history observation was made that could result in an even cooler scientific finding. I’ve brought in Collab O to do this part of the work, but because of some practical problems (the same as in Paper R, by pure coincidence) this is taking longer than we thought and is best fixed by bringing in the involvement of a new potential collaborator who has control over a unique required resource. I’ve been lagging on the communication required for this part of the project. After I do the proper consultation, if it works out, we can get rolling and, if it works, I’d drop everything to write it up because it would be the most awesome thing ever. But, there’s plenty to be done between now and then.

Paper T: This is a project by Student M, who is conducted a local research project on a system entirely unrelated to my own, enrolled in a degree program outside my department though I am serving as her advisor. The field and labwork was conducted in the first half of 2013 – and the potential long-shot result come up positive and really interesting! This one is, also, waiting for the student to convert the work into a thesis and manuscript. You might want to note, by the way, that I tell every Master’s student coming into my lab that I won’t sign off on their thesis until they also produce a manuscript in submittable condition.

Projects in development

These are still in the works, and are so primordial there’s little to say. A bunch of this stuff will happen in summer 2014, but a lot of it won’t, even though all of it is exciting.

Summary

I have a lot of irons in the fire, though that’s not going to keep me from collecting new data and working on new ideas. This backlog is growing to an unsustainable size, and I imagine a genuine sabbatical might help me lighten the load. I’m eligible for a sabbatical but I can’t see taking it without putting a few projects on hold that would really deny opportunities to a bunch of students. Could I have promoted one of these manuscripts from one list to the other instead of writing this post? I don’t think so, but I could have at least made a small dent.

Legend to Students and Collaborators

Student A: Former M.S. student, now entering her 2nd year training to become a D.P.T.; actively and reliably working on the manuscript to make sure it gets published

Student B: Former undergrad, now in his first year in mighty great lab and program for his Ph.D. in Ecology and Evolutionary Biology

Student C: Former undergrad, now in a M.S. program studying disease ecology from a public health standpoint, I think.

Student D: Undergrad still active in my lab

Student E: Former undergrad, now working in biology somewhere

Student F: Former undergrad, working in my lab, applying to grad school for animal behavior

Student G: Former undergrad, oriented towards grad school, wavering between something microbial genetics and microbial ecology/evolution (The only distinction is what kind of department to end up in for grad school.)

Student H: Former undergrad, now in a great M.S. program in marine science

Student I: Current M.S. student

Student L: Current M.S. student

Student M: Current M.S. student

Student N: Current undergrad, applying to Ph.D. programs to study community ecology

Student O: Just starting undergrad at a university on the other side of the country

Student P: Current M.S. student

Collab A: Started collaborating as grad student, now a postdoc in the lab of a friend/colleague

Collab B: Grad student in the lab of Collab C

Collab C: Faculty at R1 university

Collab D: Faculty at a small liberal arts college

Collab E: Faculty at a small liberal arts college

Collab F: International collaborator

Collab G: Faculty at an R1 university

Collab H: Started collaborating as postdoc, now faculty at an R1 university

Collab I: Was Ph.D. student, now faculty at a research institution

Collab J: Ph.D. student at R1 university

Collab K: Postdoc at R1 university, same institution as Collab L

Collab L: Ph.D. student who had the same doctoral PI as Collab A

Collab M: Postdoc at research institution

Collab N: Former Ph.D. student of Collab H.; postdoc at research institution

Collab O: Faculty at a teaching-centered institution similar to my own

By the way, if you’re still interested in this topic, there was also a high-quality post on the same topic on Tenure, She Wrote, using a fruit-related metaphor with some really nice fruit-related photos.

Making the most of a daily commute (guest post)

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Amy Parachnowitsch is an ex-pat Canadian scientist, who studies the evolutionary ecology of plant-animal interactions and floral traits. She is halfway through a 4-year assistant professor position at Uppsala University, Sweden.

Maybe it is the recent discussion about field sites both far and wide (both highlighted here) that has got me thinking about distances and research/work. Or maybe it just because I’ve been sitting on a bus trying to make the most of my newly acquired commute. Whatever the reason, I have been thinking about how travel, whether daily or to your field sites, can affect how you arrange your work.

A little context: this summer I moved from the town where I work to one an hour away. There were a lot of personal reasons that went into the decision but one of the main factors was my work flexibility compared to my husband’s. Unlike him, I can use the commute to work and have shorter days in the office. A good theory but now I’m actually testing it during the one hour (each way) I spend on a bus. Right now my days are about two hours shorter in the office than they were when we lived a ~15min bike ride away.

So far, I am seeing some pluses to the arrangement. When I’m in my office, my time tends to get fragmented during the day by various interruptions. An hour on the bus is long enough to pull something out and get somewhere with it. One of the big benefits is that I am not completely connected; I do have a smart phone but I keep my computer off-line. So far this has functioned to give me some solid writing chunks during the day. There is lots of advice out there about writing daily and it has always seemed like a good idea to me. But this commute is the first time I’ve been consistent with a daily writing routine—we’ll see if it results in more submitted manuscripts as I hope. Another long-term advantage is that it will also be much easier on the family when I go away for conferences or research (we have a 4-year old). Even short trips were difficult for my husband to manage both child-care (e.g. dropping and picking up from daycare) and a long commute. Although I did a number of work-related trips from Uppsala, there have been opportunities that I haven’t taken because it was too much to ask of my very supportive husband. All in all, it will probably take a while for us to fully realize the advantages of the move but I am hoping that it will make me more effective and freer to participate in the things that interest me.

The disadvantages are of course the commute itself. I’d rather bike or walk to work than ride the bus. It will also mean that staying late or popping by my office or lab is more difficult. On the small scale—a long daily commute can have some of the advantages/disadvantages of long distance fieldwork: when I’ve left for the day, I can’t go back. The change won’t be so dramatic for me because I was already limited in doing ‘off-hour’ work by my child (and commuting husband). And unlike data collecting, I can always bring a lot of my work home with me. But I’m not sure how my shortened campus time will function once the semester is in full swing. I’ll get a better feeling as the fall progresses if there are any hidden costs to regularly leaving early. Commuting also changes the timing of the kind of work I can do during the day because now there are many things that I won’t be able to do in the commuting hours such as meetings, lab work, extensive literature searches, adding web content to lectures, etc. I think it will take more time to know if this is a plus or minus for me.

Unlike your field sites (or lack thereof), choosing where you live seems more a personal choice and might be limited by many factors (e.g. city university vs. college town). But as I have experienced from moves in the past, where you live can also effect how you work. Gone are the days when I could do that one last thing before rushing off to pick my daughter up from daycare. I don’t know if this will make me more disciplined with my time in the afternoon or more likely to give up a task earlier so I don’t have to stop in the middle of it.

Overall, I think I will be very motivated to keep my commute productive because the alternative is really long workdays. With a young child, I think it is especially important that we have some time together each day. If I spend 8 or so hours in my office, I’ll rarely see her.

I’ve known lots of different styles of professors: those that are always in their office, those that leave early to pick up kids, or come in late because they work better at night. One of my colleagues even commutes on a weekly basis from Norway. Ultimately, academia is a job where your accomplishments often matter more than the time you do it and we’re generally given a lot of freedom to arrange how we work.

I’m curious about other people’s decisions. How do you schedule your day and what do you find to be most effective way to do your work? Does where you live effect how you work? Do you use your commute to work/think about work or is it a time-out in the day? Above all, any advice from long-distance commuters would be greatly appreciated!

Model systems don’t work at teaching universities

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Many research strategies, developed inside large research institutions, don’t work well in small teaching-centered institutions.

One of these strategies, I suggest, is the use of a biological model system.

What do I mean by model systems? Any system, used in multiple laboratories, that has been optimized for a broad variety of lab investigations. I’m thinking of zebrafish, Drosophila, mice, C. elegans, E. coli, Arabidopsis, and honey bees. For the ant people, this includes Temnothorax.

It might seem counterintuitive that model systems can prevent you from getting research done in a teaching-centered institution. After all, these model systems were developed, in part, to make research easier and remove methodological barriers experienced by those working in a broad variety of organisms.

Even though model organisms are easy to work with, have lots of standardized methods, and a massive bank of potential collaborators, the costs and risks of working with a model system far outweigh the benefits and the opportunities.

My initial thought on this topic came from observation. I don’t see much research on model systems getting done within teaching institutions. I also have seen a variety of people, who work on model systems in teaching institutions, experience substantial challenges that have kept them from getting work done. Here’s a list of problems that I’ve seen crop up for those using model systems in teaching institutions.

  • The low-hanging fruit has been picked. In model systems, the things which are a combination of easy, obvious and trivial have already been done. That only leaves things which are either difficult, requiring particularly deep insight, or trivial.
  • Model systems have intense competition. In model systems, many labs compete rather than collaborate. A small lab in a teaching institution isn’t built to deal with this kind of competition. This doesn’t mean that you should avoid competition by working on minutia, but it also means that you should choose an avenue that has high pressure competition. As any ecologist will tell you, competition is an interaction which has a negative effect on both parties. (An economist will tell you the same thing, though it’s better for the consumer).
  • You might get scooped. The probability that someone is doing exactly what you are working on is higher in a model system.
  • Collaborating is difficult. The labs that run model systems typically have lots of routine methods happening like clockwork, run by technicians and students who have become specialized in the model system. If you’re working on this model system, you are not likely to be able to contribute anything of substance to a big lab, which could do the same thing much more quickly than you could offer. Collaboration is easier if you have a specific resource or skills that others want or need, and that’s harder from a teaching institution if you work in a model system.
  • You may become isolated. In the community of people working in model systems, if you have a small undergraduate lab you’re more likely to be overlooked by your peers. I’m not sure how or why this is the case, but it’s what I’ve observed on several occasions. Everyone I know who has worked on a model system wasn’t well integrated into the community of frenemies that works on the system. It’s hard to have a scientific impact if you are unrecognized by your peers.
  • The bar for evidence is very high. Peer review is more likely to be rough and demanding. People with model systems tend to be territorial, and especially if you’re not perceived as a competitor that can bite back with teeth, then at least one reviewer is likely to go the extra mile to find something wrong with what you’ve done.
  • The minimum publishable unit is greater. If you’re working in a model system, odds are that the big labs working in the field are able to heavily replicate their experiments, and sandwich multiple experiments together into a paper. This establishes a standard for a large volume of data in a paper. I’m not arguing that you should seek to publish as soon as you have enough data for any kind of publication. However, if you have a genuine, interesting, and cool finding and want to get it out for everybody to read, it’s a bummer to have others telling you that you that it’s ready for publication because you don’t have a mountain of data.
  • Maintaining colonies takes continuous effort. Mustering any kind of consistent student work in the lab is a success. You don’t want your success in long-term student research to be squandered on the maintenance of lab colonies of a model system. You want to work on a system in which students can dive in and collect actual data, rather than spending all of their time keeping critters fed, clean, reproductive and healthy. Also, you want to be free to disappear for long periods of time, and not chained to the lab to maintain your organisms.

So why would one ever work with a model organism? One great reason would be to provide lots of valuable hands-on laboratory experiences to students. But in my observation, this kind of approach is a poor recipe for completing original research in a teaching institution.

In my opinion, if we are giving students research training, that means we’re giving them research training. Which means that they’re doing research, and research is something that ends up in new findings that are shared with the scientific community. If you want to give a genuine research experience to students, then you need to regularly publish the work that you are doing. From my vantage point, it’s very difficult to do this while working with a model system in a teaching institution.

I have a couple other alternative explanations for the (perceived) phenomenon that researchers working on model systems in teaching institution have a harder time publishing their work. The first alternative is that I’m just wrong, and that my experiences or perceptions aren’t representative or accurate.

The second alternative is that less research is completed because of the use of strategies from R1 labs that don’t translate to a teaching institution. Most people at teaching institutions, who work on model systems, are continuing with their dissertation system, I think. The research approaches from a dissertation lab probably don’t translate well to a teaching institution. I haven’t spent much time inside the labs of teaching institution professors using model systems, so I don’t know how they run things on a day-to-day basis, and am not too sure about this.

Do you think that it’s hard to get research done on a model system at a teaching institution? Did you switch into one, or out of one, to get research done? Thinking about it?