On the shrinkage of polar ice caps

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When I was a senior in college, I was in a seminar dedicated to a new book, written by a US senator who had just been elected Vice President. The book was Earth in the Balance. It explained the science of carbon pollution, the greenhouse effect, and global climate change. To me, it was a revelation. I was aware of the greenhouse effect, but I didn’t appreciate the magnitude of the problem and the massive global effort it would require, until Gore explained it. Continue reading

I think I might be a successful nag

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Has Small Pond Science helped increase broader awareness and respect for university scientists and students working outside the R1 environment?

I think, well, maybe, a little bit. Enough to keep me from closing shop. There are a lot of known unknowns, but I’ll focus on some known knowns. 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

Academia and friendships

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At one point I thought about writing a post about the difficulties that academia wreaks on friendships. All that moving about means picking up, making new friends and leaving behind the old. It is tough in many respects and it is easy to see the negatives of that part of the career. Check out #academicnomad for the joys and sorrows of traveling/moving so much. Needless to say the post slipped by and I never quite got around to writing it. 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

Where do you eat lunch? And does it matter?

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Lunch culture seems to vary a lot from place to place.

I will admit to sometimes eating lunch at my desk, even though it is seems a highly unusual thing at European universities. But these days it is rare for me to do that, partly because most people aren’t and partly because it is just nicer to take a moment and eat properly. Continue reading

Respectful conversation at academic conferences

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You’re probably familiar with this scene from academic conferences:

Person A and and Person B have been chatting for a few minutes. Person C strolls by and makes eye contact with Person A. Person C gives a big smile to Person A, which is reciprocated, perhaps with a hug. Both A and C enthusiastically ask one another about their lab mates, families, and life in general.

At this moment, Person B is feeling awkward.

Continue reading

Differences between the sciences and the humanities

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One of the great things about being on a small campus is that I have lots of opportunities to interact with colleagues in different departments and colleges. One positive side effect of being sucked into university-level obligations is that you get to know people you otherwise wouldn’t interact with.

  • Over the years, I’ve observed some huge differences differences between the research cultures of the sciences and the humanities. Most of these things are obvious, I realize.  Understanding these differences can help bridge cultural gaps.
  • In the sciences, journal articles are the primary metric of productivity and success. In the humanities, it’s books. Scientists can write books, and humanities people can write journal articles, but they’re not as important.
  • In many humanities fields, giving a paper at a conference involves actually giving a paper. Standing at a podium and reading, page after page after page. Science talks are far more informal.
  • Research in the sciences is highly collaborative. Many humanities scholars work solitarily.
  • Student mentorship happens everywhere. In the sciences, students often adopt a piece of a larger lab project, whereas in the humanities more often students work on entirely separate questions from their mentors. On average, science professors take on a greater number of student researchers than in the humanities.
  • Scientists are often expected to fund their research programs with external grants. Humanities researchers aren’t necessarily expected to bring in outside funds in order to be perceived as successful, as long as they create the research products in the end.
  • What constitutes a huge grant in the humanities is a small grant in the sciences. An award of $50,000 from the NEH or NEA is a massive success and a windfall, whereas in the sciences this is useful money but not even close to a “big.”
  • Scientists can get big pools of money to start up their labs. In the humanities, you get moving expenses, a computer, maybe some reassigned time and maybe a little bit more.
  • In the humanities, receiving a PhD from a “top 10 program” in the field is critical for professional success. Program prestige matters in the sciences, but not as much. (I couldn’t even tell you what the rankings are in ecology/evolution.)
  • The academic job market is way more messed up in the humanities. Here are two contributing factors: First, the degree of adjunctification is higher outside the sciences because tenure-line science faculty are more likely to bring in overhead to cover salary costs. Second, the job market for research scientists is more robust than for academic (say) historians. In the humanities, it’s more challenging to parlay a PhD into a salaried academic position outside a university.
  • All worthwhile doctoral programs in the sciences fund the students, so tuition and living expenses aren’t covered by loans. Graduate students in the sciences are paid to teach and do research, albeit poorly. In the humanities, PhD recipients often emerge with substantial debt.
  • Scientists need good library access to get current articles. However, physical access to great libraries is far more important in the humanities, as original papers and actual books remains important for research. The physical location of an institution, relative to an impressive library, is important for the humanities scholar.
  • Humanities scholars use the phrase “digital humanities,” and it means something to them.
  • Science professors are less likely to use elbow patches on their tweed jackets, but professors in the humanities are more likely to smoke a pipe.

Feel free to make new contributions, or disabuse me of any mistaken notions, in the comments.

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.

Putting faces to names: meeting fellow academics

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I just got back from a tour of North America, including a stop to visit my family in Nova Scotia and a conference in California. It was a great trip and a reminder of how lucky I am these days. Not only did my daughter and I get spoiled by my parents but I also had the opportunity to meet and interact with many of the leaders and new up and coming researchers of my field*.  As we recover from jet lag and get back to the routine, I have a chance to reflect on my travels.

One of the benefits of traveling for conferences is, of course, the chance to meet people. Seeing talks on the forefront of everyone’s research is definitely good for learning and stimulating new ideas, but I often find the most valuable parts of any conference are the causal conversations you end up having. It can also be pretty interesting to put faces (and characters) to the names you know from the literature.

Although not unique to academia, you often ‘know’ people before meeting them through their work. I find that I don’t often have a particular preconceived picture of authors I read, but meeting someone in person or seeing them talk does change the way I interact with the literature to some extent. For one thing, the more people I meet, the more human the literature feels. I can put faces to author names and pictures to their study systems (if I’ve seen a talk). As a student, in some ways the primary literature felt so, well, scientific and perhaps a bit cold. These days, that is less of an issue and science feels much more like an endeavour that I belong to. However, as you become more apart of the community doing science, there is the potential for things to swing the other way. I’m probably more likely to notice a publication on a list if I’ve met the author. It is always nice to see people I went to grad school with pop up in journal alerts, for example. And although I try not to be biased by my impressions of a person when I read a paper, I’m only human after all. I wouldn’t say it stops me from appreciating good work (I hope!) but personal interactions do colour whether I would want to invite a person for a talk, for example. And interactions at conferences, etc. definitely influences who I want to work with. Of course, I’m more likely to collaborate with people I hit it off with then those I don’t. I wonder if that is also true for citations and the like. Are we more likely to read and cite people we’ve met? How about those we like? I’m not sure I want to know the answers to those questions and I certainly try not to let biases like that enter my work, but science is a human activity after all.

I think it is always interesting to meet/see people in person who you know from other means. In academics, that used to be meeting or seeing someone give a talk at a conference whose papers you’ve read. Maybe their papers are seminal to yours, and especially as a grad student, seeing people behind the work can be very eye opening. I once was at a famous ecologist’s talk at a big conference. The room was packed but it was one of the poorer talks I’d ever seen. The slides were directly transferred from papers and impossible to read. Pointing from the lectern to a screen meters away also did not help (‘as you can clearly see…’ was a memorable quote). A friend and I sat at the back trying to figure out the main tenets of the classic theory from this person because it was the keystone of the talk but never directly described (we were of course all expected to be familiar with it, I suppose). The experience taught me that great thinkers don’t necessarily make great presenters. But I’ve also seen wonderful talks by some big names too.

Over the last few weeks, I’ve gotten to see old friends and put faces to more names I’m familiar with. I also got a chance to hear from and meet people I might have never have known otherwise. And seeing what the grad students are up to is always interesting. Communicating science and hearing about people’s studies is part of what I find fun in this job.

Interestingly, this blog and twitter has also opened up my scientific community beyond the boarders of my research. So whereas before putting faces to names was all about meeting people I had read in the literature, this time it included a chance to meet up with Small Pond’s very only leader, Terry. We were lucky to overlap in the LA area for a day and were able to see each other face to face. I have to admit, it felt a bit like an academic version of on-line dating or something. I was nervous to meet. What if it was awkward? What if we didn’t like each other? I’d been having fun posting on this blog but if our in person interaction didn’t work I wasn’t sure what that would mean. I’m happy to report that we had a good time and a fruitful discussion about blogging, twitter and this new-to-me on-line community. I hope it is only the first of many meetings with those that I am getting to know through their blogs and tweets. I’m sure it will mean that I will also pop in on talks far removed from my research if we happen to be at the same conference in the future. I think that is a good thing.

*being a bit of a generalist, the conference was in one of my fields of interest, plant volatiles.

The conflict-cooperation model of faculty-admin relations, Part 3: How our universities run like social insect colonies

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With the understanding that we are social animals and that principles of behavioral ecology for social groups can apply to us*, let’s look at six relevant concepts from ant societies.

1. Workers are in charge of ant colonies; faculty are in charge of universities. The stereotypical, and false, model of ant colonies is that they’re run by the queen. In fact, workers are the ones that are collectively running the show. The queen is the factory that produces eggs, but the workers actually benefit more from the reproduction of the queen than the queen herself (in terms of raw genetic relatedness). A queen is as much a slave of her own offspring than she is the leader of a band of her daughters. I’ll spare you the social insect lesson in detail, but the upshot is that most colony-level decisions are made collectively among the workers and the queen has little to no say in the matter. The queen is just along for the ride, and her life can be truly at risk if she doesn’t lay the right kind of eggs (by using the wrong sperm, or choosing to not use sperm at all). In universities, professors run the show, even when there is little true faculty governance. Even with a heavy-handed administration, we faculty control what happens. The best that admins can do is provide, or remove, incentives for particular activities. Regardless, faculty will do as they please. The good administrators recognize this fact and work within its bounds.

2. Limited resources affect how ant colonies compete with one another; limited resources predict how universities compete with one another. From the perspective of admins, universities are competing with one another for status and funding. Colonies under extreme resource limitation allocate their resources very differently than those that are not those limitations. Unpredictability of resources also affect allocation decisions. The way in which colonies compete with one another is structured by the ways in which resources are limiting.

3. Workers and queens have different interests in how the ant colony invests resources; admins and faculty have different interests in investing resources. It’s a longer story, but the upshot is that workers want different things than the queen. That’s a textbook conflict of interest, though slightly overgeneralized. (Find your local social insect biologist for a longer lesson.)

To make this messier, the workers themselves may not even be closely related to one another, because queens often mate with multiple males and colonies can have multiple queens. Many social insect colonies have behavioral bedlam at their core, with torn allegiances, nepotism, assassinations, and workers policing one another to make sure that they don’t cheat. The harmonious work-together-for-a-common-cause is a thin veneer that disappears once you start watching carefully.

In a university, faculty often have interest interests or agendas for resource allocation, so they can’t all agree. If the faculty can’t organize in a common agenda, then the administrative agenda is often the one that wins. When faculty with conflicting agendas can agree on shared priorities and can communicate these, they have a chance at winning in a conflict over resource allocation, if unified. When faculty are divided, then the ones who win are those whose priorities are consonant with the administration.

4. In ant colonies, the queen controls the productivity of the colony, but the workers have ability to shape that productivity; In universities, admins distribute funds but faculty members are the ones that make those funds go to work. Queens can control the ratio of male eggs and female eggs that she lays. The workers then can choose to help those eggs grow, or eat them. Likewise, administrators can spend all kinds of money on useless initiatives, but they will go to waste if they’re not useful to faculty.

5. While there is conflict in ant colonies and in universities, there is plenty of cooperation. By banding together in a colony, the fitness of any single individual is much greater than it would be if they were on their own. Colonies that don’t effectively work together have lower fitness, and then everybody would be worse off. Wise administrators will recognize that providing faculty with the resources that individuals need to be successful will contribute to higher levels of productivity at the level of the organization. Wise faculty members will recognize that flexibility in using the resources available from administrators, even if not efficiently allocated, is better than intransigence.

6. Developmental constraints have resulted in the exploitation of workers. Natural selection has favored the evolution of cooperation in ant colonies, however in “highly eusocial” groups that have worked cooperatively for a jazillion generations, there are likely to exist developmental canalizations and constraints that may result in workers that have no choice but to cooperate in a way that isn’t working in their best interests. If your mom creates you without ovaries, then well, you better help her reproduce, because otherwise you have no affect on your fitness whatsoever. (Note that this is not a fact that social insect researchers consider as often as they should.)

Likewise, universities have developed a system that exploits their workers that have little to no power to address inequitable distribution of resources. The conversion of teaching faculty into a caste of contingent employees without a voice in institutional governance has resulted in an excess of power in the administration that does not necessarily work in the best interest in the members of the community.

Next week: The consequences of our sociality.

*If you harbor some old-school critique of sociobiology, please take it elsewhere.

What it’s like to start a job as a Visiting Assistant Professor (guest post)

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Carrie Woods in the field

Carrie Woods in the field

This is a guest post by Carrie Woods of Colgate University, a Canadian scientist who studies the ecophysiology and community ecology of tropical rainforest canopies. If you have any questions or remarks for Carrie, please be sure to leave them in the comments.

I just entered my new office as a Visiting Assistant Professor (VAP) in the Biology Department at Colgate University, a liberal arts college in upstate New York. My office is one of the nicest I have seen and has an incredible view of hills covered in lush temperate forest – perfect for pondering life, science, or wherever the mind cares to wander. Facing this pondering window, I received and accepted an invitation from Terry to post about my experience thus far in interviewing and becoming a faculty member at a liberal arts institution.

My interview was full of friendly faces offering help, advice, and typical interview questions for a teaching position in a liberal arts college. First and foremost, everyone wanted to know what course I was proposing to teach. Is it novel in the department? Will it broaden the scope of understanding of the students? Do I have the expertise to effectively teach the course? If you have done your research, you will know the answer to all of these questions and will instill confidence in every interviewer that yes, in fact, you have perused the course offerings and believe that the course you are proposing to teach is novel, interesting, great for broadening students’ perspectives, and couldn’t be taught without you. I know this may seem obvious but I was expecting an interview that looked at my entire academic career – not just my teaching experience. I even had prepared answers to questions pertaining to my research. But I guess because a VAP position is used to bring in a professor for a year to teach, and only teach, the lack of focus on my research makes sense.

I was surprised though how little emphasis was placed on my research interests or how many publications I had or in what journals. Having only had experience in Research I universities, this came as a shock. I knew that the primary focus of a professor in a liberal arts college is the education of undergraduate students but I didn’t realize how little importance was placed on your research when deciding if you were right for a teaching position. Not one single person asked me about my research. They did, however, peruse my CV for mentoring and teaching experiences (which I placed ahead of my research experience, as per the suggestion of every liberal arts college faculty I knew). I could be misunderstanding the entire process though. It could be that my research experiences were sufficient and, therefore, not in need of discussion. My seminar was clear and focused on undergraduates but I did not dumb down my research or complicated multivariate analyses. I took those challenges head on to show that I could effectively teach complicated concepts. So maybe, my research was important to show that I was a well-rounded scientist.

I decided to take a teaching position after completing my Ph.D. for several reasons. First, many of my colleagues that had new assistant professor positions, regardless of what type of institution, seemed to be drowning in course development. I had never developed or taught an entire course before so I heeded their calls of distress and decided to find a position where I could develop and teach a course before applying for a post-doc or tenure-track position. A VAP position is exactly that. Second, science is a discipline wrought with waiting. There is little immediate gratification in scientific research – except of course for those moments during data analysis when your hypothesis is accepted or when finally figuring out the story of your paper or when a paper is accepted for publication. But other than those brief moments, science is pretty thankless and requires resilience to pursue an idea from birth to publication. In between these brief gratifying moments in graduate school, I found teaching to be extremely rewarding. Watching someone learn a concept that you taught is a very gratifying experience. These rewarding teaching moments carried me through those times when motivation for my research was waning. I found a love and passion for teaching during graduate school and wanted to pursue those passions a little deeper (hence the VAP position).

Since arriving around 9 am this morning, four different people have come by to welcome me and offer any help they can. I feel grateful for their friendly faces and help in navigating the new avenues of a faculty position at a liberal arts college as I am just starting to get my toes wet. I am super excited about finally teaching my own course. So excited in fact that I have already outlined my lectures, ordered my textbook for the bookstore, written my syllabus, and have some ideas for exam questions and the first day of classes isn’t for two weeks. I would have started sooner if it wasn’t for my busy summer of field research in Costa Rica, two conferences, my dissertation defense, and graduation. Now that those tasks are complete, I can finally focus on what I have been excited to do since I first discovered a passion for teaching in graduate school. It’s a very cool moment.

As for the future, this next year will likely dictate where I end up ultimately in academia: liberal arts or research I. I honestly haven’t fully decided where I want to go yet. However, looking out my window at forest-covered hills and being in a department with such an amazing group of friendly and supportive people, thus far, liberal arts is winning the race.

Stop using “silverback” to describe scientists. It’s sexist.

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Some people have taken to referring to influential senior scientists as “silverbacks.”

This practice should stop.

I’m writing this at a scientific conference, and I’ve heard it several times in independent contexts, in a non-ironic use. I also heard this term at the last three conferences I’ve attended in the last few years by both European and North American researchers. I’ve learned that at least one university has a lecture series called the “silverback” sessions.

I don’t know the specific origin of the term, or when it emerged as a regular term of use. Regardless, I don’t like it.

For those unaware of the behavioral ecology of non-human apes, a “silverback” is an older, large-bodied and behaviorally dominant male gorilla, characterized by silver fur on its back. With respect to at least certain aspects of a social group of gorillas, silverbacks are in charge. Their dominant status emerges from their age, size, interactions with others, experience and ability to advance in the social network of their group.

What’s the problem with the use of the term “silverback?” I don’t have a problem with the practice of using analogies from the behavior of other species to be applied to our own, as long as they’re appropriate.

Labeling influential senior scientists as “silverbacks” is a bad idea for one big reason:

It is sexist.

Female gorillas are never silverbacks. By using the term “silverback” to as a synonym for “influential senior scientist,” one is implying that women are not, or cannot become, influential senior scientists.

This objection isn’t about political correctness. It’s about recognizing the influential women in our midst, and showing respect for them. (Most people with issues about so-called political correctness are those who have trouble showing respect for others.)

What can we do? Join me in calling out the sexism of this word when it gets used. If you hear it in conversation, perhaps you could mention that you don’t think it’s a good use of the term because it excludes women?

(Most of my contemporary scientific heroes are women, after all.)

I’m a less-influential not-that-senior scientist, so I’m not in the best position to bring this issue to the forefront. Maybe we can bring this up with some big-time men and women to take up this issue?

Have you heard this term or is it a new one to you? Any additional observations, ideas, or suggestions?

On attending graduation

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Do you attend graduation?

On every campus, the formal expectation is that all faculty attend graduation. Nonetheless, not everyone goes. On some campuses, such as mine, only a small fraction of faculty go. We have so few faculty attend that it’s downright embarrassing to stand in such a small group of faculty among the massive throng of reveling graduates.

Who doesn’t attend graduation? For starters, those who aren’t available don’t go.

Who else doesn’t go? I guess it’s those faculty who wouldn’t enjoy it. There’s a lot to not enjoy. It could be really hot, it has some major tedium with all of those names, and it sucks up a good part of your weekend.

What is there to enjoy about graduation? It’s a celebration and you get to be adjacent to the center of it. The pomp can be fun. You get to meet the joyful families of your students, and you can express your pride in your students’ work and accomplishments. At my university, this is a huge deal, because in a goodly-sized fraction of these families the students are the first ones to graduate from college, and for many, graduation is an endpoint of perseverance through both economic and personal challenges on a scale with which I’m not familiar. The successes of these students is genuinely heroic to me. If I was dealt the cards that they were, I don’t know if I would have been as successful. So I attend with great pride.

I also go because it matters to my chair, and keeping him happy is important to keeping me happy. And he’s a great guy, and graduation is not a hardship by any means.

We spend a good piece of our careers working with our students, and while graduation better not be the end of their education it does mark a major milestone. If you look out at the students, it’s a condensed mixture of pleasure, pride, satisfaction and trepidation. This kind of drama is something to savor.

And, you never know, the commencement address might not be the same trite stuff. (I missed one graduation a few years ago which was an absolute train wreck for which apparently no explanation can do justice, and I’m sad I missed it.) I imagine the faculty who attended the ’05 Kenyon College ceremony are glad they went.

If you take pleasure in your students, then go on, go to graduation. You don’t have to go. But if you only did the things you had to, you wouldn’t be happy for doing your job well.

People not understanding your job

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It’s pretty obvious that non-academics, even those that are well familiar with the college experience, don’t have an idea what our jobs are about or what our responsibilities are. At social occasions outside academia, I’ve found it’s best to not mention that I’m a professor, because it triggers a set of false assumptions that misdirect the conversation.

These misunderstandings about your job are only the tip of the iceberg, if you work for a teaching institution. Misconceptions about the professoriate abound, without adding into the mix that typical preconceptions might, to some extent, fit a number of your colleagues. My campus actually has a huge fraction of professors that are only there two days per week, and are not working when they’re not there. This is not the case for the scientists, though.

Even people at your own work misunderstand your job. It’s reasonable that administrative staff and students might not get it, but most other faculty – including some science faculty – don’t understand that that scientific research is part of the job.

Last year, I had just returned from an extended trip conducting field research with ten students. It was a challenging and rewarding excursion. Upon my return, my dean at the time — once a science researcher herself — asked me without any hint of irony, “How was vacation?”

On my campus, almost no classes are taught on Fridays. (This evolved in the budget crisis before last.) Every non-scientist that I bump into on campus asks, “what are you doing here? It’s Friday!?” Even worse, every other science faculty member in other departments asks the same question, begrudging that they have to be on campus for some service commitment. It is mostly inconceivable that I am on campus because this is where my lab is located, and that I’m doing work.

I live halfway between Caltech and the nearby NASA-Jet Propulsion Laboratory. There are misconceptions tied to living near a NASA facility. Folks around here hear “scientist” and they think “rocket scientist.”  Wait, you’re not at Caltech, you teach at a state university?  Oh, I thought you were a research scientist. Nobody says this, of course. I don’t perceive an insult. This behavior is curious, though, and can lead to misunderstandings.

At my kid’s elementary school, we are blessed with a corps of volunteers that work with the kids on a variety of science, math and engineering projects. Admittedly, I don’t do this regularly or as often as I should (and I feel guilty about it, even if the time isn’t to be found on my calendar). There is a weird distinction among the community volunteers. The rocket scientists are “Dr. Smith” and “Dr. Jones” and the non-rocket scientists are “Mr. Cooper” and “Mr. White.” The only reason this does bug me is that the kids are getting a misconception that being an astronomer or rocket scientist gives you a fancy title, but geologists and ecologists don’t. The parents who are Dr. X and Dr. Z don’t ask to be called Dr., just as I don’t ask to be. They just have naturally attracted the label.  More often than not, kids beckon me as the dad of my kid, rather than by my own name. I like that best, actually.

I don’t know if this has happened because they don’t see the field of ecology as Dr.-worthy, or if it’s because I’m not at a high-powered research institution, or my attire and bearing doesn’t gel with the preconception of a scientific researcher. (I am a field biologist, after all. Cargo pants are sometimes involved.) It’s obvious that everyone’s more impressed by the rocket scientists and think they have more to offer. This is exactly why I feel guilty about not volunteering enough, because I’m tacitly allowing science to be seen as a narrowly focused enterprise.

When I lived near Ft. Detrick, where the U.S. army developed its biological weapons, people would assume that as a scientist was doing research for classified military purposes. That was an entirely different sort of misconception.

As long as the people who misunderstand you don’t have an effect on your job, I don’t think it matters. I’m sure I have no understanding about what an investment banker does all day, and if I tried to guess, I’d probably end up being annoying. It is a major problem if my own Dean doesn’t know that when I travel somewhere on university business, it is actually work, and not vacation. That’s a difficult one to fix, and usually I think the only way to do it is to let the work speak for itself. So, we’ll just keep our heads down and do our jobs.

Nobody with whom to share cool discoveries

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Research institutions build core strengths in particular research areas. However, teaching schools hire faculty to teach their specialty, so that as many subdisciplines as possible can be represented. This means that there is typically one person in each field. There are exceptions, such as a department that houses multiple herpetologists who study different aspects of herps.

The hardest part about being the only expert in social insects at my job, is how lonely it gets when something exciting happens. Without a doubt, most exciting, heart-pounding this-is-awesome experiences happen in front of the computer when I’m analyzing data. (Aside from stumbling on caecilians and big cats in the field.) When I find out something entirely new that changes, just a little bit, how we think the world works. Sometimes it’s a steady realization, but sometimes – BAM – the pattern emerges immediately. Like the one in the figure. This is a genuinely new find, which has a generalized application, and right away I was thrilled. But there was nobody there to revel with me.

interestingfind

A cool pattern.

My undergrad lab members don’t quite get how cool these moments are. My excellent department mates would be happy but it’s not their field. My spouse is as smart as a person gets, but she’s not a biologist and explaining it in a couple minutes takes away the fun. There are tens of thousands of people in the world who would understand exactly how this is cool, but none of them are next door.

There’s one big upside to being isolated when making cool discoveries. The desire to share it can burn for a while, to get me through writing, revision, submission, resubmission, resubmission, revision and publication. If I remember how cool it was to first learn about it, I hang on to that through the more tedious stages.

This is what I miss about grad school and postdoc-ing: a space full of labmates for sharing these moments. Perhaps this is why I particularly enjoy conferences, being invited for talks, and overlapping with others at my field site, because this is when I’m with my kindred.