A case of scientific dishonesty has hit close to home and got me thinking. This isn’t a post of the details of the case (you can read more here if you’re interested) or the players involved (I don’t know them more than to say hi in the hallway) or to comment this particular case since I don’t have any more information than what is publically available. So if you’re looking for insider gossip, the following is bound to disappoint. Instead this example has got me reflecting in general about scientific dishonesty and what I can do about it.
A lot of federal agencies want to enhance the research environment at primarily undergraduate institutions and minority-serving institutions. Not all efforts hit the mark.
Consider the summer faculty research internships that a variety of agencies run.
It’s not the time, it’s the people.
The popular conception is that scientists at teaching-focused institutions have lower research productivity primarily because they spend so much time teaching. I disagree.
If you look at scientists in teaching-focused institutions who have robust research programs, there’s one thing they tend to have in common: They have active collaborations with researchers outside their own institution.
The most recent paper from my lab is a fun one. We show that thieving ants have a suite of sneaky behaviors, to help them avoid being caught in the possession of stolen goods. These differences are dramatic enough to classify thieves as a distinct and new caste of ant.
Guest post by Rosie Burdon, a PhD student at Uppsala University in Amy Parachnowitsch’s lab. She is studying interactions between Penstemon digitalis and its pollinator Bombus impatiens in eastern USA. Here she shares her experiences of spanning multiple countries for a PhD and the benefits and challenges of having the USA as your long distance fieldsite. You can find her on Twitter at @RealRBurdon.
I love my job, it’s a 4-year contract asking questions about nature and ultimately answering some. Yes, it is a real job mum. Specifically, I get paid to ask questions about what plant volatiles and nectar rewards mean to bees/plant reproduction. I don’t do this in the country that employs me, or even the country I was born in. I moved from the UK to Sweden to work (where I spend most of my time) but I do my fieldwork in the US or else dwell in university of Salzburg labs.
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.
A couple of recent conversations have got me thinking about the culture of academia and grad school training.
The first conversation relates more to the general culture of academia. The complaint was that these days people are very selfish; they don’t want to participate in departmental events or even come into their office unless there is a very personal benefit they can see. The research groups are little islands and everything is about me, me, me. Young professors and graduate students aren’t thinking about how that can and should contribute to the academic community but rather always focused on what they need to do for themselves and/or their group. Now we can debate about whether or not this is really the state of academia or even if it is true for the particular department that was being complained about but it is an interesting thing to think about. In these days of extreme competition, for grants, positions, paper publications, and on and on, are we becoming too focused on ourselves? Is it really all about me?
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.
There was a piece published on the Science Magazine website, by Eli Kintisch, that smelled fishy to me. The article was an overview of a range of efforts to make the sharing of raw scientific data easier and more common.
Now is the time of year when we work with students on designing summer research projects. How do you decide exactly what their project is, and how the experimental design is structured? This is something I struggle with.
In theory, quality mentorship (involving time, patience and skill) can lead a student towards working very independently and still have a successful project. Oftentimes, though, the time constraints involved in a summer project don’t allow for a comprehensive mentoring scheme that facilitates a high level of student independence. Should the goal of a student research project be training of an independently-thinking scientist or the production of publishable research? I think you can have both, but when push comes to shove, which way do you have to lean? I’ve written about this already. (Shorter: without the pubs, my lab would run out of dough and then no students would have any experiences. As is said, your mileage may vary.)
A well-designed project will require a familiarity with prior literature, experimental design, relevant statistical approaches and the ability to anticipate the objections that reviewers will have once the final product goes out for review. Undergraduates are typically lacking in most, if not all, of these traits. Sometimes you just gotta tell the student what will work and what will not, and what is important to the scientific community and what is not. And sometimes you can’t send the student home to read fifteen papers before reconsidering a certain technique or hypothesis.
When students in the lab are particularly excited about a project beyond my mentorable expertise, or beyond the realm of publishability, I don’t hesitate to advise a new course. I let them know what I hope students get out a summer research experience:
- a diverse social network of biologists from many subfields and universities
experience designing and running an experiment
All three of those things take different kinds of effort, but all three are within reach, and I make decisions with an effort to maximize these three things for the students. Which means that, what happens in my lab inhabits the right side of the continuum, sometimes on the edge of the ‘zone of no mentorship’ if I take on too many students.
You might notice one thing is missing from my list: conceive an experiment and develop the hypotheses being tested.
Students can do that in grad school if they want. Or in the lab of a different PI. I would rather have a students design experiments on hypotheses connected to my lab that I am confident can be converted into papers, rather than work on an experiments of the students’ own personal interest. (Most of my students become enamored of their experimental subnets pretty quickly, though.)
This approach is in the interest of myself to maintain a productive lab, but I also think that being handed a menu of hypotheses instead of a blank slate is also in the long-term interest of most students. I’m not keen on mentoring a gaggle of students who design their own projects when these projects are only for their edification, and not for sharing with the scientific community. That kind of thing is wonderful for the curriculum, but not for my research lab.
Other people have other approaches, and that is a Good Thing. We need many kinds of PIs, including those that give students so much latitude that they will have an opportunity to learn from failure. And also those that take on 1-2 students at a time and work with them very carefully. I like the idea of thinking about my approach to avoid falling into a default mode of mentorship. Does this scheme make sense, and if it does, where do you fit in and how have you made your choices? I would imagine the nature of your institution and the nature of your subfield — and how much funding is available — structures these choices.
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.
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:
Facilitate more flexible embargoes on archived data
Encourage communication between data generators and re-users
Disclose data re-use ethics
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.
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.
Collectively, ants are efficient, and you might even call them smart. But individual ants are so dumb that they don’t even know how to feed themselves, as we show in the latest paper to come out of my lab. You could say that these ants have a drinking problem.
If you’re given a protein smoothie, you drink it. But if you give bullet ants a protein drink, they chomp and pull at it. If they knew how to use a fork, they’d probably try that, too.
The bullet ant Paraponera clavata has a boring diet: workers mostly collect sugar water from the rainforest canopy, supplemented with chunky prey items, like other ants and pieces of caterpillars. When they eat carbs, it’s in the form of a liquid which they gather in a droplet held by their mandibles. When they get protein, it’s in the form of a solid which they chomp and bite.
While attempting to do an experiment, we discovered that these ants are absolutely hopeless at drinking a liquid, if it’s a protein solution.
What does it look like when ants try to drink something and when they try to chomp at solid food? Here are two very short videos taken by Jenny Jandt:
We asked: what sensory cues do the ants use to decide whether to drink a fluid or to grasp at it as if it were a solid? We ran a field experiment with factorial combinations of various sugar (sucrose) concentrations and various protein (casein) concentrations, and used ethograms to measure behavioral responses. We replicated this across a bunch of colonies, randomized the order of presentation, and did other good stuff to make sure the experimental design wasn’t messed up. (We’re pros, you know.)
We mostly didn’t get stung while running the experiment. This matters because they are called “bullet ants” for good reason.
We found that the higher the concentration of sugar, the more likely the ants were to drink. If there was a little protein and no sugar at all, the ants would most likely grasp. Once protein concentrations got near 1 micromolar concentration, however, the concentration of sugar did not affect the grasping response to protein.
So, if these ants are thinking, then this is what they’re thinking to themselves: “If I taste protein, it must be food. So I’ll chomp at it, even though it’s a liquid.” But, it doesn’t look like they’re thinking much at all.
We found that the ants demonstrate a fixed action pattern of feeding behavior in response to assessing the nutritional content of food. This operationally works for them in nature, because texture and nutritional content are coupled. When we experimentally decoupled texture and nutritional content, then we were able to identify the cues that the ants used to make their food handling decision. They decide to drink when they detect carbohydrates and they decide to chomp when they detect protein, and texture has little to do with the decision.
How this project happened in a teaching-centered institution
In the first half of 2011, Hannah Larson (a Master’s student in my lab) was spending several months at La Selva Biological Station in Costa Rica, working with a microbial symbiont of bullet ants. She discovered the phenomenon of bullet ants chomping at protein solutions when she tried to experimentally feed colonies a protein solution, and the colonies opted to dismember the plastic pipets instead of drinking from them. She worked out other ways of delivering protein for her experiments, but we wanted to document and further understand this discovery.
That summer, I paired up my colleague Dr. Jenny Jandt up to mentor a student from my university on a totally different project. We all found this protein-chomping behavior so cool, and Jenny made the time for a second trip to Costa Rica after I helped her flesh the project out. My undergrad Peter Tellez was her wingman, and they did the experiment using the template of the many colonies that Hannah established for her thesis work. In late 2011, I drove out to visit Jenny in Tucson for a couple days, to work on this and another manuscript, in which the bulk of the paper was put together. Jenny put the finishing touches on this paper with just a bit of help from myself, Hannah and Peter. As it was a side project for all of us, it lingered a bit but Jenny persisted and she’s pretty much everything I could ask for in a collaborator and mentor to our students.
Where are they now? Jenny took a postdoc in the rockin’ lab of Amy Toth at Iowa State. Hannah is now in her second year of the DPT program at the Univ. of Washington and Peter is now a PhD student in the lab of Sunshine Van Bael at Tulane.)
In short, this cool paper came together because I was able to talk my postdoc buddy Jenny into coming down to the rainforest to work with my students for about a month. She is otherwise a wasp and bee behavior person, and I was glad to give her an avenue to work with ants and tropical rainforests, and my students greatly benefited from her careful mentorship and expertise in individual and collective behaviors of social insect colonies.
Reference: Jandt, J., H.K. Larson, P. Tellez, and T.P. McGlynn 2013. To drink or grasp? How bullet ants (Paraponera clavata) differentiate between sugars and proteins in liquids. Naturwissenschaften. DOI: 10.1007/s00114-013-1109-3
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:
- Manuscript completed
- Paper in progress
- 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.
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.
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.
I recently went over why seminar speakers might give a talk. Now, the flipside:
What is to be gained by inviting and hosting a seminar speaker?
There are institutional advantages to running a seminar series: to promote an intellectual atmosphere in a department, build a diversity of viewpoints, train students and keep everybody current. However, when an individual person or laboratory decides to host a particular guest speaker, there are other primary motives at work.
Here is a non-exclusive list of goals of hosts, that could explain why certain speakers are picked for a seminar series.
Schmoozing for a postdoc. I think this is the main reason that speakers are invited. Grad students want to be able to land a postdoc, and PIs want their students to land postdocs. Bringing in potential postdoc mentors to build relationships with graduate students is an old tradition.
Hang out with your intellectual hero. There’s something special about academically famous people in your field. The chance to visit just have a coffee with, say, Bert Hölldobler or Dan Janzen would be mighty darn cool. When I was in grad school, one person I invited was Ivette Perfecto. My main motivation was because because her science is just so darn awesome, and the chance to hang out with her was tremendous.
Quality time with a friend. Wouldn’t it good to see an old pal you haven’t seen for a while, and catch up on what work they’ve been doing?
Being an alpha. Hosts could invite junior speakers in their same field which are sure to be flattering of their more esteemed hosts whom they are visiting.
Be a beta. Hosts could invite senior researchers in their field, upon whose feet they may grovel. How is this different from hanging out with your hero? Betas are looking for status and opportunity, while it’s also possible to invite someone for less careerist motives.
Develop the career of another scientist. It could be that you just want to give an a good experience to a junior scientist who does good work, who could stand to benefit from giving an invited seminar.
Work with a collaborator. Some work is a lot easier, or more effective, when you’re in the same room, rather than using various methods of remote communication. Why not bring your collaborator out on the department’s dime?
Build a culture of inclusiveness. It’s no accident that most visiting speakers that I invite to my university’s lecture series are early career women, often with an international background or from underrepresented groups. This helps promote the careers of these scientists who are at a structural disadvantage because of biases in the system. An even stronger motivation, from my standpoint, is that these speakers are inspirational role models for our students, most of whom are minority women. I can talk about a commitment to diversity until my white face turns blue, but the fact of who I am speaks more than my words. Regular exposure to the experiences of senior doctoral students, postdocs, and junior faculty who have backgrounds not so different from my own students are critical. This isn’t the only factor involved in extending an invitation, but it’s a big one for myself and others at my institution.
Trade favors. Bringing a speaker out might be to make someone owe you a favor or a way to repay a favor. This could be to help out someone’s postdoc, or help out someone with a shaky tenure case who could use a bit of external validation. This might sound like a silly motive, but not without precedent. Once, when I was organizing a symposium, someone asked me for a speaking slot, and if I did this favor, this person said that I would be invited for the seminar series.
Show grad students a variety of career options. The flawed default mode in many universities is that moving onto an R1 faculty position is the natural and expected progression after grad school. However, the majority of Ph.D. recipients don’t go this route. Inviting people who work in industry, NGOs, and governmental agencies can help broaden perspectives. Also, of course, you could invite a researchers based out of a teaching institution. This will definitely widen the job horizons of grad students.
Entertainment value. Some people are invited because they’re known for giving a really great talk, will fill the house, and will bring not only reflected praise on the hosts but also a good time.
Learning science. Some people actually invite seminar speakers because they want to learn about the science that’s being done by the guest.
And that’s it for the list. Feel free to add the ones that I’m forgetting in the comments. Or to tell a funny story, for that matter. We could use more funny stories in the comments, right?
What is the purpose of an invited seminar?
Everybody wants to give a great seminar. But when the speaker gives the talk, what is the purpose or goal of the talk? What is the speaker trying to accomplish?
The purposes of dissertation defenses and job talks are obvious. However, whenever an invited speaker comes to give a seminar as a part of seminar series, the speaker could show up with many different kinds of priorities and purposes. We all have a variety of motivations that are context-dependent. Visiting speakers have overt and tacit messages that they have designed to be delivered in their slightly-less-than-an-hour timeslots.
Here is a classification of the non-exclusive goals that speakers might seek to accomplish in a seminar.
Build a reputation as an important scholar. Seminars can be used to help the speakers grow the perception that they, and their work, are important. To some extent the invitation to give the seminar itself is a validation, but the delivery of the talk is required to cement that validation and help people spread work.
Being an alpha. Some speakers know that they don’t need to build their reputation, but they can use the time allotted to them in a seminar to assert their dominant status. These talks might be used to stake out territory of interest to people working in the institution sponsoring the visit.
Be a beta. If the visiting speakers were invited by more prestigious research groups, then the speakers might choose to demonstrate behavioral submissiveness to the dominant hosts.
Bask in one’s legacy. Some speakers don’t want to say anything particularly new, but want to use the time to provide an overview of the major accomplishments that have been made over a successful career.
Promote students and postdocs. Seminar speakers are often invited to be the schmoozed, but they also can use seminars to promote the work of the members of their own labs. These kinds of talks heavily feature the roles of lab members in work presented in the talk.
Be entertaining and have fun. Some talks are designed to entertain the audience rather than inform. Moreover, the speaker could be giving the talk just for the fun of it.
Show off smarts. On some occasions, the speakers just want to show off how smart they are. This is likely to involve a number of obscure details that the audience wouldn’t want to bother understanding.
Not embarrass oneself. The imposter syndrome is well described in academia and speakers might not recognize that they are up to the task or are worthy of an invited talk. Other speakers might feel great about their science but are not sure that they can give a great talk. So, just getting through the talk without screwing up might be a goal of its own.
Build collaborations. When scholars visit one another’s institutions, the social context and resource access can facilitate collaborations more readily than what might occur at a professional conference. The seminar might be constructed to demonstrate opportunities where collaborations could be most fruitful.
Recruit students or postdocs. Faculty should always be on the lookout for motivated and talented future lab members. If there are potential recruits in the audience, the talk could serve not only as inspiration but also communicate clear possibilities for exciting student projects.
Give a lesson or advocate for an approach to how science is done. Oftentimes, seminars are most interesting not because of what was learned, but because the person presenting the work explained their rationale for choosing their experiments and provided arguments for the effectiveness of their approach to doing science. Speakers might choose to use their talk to give a lesson about more abstract ideas about the best ways to do science.
Argue for or against a pet theory, or shape the future of the field. Speakers might not be so heavily focused on their own findings, but instead use the seminar to advocate for or against a broader theory or direction for the field.
Pick an unnecessary argument. Some people are inherently antagonistic. They might think so strongly that the advance of knowledge emerges from arguments among academics, that they pick arguments and intentionally say controversial things to get the ball rolling on arguments.
Be cool. Some people need to show that what they are doing is cool. Obviously this purpose could overlap with other purposes, such as building a reputation or having fun. But sometimes, being cool is most important.
Inspire a new generation of scientists. Some speakers design their seminars specifically to be inspiration for the grad students in the audience. They might not be working hard to market their own ideas, or promote themselves, but to provide guidance for the junior scientists.
Actually give a science lesson. This might sound crazy, but some people design their talks so that they are giving a lesson about their own scientific research so that people can understand more about the world.
And that’s it for the list.
So, what are my priorities in giving a talk? I’m all for everybody having fun. If someone in the audience sees potential for collaboration, then that would be really cool. I make sure that my students get appropriate credit when due, and I highlight the fact that my lab is an undergrad-run operation. I also want the grad students there to see what I’m doing and realize that a job at a teaching institution is compatible with mighty awesome research. Of course, I really do want people to learn a bunch about the topic of the seminar, and more generally I like to make the case that we need to change how we do science. (For example, in my next batch of upcoming seminars, I argue that orthodox ideas often are nonsensical and not well supported, and my whole talk is built around one of those ideas.)
And, I’d be dishonest if I ignored the fact that giving a kickass talk makes one look good in the professional arena, which has practical long-term career advantages. It’s all a part of the dumb sociological game in science. While we can pretend to transcend the game, we are on the game board whether we like it or not.
Later this week, I’ll be considering the various priorities that people have in mind when hosting a visiting speaker.
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.
Being a professor is a relatively unique job. We have near-total authority over how we do our jobs, but there are a lot of interests working to shape what we do and how we do it. How we interact with the administration at our university can affect whether we can be successful in what we want to do.
Here’s a way to think about how we approach our jobs, as researchers and teachers within universities.
If you have a salary, you have a boss.
We should consider what structures our relationships with our bosses. Because professors enjoy academic freedom, and those with tenure are free to speak their minds on nearly everything, this relationship is different than most boss-employee relationships.
To do our jobs well, we need to understand the nature of our relationship with our bosses. We need to know how this relationship affects how we interact with our students, and the research community outside our campus.
When the faculty vision of the boss-professor relationship is incongruent with the vision of administrators, things can fall apart.
Here I consider the differing roles of professors and their bosses in the university, and how these distinct roles can work together to maximize the benefit for all parties: administrators, faculty, students, and the scholarly community. By understanding how the faculty-admin relationship is structured, we can all relate to one another in a fashion that fits not only our own interests, but also allow us to provide more and better opportunities for students.
Administrators can empower, or minimize, your ability to get stuff done. By understanding the areas of cooperation and the areas of conflict with administration, we can work to maximize the benefits for all parties. Administrators won’t make decisions in the interests of faculty unless it meets their own interests as well. So, you need to understand not only your own interests, but also the interests of your bosses.
A current conversation about model systems and the benefits of being a scientific specialist or generalist has been interesting, and I thought I’d join in. (This post was written for a while, but now’s a good moment to actually share it.)
Here’s my main point : Becoming a specialist on a narrow topic or taxon is a bad idea, because it narrows your opportunities and decreases both your visibility and your impact. If you’re in a position to choose your research trajectory and degree of specialization, I have two specific prescriptions near the end of the post.
When building a research program within teaching institutions, I’ve seen junior faculty advised by senior colleagues to avoid competing with big research labs. They are advised to find a specialized topic, not subject to competition, and develop expertise. For example, I’ve seen people whose entire research career, at a teaching institution, is built on the phylogenetic reconstruction of a single genus.
The notion behind this strategy is that you can be the world expert in something if nobody else is studying it. This route can allow you to retain a niche in academia, even if you are busy enough teaching that you can’t penetrate a more broader or more mainstream research agenda.
This sounds like a horrible idea.
If you specialize on an obscure topic that nobody is interested in studying, that means that you’ve become an expert in a mostly useless topic. What good is that?
If you are convinced that your particular obscure field should is truly important, and that the whole world is wrong, then that’s a different matter. If you think that understanding the detailed phylogeny of a single genus is important because of the unrealized significance of this group and the broad and general lessons to be learned from this model taxon, then fine, go ahead and spend your career working on it. However, if you choose a specialization for the purpose of defending a narrow niche, then you’re not allowing yourself the academic freedom that produces the best scholarship. And, you’ve intentionally chosen a route to obscurity.
Let’s just say that your obscure specialty suddenly matters to everyone? Let’s say that your model genus simultaneously emerges as an invasive species, produces a cancer-curing chemical and is the secret to carbon-free energy production. Are you going to have a great impact? No.
You’ll get more attention at first, but then you’ll then be shuffled back into obscurity when the big names in the field move into your pet taxon. Then, you won’t even own your little niche anymore, and your work will be seen as even more obscure.
Here’s a true anecdote: I once worked with someone who worked in a tiny niche, without collaborating. Coming up for tenure at a teaching institution, this person focused work on a single niche project over a few years. Shortly after the project was finished, less than a year before turning in the tenure dossier, this person was scooped by a bigger lab. This bigger lab just knocked out this work as a side project with some spare time. This unfortunate choice to overspecialize, without collaboration, meant that this person came up for tenure short of research expectations.
You won’t be a famous scientist working at a teaching institution. So, if you’re going to be a successful scientist, you need to be productive and successful one without the fame. Success in research is often based in collaboration, and people won’t be seeking you out for your narrow expertise in an obscure topic – they’ll be seeking you out because you are good and have something useful to offer for research on an existing question of interest.
Why would someone work in an obscure niche? Maybe it could be a personal passion, which would be a great reason, just like any other hobby. Other than that, the only reason I can think of is that a noncompetitive niche enables someone to continue to consistently publish a large number of papers that few people read. Some have made the argument that working on an obscure topic is the only reliable road to a position in the academic community, for those who are working in teaching institutions. It’s the road to being recognized as a scholar on your campus, but that’s not our real academic community.
Doing this work on an obscure topic buys you a place at the table. That might be true, with a caveat: Working on minutia buys you a place at the kiddie table.
What is a good attitude towards developing a research program at a teaching institution? It should take advantage of the fact that the productivity or prestige of your research program isn’t so important to your institution. I agree with Chris Richardson of Young Harris College, who uses his job at a teaching institution to give him the freedom to do research on anything that interests him:
There is… much less pressure on me to be the expert in one particular niche, leaving the research questions I can pursue much more open.
What is the best approach towards the generalization/specialization continuum as a researcher at a teaching institution? I have two specific prescriptions:
- Do whatever you want! Be free! Use the freedom that have.
- You’re best off if you general work on a diversity of questions, but within a framework that allows you expertise that will be of collaborative utility.
I’m not an ideal example, but at least I’m familiar with myself. Nearly everything I do involves ants. Moreover, it’s on one particular location that’s only 15 km2 in area. It’s easy to argue that this is obscure. However, the questions that I have asked include behavior, community ecology, some ecosystem ecology and a little chemical ecology sprinkled in. I have a set of experimental techniques and data that allow me to have specific, and long-term, information that is very difficult to acquire in any system. So you could say that I only study ants, in this one tiny place. Or you could say that I’m a generalist who works on all kinds of questions, with this one particular system.
What has this approach done for me? I’ve chosen the place where I work carefully, and it is a place where a lot of other people work. The kinds of information that I have generated can be useful to others, and my experience allows me to be of substantial use as a collaborator. Actually, I’m not even close to being the main expert of my taxon of choice at my field site. I am, however, a guy who works there consistently and broadly on a variety of questions, and is prepared to engage with collaborators on new questions.
I’m not required to follow a path that necessitates continuous external funds, marketing my doctoral students and publishing well every year. This is the ideal academic job – I am not required to satisfy anybody with my research program, other than myself. Hopefully the research will be broadly useful to all, but I can design and run it as I wish, based on the funding, timescale and focus that suits my needs.
I’m not wedded to my study system, nor to the single place where I’ve conducted most of my field-based research for my career. But, it works for me, and I really enjoy it, and this depth of experience in this one system and location gives me an avenue to ask lots of questions in all kinds of fields that I wouldn’t be able to do if I switched to a different taxon or location with which I was less familiar.
So, I’m a geographical and taxonomic specialist, but a conceptual generalist. To me, this is the most fun way to do science, and it lets me give great opportunities to students as well as to collaborators. I think this approach merges the benefits of generalization and specialization but minimizes the drawbacks.