The great escape: charting a career outside of academia (guest post)

Note from Jeremy: This guest post is the first in a series on non-academic career paths for ecologists. Not because non-academic career paths are somehow inferior to academic ones (they’re not). But simply because academic jobs are very scarce relative to the demand for them.

This post is by my friend and former Calgary colleague Carla Davidson, who turned off the academic career path after doing a PhD and a postdoc. She’s now an independent scientific consultant. She’s also a blogger, a reality tv star (no joke), and a mom.

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So, it turns out that the best thing I ever did for my career was fail at what I thought I wanted most.

Like most bright-eyed grad students in their first year, I never doubted that my natural brilliance would mean that I could be one of the roughly 10% of PhDs that go on to faculty jobs. I did a project that melded computer science with microbiology. Grad school was an education in not just science, but a sometimes shocking lesson in humility, a yardstick of my strengths and weaknesses, and a persistent reminder to look at what was most important to me. I finished in a little over six years. With mixed feelings. It never felt like the grand victory that I had hoped for after all that work.  What happened to that single-minded focus that begins this paragraph?  In the end, I was consoled by the words a colleague wrote to me: “In the conflict between the stream and the rock, the stream always wins, not through strength, but by perseverance! – H.J. Brown.”

Here’s what happened to that focus. The truth is that though smart, I hadn’t settled on one thing and become an expert in it. My education and experience until now was a long and winding road. I have worked in forestry and wildlife biology; my PhD was in microbiology, genetics and systems biology; and since I’ve worked in vaccine design, microbiome studies, ecological genetics in Daphnia, and experimental evolution. Could someone could question my focus? Yes, they could.

Moreover, the problem with becoming a prof went beyond this lack of focus (In fairness, ‘lack of focus’ isn’t entirely correct. I am just as comfortable with the thought that it was my curiosity that led me down so many paths.  In any case, I harbour no regrets).  My doubts about whether I wanted to be a prof started with my first postdoc. It was the position of my dreams: I went to Michigan State University and worked in the lab of Richard Lenski, studying experimental evolution. It was a beautiful lab, with incredible people and lots of support. But like many women, I was deeply conflicted between my academic dreams and a growing need to settle down. I left my long term boyfriend and the house we bought in Calgary, and resolved to spend half the year in Michigan and half in Calgary. This was difficult on both the relationship and on my meagre stipend. While in Michigan I worked as hard as I could, but roughly every two months I would burst into tears and search the internet for new jobs. I applied for probably 100 jobs over the combined four years that I was a postdoc. How many interviews did I get from them? Two.

One reason for such a dramatic non-response is that my interest was environmental but my scientific work had swerved medical. I didn’t have the work experience for the jobs I wanted.  Also, most big consulting firms have no interest in hiring PhDs, especially when they’re leaving the fields in which they trained. Rightly or wrongly, they think that PhDs can’t work as a part of a team and are more trouble than they’re worth. If so, their reluctance is a further sign that they see their function as one in which they tell the customer what he wants to hear, not what he needs to hear.

By this time, I was just starting yet another postdoctoral position, this one especially miserable. I was back in the dungeon-like lab where I got my PhD (Actually calling it a dungeon does a disservice to medieval law enforcement. It was prone to floods, chemical spills, bad smells from the nearby central sanitation facilities, and random appearances of dead bodies, or body parts, due to the medical anatomy lab across the hall). My income, such as it was, was partly funded by a technician’s position, so I was also responsible for making a dozen different media types for the lab, a most annoying diffusion of my efforts. It was only tangentially related to things that interested me scientifically and, lastly, this dispiriting lab was filled entirely with women who, though I love each individually, as a group tended towards the dramatic.

Such misery often moves us to desperate measures, and, in this regard, I was abetted by a wonderfully supportive supervisor. I got my faculty teaching certificate, continued writing, started sessional teaching at a neighbouring university, and applied for (and got on) a reality television show. My big break came as a result of chance meeting between my sister and a woman who runs a not-for-profit organization that delivers sustainability learning resources, Lisa Fox, Executive Director of Sustainability Resources, Ltd. I emailed her out of the blue, and asked for a meeting. It was awkward and felt like I was asking for a date. But she accepted and we met; she told me all about different people and projects working in the field of sustainability in Alberta, and I hid my shock that there was such a field in this province. I said: “I think what you need is a course on scientific literacy for policy professionals.” She said: “Sounds great. Put one together in six weeks.”

Tina Fey, one of my personal heroes, says she learned everything she needed to know about success from improv comedy. If someone takes the sketch in a direction you didn’t expect, just go with it. In short: say yes. Always say yes.

This is how I started consulting. After all my scientific experience, I knew enough about various fields of science to be generally useful, but not specialized enough to be an expert. I realized that I needed to find a niche for a scientific generalist, and I truly believe that there’s a big need for this. So many policy decisions include various pieces of scientific evidence, and non-scientists really need some help interpreting it. I decided that what I could do was not only translate science, but teach my clients how to think critically about the scientific evidence that comes across their desk. So I designed a half day workshop that managed to get people to start thinking this way.

My last big realization came while I was doing the reality show (Canada’s Greatest Know It All). Whatever audiences might have thought about the ten contestants (obnoxious nerds might have headed the list), they were, to a person, extraordinarily talented in a startling variety of fields. Several of them were running their own successful businesses. One thing became clear to me: though I seem to be unemployable, I was just as smart, scientifically, emotionally and practically, as those against whom I was competing. There was no reason I couldn’t do the same thing. I registered a domain, bought some business cards, and started my business.

So, today, I find myself on a path I enjoy and am rewarded by. My advice is being sought and I get paid for it. I hope that my career trajectory holds meaning to those thinking of leaving the hallowed halls of academe. Especially for those of you whose talents are being wasted on dreary jobs with poor pay, poor job security, and the appalling realization that you went through all that hard work just to find a closed shop.

Pure research may be the best job in the world, but it’s one of the hardest to get, and even once you’ve got a faculty job, the hard work, uncertainty and drudgery only continues until you get tenure. If it’s truly your passion you’ll find a way. BUT, if, like me, you love it, but not enough, think hard about what you are good at. Graduate school does not do a good job of imparting translatable skills, at least formally. But it does teach you some incredibly important things that are underrated: critical thinking, project management, communication, mentoring, team work, and an ability to jump into the deep end of something new and swim your way out. It does NOT teach you how to recognize or market those skills. In fact, the scientific caution you learn often works against you in this regard. So think about what you do best, and figure out a way to market that. If that means that you don’t get a job with some big company, so be it, think of how to market yourself, and start your own business.  I can’t think of any really successful people who became so by attaching themselves to a large corporation.

The minute I completely left academia and committed myself to consulting some amazing things happened, and it’s a constant surprise to me. I now have one major contract and three minor ones that keep me busy and food in the fridge. I have more confidence. No one’s career path is free of rocks and I expect mine still has a few to get in my way.  The difference now is that I have found that being in control of my path makes it easier to kick those rocks to the side.

Who’s asked me to review recently, and how I’ve responded

In case anyone’s interested, here’s a list of the journals and funding agencies that have asked me for reviews since July 2011, and how I’ve responded. I don’t count reviews of invited revisions, because that’s really just a matter of completing the review of the initial ms. Nor do I count papers I handled as an editor before I resigned from the editorial board of Oikos. In agreeing to serve on an editorial board, you’re agreeing to handle any mss you’re assigned to handle; you can’t pick and choose the way you can as a peer reviewer.

I’m starting in July 2011 because I track these data for my university’s biennial performance review; the next one is this summer. I’m putting the data up now because I’m short on time to write real posts at the moment.

Journal or funding agency, # of reviews requested, # agreed to

Ecology Letters, 11, 3

American Naturalist, 7, 2

Plos One, 6, 0

Nature Communications, 5, 3

Ecology, 3, 2

Ecography, 2, 0

Theoretical Population Biology, 2, 0

Journal of Theoretical Biology, 2, 0

Methods in Ecology and Evolution, 2, 0

Proc. Roy. Soc. Lond. B, 2, 0

Journal of Ecology, 1, 1

Oikos, 1, 1

Ideas in Ecology and Evolution, 1, 1

Frontiers in Microbiology, 1, 1

Journal of Applied Phycology, 1, 0

Applied Vegetation Science, 1, 0

Global Ecology and Biogeography, 1, 0

BMC Biology, 1, 0

Plant Ecology, 1, 0

Functional Ecology, 1, 0

Microbiology and Molecular Biology Reviews, 1, 0

Landscape Ecology, 1, 0

Ecosphere, 1, 0

Oecologia, 1, 0

Frontiers in Biogeography, 1, 0

Trends in Ecology and Evolution (book review request), 1, 0

National Science Foundation (USA), 2, 1

Natural Science and Engineering Research Council (Canada), 2, 2

Marsden Fund (New Zealand), 1, 0

Some comments:

• If you’re scoring at home, that’s a total of 60 requests to review, of which I accepted 17.
• I’ve done over twice as many reviews as papers I’ve submitted or co-authored since July 2011, and that’s without counting the editorial duties I had during that time. In the past, I used to do reviewing at something like four times the rate I submit papers. My PubCred balance is very positive and growing, albeit growing slower than it once was. I’m lucky to get as many requests to review as I do, as it means I can pick and choose without worrying about whether I’m doing my fair share of reviewing.
• In only a few cases have I turned down reviews that I really wanted to do but just didn’t feel like I had the time to do. More often, I decline because the paper sounds boring, bad, or too far from my area of expertise. Of course, being busy and thinking the paper sounds boring or bad aren’t mutually exclusive. Again, I’m always busy, and I get lots of requests to review, so a paper needs to sound interesting for me to agree to review it. Note that I don’t just assume that anything submitted to a leading ecology journal like Ecology Letters or Am Nat will be interesting. I judge by reading the abstract. More than once (including just the other week…), I’ve declined reviews because it’s pretty obvious from the abstract that the authors should’ve submitted to a highly-specialized or unselective journal, but for whatever reason decided to take a shot at a journal like Am Nat.
• Speaking of far from my area of expertise…several of these review requests came from journals I don’t read, including some that came from journals I’d never heard of before receiving the request to review. I’ll let you guess which ones those were.  I declined them all, because none of those papers were anywhere near my area of expertise. This illustrates why you should always suggest reviewers in the cover letter accompanying your submission. Editors often are obliged to handle mss far from their area of expertise, and so may have a hard time identifying potential reviewers. Which sometimes leads them to send papers to reviewers who aren’t really appropriate. This also illustrates why you need to put a bit of thought into which names you suggest. I’m far from the only established ecologist who gets far more requests to review than I will ever agree to. So before you suggest me, or any established ecologist, as a reviewer, try to honestly consider whether I’m likely to do it. In general, people are very reluctant to review papers that they wouldn’t be interested in reading. So in suggesting referees for your paper, think about the audience of the journal to which you’re submitting, and whether the referees you’re suggesting are likely to be part of that audience. Someone who’s never published in the journal to which you’re submitting, or in any closely-allied journal, is probably not part of the audience for your paper, and so isn’t a good person to suggest as a reviewer.
• Yes, I do sometimes do reviews for granting agencies to which I am ineligible to apply. Basically, if I think the grant sounds interesting, I’ll review it.
• No, I don’t have anything against open access journals. I decide whether to review for Plos One, Ecosphere, and other open access journals on the same basis I decide whether to review for any journal. If Plos One ever sends me a paper that sounds sufficiently interesting, I’ll review it.
• A few of these review requests came to me at least in part because of my blogging, specifically concerning the intermediate disturbance hypothesis. Judging from the abstracts of these papers, let’s just say that there are clearly many ecologists out there who don’t read this blog.
• In the past, I’ve reviewed for a number of journals and funding bodies not on this list, and for various journals on this list for which I haven’t reviewed since July 2011.

What it takes to do policy-relevant science

I just spent last week at SESYNC which some ecologists might still know better as “the new NCEAS”. It is however different from NCEAS in at least two ways. First the first “S” in SESYNC is “Socio” – not that NCEAS didn’t do plenty of applied ecology with a human dimension, but it is right up front and part of every single project at SESYNC. The second is that the focus is on what SESYNC calls “actionable” science. As some of you know, I have a joint appointment in the Sustainability Solutions Initiative (hereafter SSI) at the University of Maine which has a very similar orientation. Of if you want another hook, the NSF SEES granting program has a very similar orientation. Loyalty demands that I point out that University of Maine’s SSI was the first of these 3 programs!

I should warn you this is a long post. This represents 3+ years of active thinking. Feel free to skim or skip if this is not a topic of interest to you.

I don’t say you need to do the kind of science these three institutions/programs are aiming for. Nor do I say that all science should be of this kind. I still have a healthy component of basic research in my own work and think this needs to keep going. But many, many scientists, especially I find earlier career scientists, want to deliver solutions. And I think I could (if I weren’t lazy) build a case using data from NSF that funding for this kind of science is on the upswing. There is an increasing discomfort with the “loading dock” model of science (do the science, put it on the loading dock, and wait for the adoring users to back up their trucks and do all the work to load it up and use it)

The rest of this post reflect my musings, hard won experience, and education by my colleagues on what it takes to deliver policy-relevant (or actionable science or solutions) .A good part of what I am writing here was learned from my colleagues in SSI*.

So without further justification, let me present my version of the 7 P’s of policy-relevant science (yes I am a sucker for alliterative memory gimmicks). . Also just to be clear I am trained and experienced as an ecologist, so that is obviously my bias, but in this piece I am saying things I believe to apply to science generically (and broadly to include all empirical quantitative researchers and maybe even all academic researchers, certainly not just biophysical science, but remember I can only really claim expertise in ecology).

1. Presentation – one common claim is that science would be used for policy decisions more if we only presented it better. This is the idea that scientists are terrible communicators. It is epitomized in Randy Olson’s movie Sizzle. I might even go so far as to say this has been the biggest effort by NSF to make work more useful – they have been conducting extensive media training under the title “Becoming the messenger” conducting workshops all over the country. Personally, I think this one is a big cop out  Are there some terribly poor science communicators? Yes. Would all scientists benefit from additional training in communication? Yes. Will the world change if all science were well communicated? NO! There are already plenty of great scientist communicators. There are also plenty of NGO organizations functioning on the boundary with professional communicators repackaging the work of scientists. Improving presentation is relatively cheap and requires little effort and change on the part of scientists – it would be nice if it were the solution. But its not.  You can put me down for this being 1% of the problem.
2. People – scientists should talk to people who are going to be affected by or care about the problems they addressing. This more commonly goes under the label “stakeholder engagement” and has been a movement building since the social activism of the 1970s and become a US government mandated part of land management under Clinton in the 1990s under the label Community Based Management or CBM. I 100% think policy-relevant science requires engagement with stake-holders (lots of engagement in most cases). However, I don’t think there is much training for scientists in how to do this. And even more importantly, I don’t think there has been much thought in how this process should occur in a way that leaves the scientist doing research instead of just turning into another activist voice at the table. Many social scientists have written research arguing that scientists are just another voice at the table. And of course many politicians are also framing scientists in this just-another-voice box to pursue their own agendas. Thus, while I think stakeholder engagement is critical, I think scientists need to take some real ownership and leadership in figuring out what this looks like and in training our peers. Iin my experience most stakeholders (and most politicians who aren’t pushing an anti-science agenda and many social scientists like Cash) also think science should not just be another voice at the table, but they don’t yet have a well-formed idea of what science-stakeholder engagement should look like either.  In my opinion David Cash has written some of the most thoughtful work on this topic (e.g. this piece which coined the idea of “loading dock” research). So to repeat myself, scientists need to be thought leaders in what stakeholder-engaged research looks like. The next 5 P’s are some of my thoughts of what this should look like.
3. Problem co-defined – this is probably the most radical but most important of the 7 P’s. The vast majority of scientific questions that get asked come from the scientists themselves. The proportion is a bit lower in natural resource departments  but even there a high proportion of the questions are scientist driven. If one steps back though, it is blindingly obvious that if one wants to deliver useful, policy-relevant science, one ought to ask potential stakeholder constituencies and policy makers what science might be useful to them! This is no different than a business finding out what their customers want. This is not to say that scientists are then obligated to do whatever the stakeholders ask. Often they ask impossible questions, questions more expensive than what they or society are willing to pay for, questions outside the expertise of the scientists talking to them, and yes, questions uninteresting to the scientists talking to them. Question definition is best done as a joint negotiation between scientists and stakeholders, and this has been called in the literature as “problem co-definition”. Despite the obviousness of the necessity for this approach, there is an equally obvious reason why it is rarely used – it involves loss of control for the scientists! it is frightening. It is a radical break from existing practice. The experience of SSI with over 20 separate projects is that every time you start a project thinking you know what the stakeholders need but then go ask them before starting, you are usually only somewhere between 0-50% right. Every single SSI project has changed their research questions in fundamental ways in response to stakeholder engagement. This approach fully embraced will radically change the kind of science that is done. That said, in SSI every scientist is still happy with the questions they co-defined and even happier with the fact they are sure the work will be useful. This might be radical, but it might not be as painful as people think at first glance. It might even be good for science for us to be pushed to pursue some questions we avoid because they’re hard!
4. Place-based – One common difference between what the researchers want and the stakeholders want is that the stakeholders want much more specific research that is useful to them. In contrast, most scientists are trained to generalize, generalize. It can seem a come down to be asked “what is the population size of deer over the last 5 years in Orono, Maine, USA?” Asking what are the general drivers of deer populations is a more scientific seeming question. And again, natural resource department researchers are already much more used to this kind of research (for which biology/EEB departments sneer at their colleagues for not doing general research and for which the natural research department folks sneer back at their colleagues for building castles in the sky). This is a real issue and research really does fall on a spectrum from highly general to place-based (and organism based and time-specific). To some degree people wanting to do policy-relevant science might just need to sacrifice a bit and move past their comfort zone to do more place-based research. But I would like to argue this dichotomy is made out to be bigger than it really is. How often does a researcher aiming for general results really span more than a handful of sites in a small geographic area at a limited point in time? This is research that is place-based even if it might also be designed to be generalizable. Many scientists can do policy-relevant place-based research funded by policy making agencies (e.g. USDA, state wildlife departments) while still writing very general science papers from the data in addition to the detailed grey-literature place-based reports they deliver.
5. Poly-disciplinary – OK – I made this word up to fit my P fetish. Interdisciplinary would be the more common word. Or the hip word would be transdisciplinary (so fully merged the individual disciplines aren’t recognizable). But I think most ecologists recognize that if you want to make the world more sustainability the biggest challenge is humanity. So you better study humanity. The NSF program Coupled-Natural Human (CNH) systems gets at this, although I think it is fair to say that not all policy-relevant science need use a CNH approach (often times the human system completely dominates the natural system and the idea of delicately balanced back-and-forth feedbacks isn’t too useful) nor do all CNH studies lead to policy-relevant science. Instead it is often important to understand the psychology of what motivates people to change, the economics and policy of creating appropriate incentives, human dynamics of population and consumption growth, technology change, etc.
6. Post-research-engagement - There is some talk in the literature about co-definition of the problems (see #3 above) and some talk about improving mass-communication (See #1 above), but I think truly successful policy-relevant science requires more – namely stakeholder engagement during research to some degree, but especially after the research is done. As science transitions into policy, there is a very important period of interpreting research. Again scientists like to pretend we don’t do interpretation, but there is no denying it happens in a policy arena. Being actively engaged with stakeholders during this stage is critical. In part it is because stakeholders can help with the communication and help us strip out jargon and avoid obvious pitfalls (like communicating climate change in degrees Celsius instead of Fahrenheit to the American public). More subtle issues of presentation are often important too – for example there have been interesting studies of “boundary objects” – things that sit on the fence between science and policy like maps and what makes them successful or not. But things beyond communication like pulling out what is important to policy, the implications for policy etc also must occur. To my mind this aspect of stake-holder engagement in interpreting research after the research is done (or at least a round of research is done) is the most overlooked and ignored part of doing policy-relevant science. This is why I prefer the phrase co-production of science with its focus on engagement with stakeholders from start-to-end over the more common phrase co-definition of problems or the other common-phrase of stakeholder-engagement that doesn’t given an explicit relationship of stakeholders to research (opening the door to the scientists are just another voice at the table paradigm)
7. Personal relationships – stakeholder-engagement has already been discussed above. But it is important to note that successful stake-holder engagement will depend directly on personal relationships built up over time and through informal contacts over meals and what not as well as formal meetings. In a boundary-spanning class I co-taught (more about this below) we brought in 20 people with experience in spanning boundaries between science and policy and literally all 20 people said that in the end personal relationships were the most important thing. This is pretty different from science with its efforts (although never fully successful) to focus on objective progress independent of personal links. But if a legislator is going to take a vote based on science, you can bet they want to know the person who did the science and trust them.

So to do policy relevant science, all you have to do is spend a lot of time  with stakeholders, let them have 50% of the job of deciding what research to do, study complex social behaviors in addition biophysical sciences, become more place-based, and open up the research interpretation process to other people and invest time in building relationships with them. No problem, knock it off with a few extra hours of work, right? Of course not! Policy-relevant science involves a fundamental change to the way science is done (again assuming policy relevance is your goal – not all science should have this goal). I have argued above that some aspects like the place-based research and the stakeholder co-defined research need not be as frightening as they seem. But in total, there is no denying doing policy-relevant science is a lot of work! And a big change for many scientists.

So for those scientists who are making this change, or for those educators and administrators trying to facilitate this change, how should the change happen? As always, I am ready with a silly mnemonic. The four T-s of transition.

• Training – This should be obvious to professors, but in truth we have a blind spot – we have gotten so good at teaching ourselves things related to our discipline that we think we can teach ourselves anything. Or that science is the hard part, policy is easy to pick up by osmosis (this is obviously false). If we want scientists to do policy-relevant science we need to train them in skills related to the 7 P’s and this includes soft skills like facilitation  To my mind this can be best summarized as the task of boundary-spanning. A transformative event for me (and I think the 3 colleagues I co-taught the course with – David Hart, Laura Lindenfeld and Kathleen Bell) was teaching a 1 week intensive course in boundary spanning for graduate students. In addition to reading some of the literature and theory of boundary spanning (probably a whole other post in there), we brought in panels of people who successfully span boundaries and had them speak about what they saw as key success factors. But whatever you call it, there are a lot of skills that boundary-spanning scientists doing policy-relevant science need. Media training (P #1) is just the tip of the iceberg.
• Teams – the biggest error in thinking is that one person can or should do all 7 P’s. It is a rather laughable idea actually when you really stop to think about it. This means that policy-relevant science almost always happens in teams. In these teams everybody needs to have the rudiments of the 7P’s but different individuals with different strengths and expertise can complement each other to actually fulfill the full range of the 7 P’s.
• Tag teams- policy relevant science can also happen by one person doing one piece and making it available in a public manner and then somebody else picking it up and doing another piece, not unlike children’s tag-teams where when you tag somebody you go out and they step in to do something (or WWF wrestling if you prefer). People can do this without ever thinking of themselves as a team or ever even meeting. There is obviously a continuum from strong teams through weak coalitions to tag-team processes. And tag-teams are dangerously close to the loading dock model that was derided above and in the piece linked to above by Cash. Thus I expect the majority of successful policy-making will occur closer to the team than the tag end of the spectrum. But some good policy-relevant science happens through tag if people do it in a thoughtful way. And no doubt this approach can be used to channel more basic research in useful ways. As an extreme example you don’t need stakeholder engagement to know that cold-fusion or understanding the causes of extinction are important research topics. Just don’t use this as a cop-out to avoid teams.
• Thinking – I’m talking about big changes. Scientists need to apply their academic approach of discussion, analysis and data to the 7 P’s and success factors in doing policy-relevant science. Social scientists have been the ones studying and writing about this problem most to date. Many of them are doing a good job. A few of them in my opinion don’t get biophysical research at all. But it is kind of ironic that if the ideas which emerge are things like teams and inter-disciplinarity that the topic of how to policy-relevant science is not team-based and inter-disciplinarity. A full understanding of how best to do policy-relevant science needs to include the biophysical scientist’s expertise and perspectives too. This has not been happening enough.

You can call everything I have talked about boundary spanning or co-production of science (as Cash that I cited above does). I think both of these phrases are useful. But whatever you call it, it is a lot of work and the world needs more of it. And as a scientist trying to learn this material, I found very little published material (journal articles or otherwise  to help me learn. That needs to change.

I am very curious to hear what others think. Again I don’t want this to turn into a debate about whether policy-relevant science should happen and scientists should be part of it. They should but not everybody should be forced to do so – basic science is important to. For those who would describe themselves as already engaged in policy relevant science, what did I get right or wrong? For those just starting out, was this helpful? what are your impressions? For those who have never done policy relevant science, does this scare you away or appeal to you?

* Including amongst many others David Hart, Tim Waring, Kathleen Bell, Mac Hunter, Aram Calhoun, Laura Lindenfeld and Shaleen Jain

Education Components of CAREER Proposals (UPDATED!)

This post is aimed at people who are considering applying for a CAREER award from the US National Science Foundation. For those who aren’t familiar, these awards are aimed at pre-tenure faculty. The key ways they differ from regular grants are: 1. they are restricted to pre-tenure faculty, 2. they are for five years instead of three, and 3. they include both a research component (like typical grants) as well as an education component (which, to a first approximation, is sort of like a broader impacts section on steroids). I’m focusing on the education component here. I am writing this based on experience applying for one (which included reading the CAREER proposals that had been written by a lot of different people), talking with people who applied for them about what worked and didn’t work, reviewing CAREER proposals, and talking with others who’ve reviewed CAREER proposals about what generally works and doesn’t work. With a CAREER proposal, if you don’t do a good job with the science part and the education part, you won’t get funded. I’m hoping this post helps people as they work on the education component.

As with any proposal, it is immensely valuable to get examples from people who’ve applied. Clearly it’s good to read proposals that were successful, but it also helps to read ones that were not, especially if the person is willing to share reviews, too. Knowing what doesn’t work can be as important as knowing what does.

In my opinion, the following things are needed to have a compelling education component of a CAREER proposal:

1. Focus on a particular subset of educational activities. Don’t try to do all the possible broader impacts that NSF lists! Choose 1-2 areas that you particularly care about and focus in on those. In my case, I focused on topics related to underrepresented groups in science. I had several different activities, but they all centered on that theme.

2. Convince the reviewer that what you’re focusing on is important and that what you’re proposing to do to address that problem really will address it. Cite your methods/approaches in this section of the proposal the same way that you would the methods in the regular part of the proposal. For example, if you are proposing to develop hands-on activities for middle school kids to try to help them stay interesting in science, show (with citations) why hands-on activities are particularly valuable, and why it makes sense to focus on middle school kids.

3. Convince the reviewer that you can do the proposed work. Don’t just say that you will recruit minority students, or that you will set up a summer program focused on math-bio, or whatever. Prove you can do it. It’s the same as with the regular part of the proposal: preliminary results help convince the reviewers that you can pull it off. Yes, this means that you need to do some of the work ahead of time, before you know whether you will be funded. But, in my opinion, that is time well-invested, in part because it increases the odds that you will be funded. And, if you take my advice from point 1 above (about choosing something you particularly care about), hopefully you’ll find that you enjoy the time you invest in this.

4. Take advantage of existing infrastructure at your university. Lots of places have units devoted to helping with teaching and outreach activities.  Maybe they already have an existing camp in place that you can work with. Maybe they can provide you with references related to pedagogy. Maybe they can help you design ways to assess whether your approach is working. Whatever it is, take advantage of these resources. At Georgia Tech, I established my own links with a camp at Piedmont Park (located in Midtown Atlanta near Georgia Tech), and I also took advantage of the help from CEISMC, which is Georgia Tech’s Center for Education Integrating Science, Mathematics, and Computing; they helped me add in realistic assessment tools.

5. Include a means of assessing the success of your education component. For example, if you are going to design an educational activity for K-12 students, you should assess whether that activity is an effective means of achieving your educational goals. In my case, as I said above, I relied on help from educational experts (from CEISMC) for developing those assessments.

6. Similar to a regular proposal, if someone’s help is essential to the success of the education component, get a letter from them. In my case, I included letters from the person who runs the camp at Piedmont Park, a letter from someone at CEISMC who could help me with assessments, and a letter from a colleague at Spelman College who helped me recruit Spelman students to work in my lab.

7. Devote funds to the education component: Another way to show that you are serious about the education component is to include funds in your budget to support it. There are lots of things you could budget funds for: supporting students and/or K-12 teachers, buying supplies for classrooms or camps, etc.

Of course, all of the above is just my opinion on what makes for a compelling education component of a CAREER proposal. If you disagree (or agree), please let me know in the comments!

UPDATE: In the comments, Ethan White pointed out something very important that I forgot to include in this post. It’s so important, that I will make it point 8:

8. The research and teaching components should be integrated with one another. Having a great education component that is totally unrelated to the research component will be seen as a major weakness. Reviewers will want to see synergy between the components (and are asked to consider that when reviewing CAREER proposals.)

Prioritizing manuscripts, and having data go unpublished for lack of time

A few recent things have gotten me thinking about how I (and others) prioritize working on manuscripts. First and foremost, I have a new baby who is not yet in daycare. My husband and I trade off watching him, and I need to be really efficient and make sure that I work smart during the chunks of time that I get to work. (By the way, one thing I’ve discovered is that I should schedule Skype meetings for times when I am watching the baby, as it’s relatively easy to keep him happy by bouncing on a yoga ball while Skyping. Yoga balls and Skype are apparently important components of work-life balance for me.) In short, prioritizing tasks on my to do list is even more important right now than it normally is. Second, there has been renewed chatter on twitter related to whether some data are not worth publishing because they are not of sufficient interest (which, for some people, apparently is if they can’t aim for Nature, Science, or PNAS). DrugMonkey has been heavily involved in these discussions, and wrote this blog post in response to one of those discussions. His first point is “never let data go unpublished for lack of impact.” That seems reasonable. But it made me wonder how much I and others let data go unpublished for lack of time. And, if that is happening, is it a sign that I (or we) should change how we approach things?

To explain a bit more: every PI that I know – and many postdocs and grad students – has generated more data than they have been able to publish. This means that decisions are being made about which projects to write up and which ones not to. As a grad student and postdoc, when deciding which things to write up first, I generally focused on the studies that I thought were the most interesting. I certainly thought it was important to have publications in top journals (and by “top” I mean journals such as Ecology, AmNat, and Evolution), and so whether a manuscript had a shot at one of those journals did influence my thinking on where to place it on my priority list. At the same time, if I manuscript didn’t have a shot at a top journal, I still hoped to get it published at some point (possibly by collecting additional data, or by aiming for a lower impact journal). I just didn’t prioritize it as highly.

Now, as a faculty member, my lab generates even more data, and I have even more responsibilities (especially teaching and service). How do I prioritize manuscripts now? At this point, it is based on who is the lead author. If it is a grad student or a postdoc, that manuscript moves to the top of the priority list. (This recent post by BabyAttachMode suggests not all PIs prioritize this way.) Within that group of high priority manuscripts, it’s more or less first come, first served. In the past year or so, working on those manuscripts has basically taken up all of the time I have available for working on manuscripts. The unintended side effect of this is that manuscripts that do not include students or postdocs as coauthors are currently languishing near the bottom of my manuscript to do list, and it makes me wonder if/when I will get back to them. In this case, results are going unpublished due to lack of time. I really wish this wasn’t the case, but I’m not sure of what to do about it.

This means that some of the data we’ve collected – some of the things we now know based on work done in my lab – are not available generally, which is wasteful. Is this inevitable? Does it mean that everyone who currently has a file drawer of data (or, more likely, folders on their computer containing unpublished data) should take some sort of hiatus from doing science until they’ve cleared the backlog? I don’t think that’s likely to happen, but I don’t know what the right answer is.

So, readers, my questions for you: How do you decide which manuscripts to work on first? Has that changed over time? How much data do you have sitting around waiting to be published? Do you think that amount is likely to decrease at any point? How big a problem do you think the file drawer effect is?

Related posts:
How do you decide authorship order? (by Jeremy)
How to decide where to submit your paper? (also by Jeremy)

Surviving your comprehensive exams

Quite likely the comprehensive exam (aka qualifying exam) is the most feared moment in an academic’s life. It is on my mind right now because it is comprehensive exam season. I am sitting on two comprehensive exams (hereafter comps) this week, advised a 3rd student how to prepare for comps in June and did several more of both activities the last few weeks. I thought I’d share a few thoughts on surviving. I expect a majority of readers have passed their own, but this post should: a) be helpful to those who haven’t, b) provide a place for survivors to share their advice in one place on the web, and c) help those of us who have students and have to help them navigate it.

I’ve served on 3 faculties in the US and Canada, so I think I have a pretty good idea of the range of variation in North America, but to be honest, I have no idea of how things differ in other continents, so keep that caveat in mind. Comps usually have 3 parts (always in the same order):

1. Presenting your dissertation research proposal to your committee, answering questions about it, defending and revising until your committee is happy. Some places this is a formal part of the comps and possibly public, some places it is more informal and just your committee. Many places you are supposed to write your research proposal like an NSF grant (15 pages) other places they say 10 pages, but students often go way over these limits. I don’t actually recommend exceeding the limits (but I confess mine was 32 pages).
2. Written exam. Normally 3-5 of your committee members will come up with a question that you have to answer in writing (on a wordprocessor, not handwritten). Two places I’ve been each question was given 3-7 days, was open book and you were expected to do literature searches, find new papers and synthesize them (thus spanning 3-4 weeks). My current place you have 4-6 hours on each question (3-5 consecutive days), often closed book, and more of a “what do you know” question. There are pros and cons either way. Mostly I am partial to the longer questions since it really tests the ability to read literature and synthesize. On the other hand, students who are facing closed book knowledge questions have been in my experience more motivated to study and often transition better to the oral exam
3. Oral exam – this is the part that really scares everybody. Just you facing 5 professors asking you questions. Most places I’ve been this is required to last at least 2 hours and no more than 3 hours. Some places have external examiners (outside your committee and department) to keep your committee from being too soft, some don’t.

Usually the written questions will center around topics related to your thesis. Technically in a qualifying exam the oral questions are also centered around your thesis, but in a comprehensive exam questions across your whole field (i.e. all of ecology and evolution) are fair game, hence the name. In practice most are strongly centered around your thesis topic with a few basic broader knowledge questions (if you’re an ecologists know at least the basics of evolution; also know the names and contributions of the 10-20 or so most famous people historically in your field).

There is no way around it – comps are one of the most fear inducing experiences you will ever have. I get an adrenaline rush but enjoy job talks and interviews, I was cool as a cucumber on my wedding day, and I was well prepared and fully expecting to do well on my comps. But it was still numbing. I found myself walking into walls culminating in slicing my finger while cooking and going to the emergency room. After this, I started keeping track and large numbers of people I knew had some major incident in the months leading up to comps (driving over their backpack containing their laptop, minor car accident, etc). It is truly a distracted, even-out of body time.

I don’t say this to scare you if you are in the miraculous 1% that isn’t stressed. But just to normalize it for the vast majority who may find this the most stressful thing in your life.So does everybody else. A great deal of this is self-induced. Take a type-A personality who has gotten good grades all their life and tell them they’re about to take an exam that could flunk them out of their life dream, and well, we put pressure on ourselves. This is not productive. And not even really rational. Your committee has already sunk time in you – they want you to succeed! Pretty much everywhere will give you a 2nd chance if you fail. And ask around to see how many people in your department even failed once (varies widely but usually in the 3%-10% range) and failed twice (i.e. flunked out – usually it is down in the 1%-3% range), and most of these people you could have predicted in advance by grades, prior negative feedback from the adviser, etc. Its not particularly likely you’re going to fail!

There is a piece of this fear that many call hazing. And I won’t deny that there is some piece of this in some departments and some individuals. But for every professor who acts this way, there are three who do everything they can to quell it. We all had to pass comps too! And again its a waste of our time to fail somebody. Less appreciated is that comps are not just a way of “keeping up the standards” but are actually designed for the benefit of the student, believe it or not! I say this for two reasons:

1. Comps are a chance where you can force a student to learn something. I can’t tell you how many times I’ve had a student where I told them you really should read such and such paper 3 times and they never do. Then comps come around and I tell them to read the paper, and they do. And they thank me for it afterwards. Students who really need to know genetics but stay away, can be forced to learn genetics. And etc. Some part of comps will be forcing you to fill in the holes you’ve avoided filling in.
2. You will face these situations in the future. Job talks. Postdoc interview talks. Presenting reports if you work in the government, possibly to hostile audiences. Giving testimony to legislatures or even in courts.

The last is really the main point of comps, and indeed the standard by which comps are judged. If you present as somebody who will go on and do a credible job of sounding knowledgeable and defending your ideas in your defense and job talks, you will pass your comps. This is the real goal.

A brief word on format. Since the 2-2.5 hour goal is nearly universal, this means the schedule looks similar most places. The professors will set up a rotation, usually the furthest from your research first, your adviser last. The first professor is told they have about 20 minutes to ask you questions. When they finish you go to the next professor and on through all four or five people. Many but not all places then take a 5 minute break where you leave the room and they confer. Then usually the professors go around as second time for 5-10 minutes each. Five to eight questions would be typical for one professor in the first round and one to three in the second round.

So on to advice I give students (this is all about orals – writtens vary more in expectations from place to place and are not what most people want advice about anyway):

1. Just do it – students always want to postpone their comps. I don’t let them. I insist on 4th or 5th semester. Once you’ve got the classes and reading under your belt, you have nothing to gain (indeed possibly more time to forget) by waiting. Comps hang over everything and make you less efficient in your research. GET THEM OUT OF THE WAY!
2. Prepare. This may sound blindingly obvious. But increasingly I am seeing students who haven’t studied adequately. You should study 10-20 hours/week for 1-3 months. And this is after your two years of course work and independent reading beyond coursework (its rather late to start cramming in learning completely new things). I produced a 20 page cheat sheet of everything I thought it was important for me to know. I still refer to it today. If you’ve gotten this far you know how to study – just be sure you do it.
3. Do the standard tricks. Namely ask each committee member for their recommended reading list. And hold a mock oral comp (and maybe a mock research proposal defense/discussion). Mock comps work best when they are like the real thing. Not 15 grad students shouting out questions. This honestly is a waste of time. Instead, get 5 students who have already passed and are close to graduating, tell each one which of your committee members they are representing (ideally their adviser). And emulate the format (i.e. 2 hours, 20 minutes each person, etc). You’d be surprised how many professors have a question they ask in every comp. Advanced graduate students know what these are and they know what style each professor has. If you let them have fun pretending to be their advisers grilling you, you will have fewer surprises.
4. You will not know the answer to some questions. That’s OK. Although you are at this point more of an expert than you think, there are still five of us. And it is our job to find out the boundaries of your knowledge, which we can’t do if we don’t ask you a few things beyond your boundaries. I had questions that I didn’t know the answer to. Your adviser did. Everyone of your committee members did. You will too. The real key here is how you handle that. Namely don’t panic yourself into a death spiral just because you didn’t know something. It is totally normal. A majority of failed comps I’ve seen occur when a student gets a question they don’t know early on and they lose confidence and start spiraling down. If you don’t know answers to half the questions and you’re two professors in, then panic. Otherwise, hang in there!
5. It’s OK to say “I don’t know”. This is a corollary to #4. Part of the comp is making sure you know what you know and what you don’t. Sometimes saying “I don’t know” will be the end (usually for specific factual questions). Other times it will lead to a response of “OK, let’s see if we can think this through” followed by some leading questions to help you. Either way, you’re better off than umming and ahing and making up answers.
6. Play to your strengths. Spend a little more time on questions you know a lot about. From the schedule described above, you can tell that most questions should have answers in the 30 seconds to 2-4 minutes range. A five minute uninterrupted monologue is getting too long for most questions and ten is way too long. Sounding meandering and unable to know when you’ve answered the question is bad. But be sure that if you get a question you know the answer to really well, don’t give a one word answer! Draw some connections. Expand! Comps may seem eternally long, but they are finite and you have some choice what you fill those two hours with. I am NOT saying you should watch your watch during your oral – your focus should be answering the question. But you might want to ask somebody to time you a bit if you do a mock comp. Some questions will be interactive. A professor will ask you to do a task (e.g. draw a particular diagram on the board), and then another (show how it changes in scenario x), and then so on that build on each other. If you sense you’re in this scenario (being asked to go to the board is a good clue) don’t drag things out because the real question is five steps in and the professor wants to make sure they have time to get there. In all I’ve given kind of mixed messages here – but be aware how long your answers are and think how well that is serving you and whether they are frustrating the person asking you.
7. Its about attitude not knowledge. Not totally – you definitely still need to study. But attitude is a big part of deciding the outcome. Confident is what you are aiming for. Arrogant is a rare problem (and it is usually a nervous tick or lack of preparation when it happens). Timid and unconfident is a much more common problem. As I’ve said, people want to see you comfortable putting your knowledge out there and standing up for your opinions. Don’t keep checking your questioner’s face to see if you’re getting it right or not. If you’re answering (and not going for #5 above), sound like you know you’re right. It is usually blindingly obvious to your committee when you’re losing your nerve and when you’re digging in and plowing ahead even if you’re on the ropes for a moment. You will have moments of both, but try to have more of the latter.
8. Get some rest – as I hope I’ve emphasized, there is a significant psychological component to this. Cramming until 2:00AM the night before is the wrong thing. Get your exercise, take care of yourself, go out to a movie the night before and get a good nights sleep. If possible schedule the comp in the morning or afternoon depending on when your body rhythms have you most awake (although increasing scheduling the comp with 5 professors is such a challenge you may have to let this one go).
9. Have fun! – This might sound impossible. But the comps that go the best are ones where people go in thinking that they’re looking forward to having an intellectual discourse and treat it as a bit of a game. And two of the last three comps I sat in on the student actually did say it was fun afterwards. And not coincidentally, they both did great.

So if you haven’t yet had your comps yet, they’re not as bad as you think, have some fun, and good luck! If you have passed (or have been advising your students how to pass for 30 years), what advice would you add?

Advice: Why some academics SHOULDN’T blog (or use Twitter, or Facebook, or…)

I came of age as an academic more or less in parallel with the rise of online tools that changed how many academics work. When I was an undergrad, email was just starting to come into widespread use. I only started using it my senior year (1994-5), and I handled my email using Telnet. Senior year was when I also started to play around a bit with the world’s first popular web browser, Mosaic, then less than two years old and the forerunner of today’s Firefox. I’m old enough to remember a time when search engines didn’t exist, and then a time when they sucked. The best of them in the late ’90s was AltaVista, because it had millions more pages indexed than competing engines. Miiiiillllllions!

I will now pause this post while younger readers mock my age. Younger readers: begin your mockery…NOW!

[pauses, looks at watch, wonders idly why MTV doesn't show videos any more, the way it used to back in the good old days...]

Ok, welcome back! So, like I was saying, while I’m young enough to have grown up with the internet, I’m also old enough to have learned a fair bit about how to be a scientist and an academic before the internet mattered to anyone but Tim Berners-Lee. And because the internet took off so quickly, there aren’t that many people like me. You don’t have to be that much older than me to have become fairly set in your professional ways before the advent of what used to be called “electronic mail”. And you don’t have to be that much younger than me not to be able to remember a time before, say, Google or Facebook.

Which I think gives me a fairly unique perspective on the role of online tools in the production and communication of science. I’m not someone who reluctantly accepts email as a necessary evil, I don’t find Faceplant and Tweeter or whatever the hell they are to be incomprehensible, and I don’t think blogs are just vanity publishing. On the other hand, while I know what Twitter and Facebook are, I don’t use them myself. I don’t use keyword searches to keep up with the literature, and I only use Google Scholar’s recommendations as a supplement to scanning the titles of new and forthcoming papers from a (lengthy) list of journals. I still decide where to submit my own papers based on traditional criteria, weighted in a traditional way. I don’t use any reference management software. I think that pre-publication peer review is just as important as ever, and that “post-publication review” is a non-starter. Etc. I wouldn’t be surprised if many of my older colleagues look at how I operate and wonder “Why does Jeremy Fox bother blogging, when he could be writing papers for Nature and Ecology Letters?” And I wouldn’t be surprised if many of my younger colleagues look at how I operate and think “How can Jeremy Fox be a blogger and yet not see any reason to be on Twitter or Facebook, and still see plenty of value in glamour mags like Nature and Ecology Letters?”*

But I’d like to think that I’m not merely a fence-straddler. It’s not that, because my professional habits were partially formed before the internet took off, I now use some random subset of all the online tools that someone like Jaquelyn Gill uses, or that I’ve given up some random subset of the practices I learned from Peter Morin. Rather, I use some online tools a lot, others a little, and others not at all because that’s what works for me.

Every word in that last italicized phrase is important. My way of working does work. Hard as it may be for some of my more senior colleagues to believe, yes, I do find blogging to be a really good use of my time. It doesn’t take a lot of time away from activities I could otherwise be pursuing, and in return I’ve gotten multiple papers I wouldn’t otherwise have written, a much higher profile in the field, and a lot of positive feedback from readers. Conversely, hard as it may be for some of my more junior colleagues to believe, my way of filtering the literature keeps me very well and very broadly-informed about what’s going on in ecology and other fields, finds me a lot of cool papers that I never would’ve known to search for and that never would’ve been recommended to me by my friends or Google Scholar, and does so without taking much time. Insofar as both my older and younger colleagues struggle to understand how this could possibly be, I suspect it’s because they’re forgetting that my way of working works for me.

Now, in some cases, what works for me would work for anyone. For instance, I use email. It’s been a long time since you could be an academic without using email. But in other cases, my way of working would not work for everyone, or even for most other people. Sometimes, that’s because I’ve consciously tailored my way of working to what I know to be my own strengths and preferences. For instance, I like blogging, I’m able to write good posts pretty quickly and pretty often, I’m tenured, I’m in Canada (so I don’t have to chase grants all the time), and I’ve built a big readership. That means that, in deciding how much of my time to allocate to blogging, the cost-benefit calculation is quite different for me than for someone who doesn’t like blogging, or writes slowly, or etc. In other cases, what works for me works because I’ve developed into the sort of person who can work well in the ways that I work. Just as I’d presumably have become a different sort of ecologist if I’d gone to work with Bruce Menge rather than Peter Morin, presumably I’d have become a different sort of ecologist if I’d, say, taken up Twitter and Facebook years ago. Everyone “coevolves” with their own ways of working–we choose them, but using them feeds back and affects us, making us different than we would’ve been had we chosen to work in different ways.

All of which explains why I’m all for it when folks explain why their way of working works for them. That helps students figure out their own ways of working, and helps more experienced people like me learn new ones, or at least make more thoughtful decisions about sticking with our old ones. Like I said, I didn’t always use Google Scholar; I tried it out on the suggestion of a colleague. And I still don’t use Twitter, but thanks to Meg and commenters I now have a much better appreciation for why some people do (I myself have been guilty of failing to appreciate that what doesn’t work for me may well work for others).

All of which also explains why I have little patience for people who think that others who work in different ways than them must be doing it wrong, or at least less well than they could be doing it. I’m sure that ecologists younger than me are, as a group, really sharp, that they really love doing good science, and that they basically define “good science” more or less the same way I do (and insofar as they don’t, well, it’s not as if more senior ecologists all agree on exactly what “good science” is). So if many younger ecologists work in ways that I wouldn’t choose myself, well, I’m sure they know what they’re doing and that it works for them. And I’d say all the same things about ecologists older than me. Because in my experience, when it’s really dead obvious that there’s an objectively-better way of doing something, they all adopt it, rapidly. That’s why, very shortly after email was widely adopted, journals starting accepting ms submissions and sending our review requests via email rather than via hard copy. Email was obviously a better way of doing things, so everybody, even the most senior ecologists, quickly and happily switched (and then shortly afterwards switched again, to online ms handling systems). In contrast, it is not nearly as obvious that all ecologists who aren’t on Twitter, or on Facebook, or using Google Scholar, or blogging, or whatever, would be better off if they were (or that the field as a whole would be better off). Which is why all ecologists aren’t on Twitter or whatever. Now, maybe someday they will be–but that will be because the field will have changed to such an extent that not being on Twitter or whatever is no longer a viable option. And if you say that day can’t come soon enough, ok fair enough, tell people why you like Twitter or whatever and maybe some of your colleagues will try it too. There’s more than one way to be a successful ecologist, so own your choices and explain them to others as best you can. But be careful not to cross over into telling others that they’re wrong, or even suboptimal, not to do as you do. Personally, I prefer to trust people, young and old, to figure out their own ways of working, and to save my critiques for the science that they produce.

This post was prompted by reading a recent blog post by Reuters financial blogger Felix Salmon. His post asks whether, these days, financial investors “must” be on Twitter if they’re going to make informed investment decisions. And his answer–and remember, this is coming from a guy who sends dozens of tweets a day, follows over 1200 people on Twitter, and has over 92,000 Twitter followers himself–is “no”. Here’s the final paragraph of his post (emphasis in original):

All of which is to say that while I’m sure there are many investors out there who would be lost without Twitter, there are surely just as many for whom it would be little more than an unhelpful and noisy distraction. The great thing about Twitter is that the value and the conversation take place among people who want to be there. Telling people that they have to be there, or else they’re missing out, is actually not helpful. Because the one thing we can probably all agree on is that people who feel obliged to be on Twitter are very unlikely to either contribute or receive much of value at all.

Couldn’t have said it better myself. Though I did try just now.

*I actually hope there are people older and younger than me who think these things. Because I have a perverse streak, and so I kind of like the idea that something about the way I operate is annoying or incomprehensible to everyone.

How do you decide authorship order?

Authorship of scientific papers matters because it’s a way of claiming credit and assigning responsibility. The authors are all and only those people who are entitled to claim credit for the work reported in the paper. And they’re responsible for the work reported in the paper, and so (for instance) are the ones who get blamed if the paper turns out to be flawed and needs to be corrected or retracted. Further, order of authorship traditionally provides information about the relative contributions of the authors–degree of credit and responsibility, if you like. So, how do you decide who the authors are on your papers, and in what order they’re listed?

My own approach, which I think used to be fairly standard in ecology and is still pretty common, is to think of a paper as arising from three main activities: conceiving and designing the study, conducting the study (e.g., collecting and analyzing the data), and writing the paper. You’re an author if you make a substantive contribution to at least two of those three. You’re the first author if your overall contribution exceeds that of any other author (typically, because you’ve made a substantial contribution to all three activities). And other authors are listed in decreasing order of their overall contribution.

My approach has some implications. It means people who contribute only to data collection, such as research assistants or technicians, wouldn’t be listed as authors (they would be listed in the Acknowledgments, of course). It means that the PI shouldn’t be listed as an author just by virtue of being the PI, or even if they came up with the basic idea for the project or gave some suggestions on study design but didn’t make any other contribution.

As sketched above, my approach obviously doesn’t cover all possible situations. It’s particularly ill-suited to big collaborations in which each of the “main” activities is subdivided among many people. But it at least provides a starting point for thinking about other situations. For instance, contrary to what I just said above, I do think it would be weird for someone not to be an author if they made a substantive contribution to the writing, even if they didn’t do anything else. But that situation rarely comes up in ecology (it comes up in medicine, where hiring “ghost authors” who write the paper but aren’t listed as authors, isn’t unheard of). Similarly, I could see making someone an author if they were largely or entirely responsible for developing and designing the project, even if they didn’t contribute much in other ways. As a grad student, I did a project that my supervisor had designed as part of a grant he got before I joined the lab. He handed the project to me to do, as a way to get the project done and as a training exercise for me. Not yet knowing much about the topic or the study system (that’s why I needed the training!), I followed his design and proposed analyses to the letter. I also wrote the paper. And then I had to talk him into being second author, as I didn’t feel comfortable being sole author without having made any contribution to the study design.

Underpinning all of this is the intuition that authorship should go to the people who make contributions of their own to the project, as opposed to people who contribute only by following instructions (as with research assistants who collect data as instructed but make no other contribution). On the other hand, data sharing is increasingly common and important in ecology. So it’s fairly common for people whose only contribution was to collect the data (or instruct others to collect the data) to expect co-authorship of any paper using their data. Especially when those data come from ongoing long-term studies and monitoring programs, that need to justify their continued funding by showing that their data are leading to lots of papers. I’ll be interested to see how attitudes on this issue develop in future. If in future data sharing goes from being valued to being expected or required, then it’s hard to see how sharing data should entitle you to claim co-authorship (i.e. credit and responsibility) for papers produced by others using data you collected. For instance, if you deposit gene sequences on GenBank (which journals, funding agencies, and professional norms require geneticists to do), you aren’t entitled to claim co-authorship of any papers that later use those sequences.

There are always judgment calls involved in determining authorship. For instance, if I just make some comments on a draft ms written by someone else, does that count as a “substantive” contribution to the writing? (I’d say it depends how numerous the comments were and how much the ms was altered by incorporating them) As with all judgment calls, I think the way you learn to make them is to make them, and to talk about ones others have made. General principles only get you so far; there’s no substitute for thinking about specific cases.

The above is merely my approach to authorship. Different people have different approaches, which is a bit of a problem since others can’t interpret authorship order properly if they don’t know how it was determined. Many cellular and molecular fields are pretty much the opposite of ecology: the last author, not the first author, is considered to be most important. The last author is usually the PI. Indeed, in many molecular labs the PI is always the last author on every paper produced by the lab, presumably on the view that the PI is ultimately the one responsible for all of the lab’s work. I hear from colleagues that this approach to authorship is starting to creep into ecology, which dismays me. It’s totally foreign to how I was trained, and it smacks of allowing external incentives to determine authorship. On the other hand, as someone who (like everyone) has strong incentives to have my name on as many papers as possible, I admit that I feel tempted to take a more expansive attitude towards authorship than my supervisor took. I do wonder if the average number of authors per paper is going up in all fields of science not just because we’re all becoming more collaborative, but because we’re all taking a more expansive attitude and lowering the bar on authorship, so that we can all put our names on more papers.

Perhaps the way to deal with this is to get away from authorship order as a means of apportioning and recording credit and responsibility. Authorship order was fine as a summary of authors’ overall contributions when few papers had more than 2-3 authors, but times have changed. I think it’s great that some journals now require statements of author contributions, which are published as part of the paper (e.g., “John Doe designed the study, John and Jane Doe performed the experiments and analyzed the data, and Jane Doe wrote the paper with assistance from John Doe.”) I’ve sometimes included such statements in the Acknowledgments even when not required to do so, and I plan to always do so in future. I’d encourage others to do the same.

The other universal piece of advice I can give is that everyone involved in a project should talk about how authorship of any future papers will be decided as early as possible, ideally before the project even begins. That means, for instance, that supervisors should talk about authorship with their grad students before the students even start designing their research projects, and collaborators should talk about authorship before they actually begin working. I actually talk to prospective students about my general approach to authorship issues when they visit my lab. Far better to address the issue early on, at least in a broad way, than to have to deal with disagreements and misunderstandings later. Similarly, all authors should discuss and approve a statement of author contributions as early on as feasible.

Peers, mentors, role models, and heroes in science (UPDATED)

I now receive emails from grad students and postdocs indicating that, for them, I am now a person that they look to as an example that one can have a tenure-track career with children…It’s a little scary – and definitely humbling – to me that some people view me as a role model for this sort of thing. – Meg Duffy, in a recent post here

When I’ve crossed paths with him at meetings, I have been reluctant to disturb him. Even when he’s two chairs down from me having a beer. I had a similar feeling when I walked past John C. Reilly when I was out to lunch last month. I wouldn’t want to disturb his pleasant lunch by getting all excited that I saw him, though it’s moderately exciting, like finding a bird far outside its range or stumbling on a Leptogenys colony in your field site…he’s frickin’ Bert Hölldobler. I’m pleased to retain some awe in his presence. – Terry McGlynn, in a recent post at Small Pond Science

Which is something you really need to try to get over–the feeling that anyone is your superior…have the confidence to approach others as peers–and they’ll treat you like one. – Me, in an old post here

I once did a post about networking at scientific meetings where I referred to a hypothetical “Dr. Famous”. It’s a little weird to think that, at least in some folks’ eyes, I’m Dr. Famous. – Me, in a comment here

I am not a role model. – Charles Barkley, in this old commercial:

Think of some people you consider to be your scientific peers.

Now think of some people you consider to be your scientific mentors.

Now think of some people you consider to be your scientific role models.

Now think of some people you consider to be your scientific heroes.

Now consider those four sets of names. Did you name anyone more than once? If so, are you sure that’s even possible? After all, a peer is someone you consider to be your equal in some sense, right? You’re the same as your peers, not in every way, but in some important way. But the whole point of mentors, role models, and heroes is that they’re different than you. A mentor is someone who’s more knowledgeable or wiser than you about something, who agrees to advise you. A role model is someone who is different than you, whom you try to model yourself after in the hopes of someday becoming like them. Which you can do without them knowing that you’re doing it, or even knowing you at all, in contrast to a mentor. And a hero is someone whom you respect and admire to such an extent that you probably don’t try to model yourself after them, because how could you ever hope to become like them? Indeed, you might admire them so much that you’re too awed, nervous, or deferential to even speak to them.

Are there people you named in one category whom you wish you could’ve named in another category? If so, why? And are you planning to do something about that? For instance, say you’re a grad student hoping for a career in academia. And say some of your role models or heroes are academics. That’s understandable, and probably fine; it could even be a really good thing for you. But it has drawbacks, and it may be something that you want to try to get over. If you think of someone as your role model or your hero, then in some sense you think of them as better than you. Which can be a problem because it can alter your behavior. For instance, if you feel like you’re inferior to someone (which is just the flip side of thinking that someone is better than you), it can make it hard to talk to them, or to talk to them without treating them with deference. And the surest way to get someone to get someone to treat you as an inferior rather than a peer is to act deferential towards them. Don’t get me wrong, I think it’s great to have role models. But paradoxically, I think the best way to model yourself after your role models is to treat them, and people like them, as your peers. You have to walk the walk and talk the talk, as the saying goes. You’re not magically going to start thinking of people with PhDs as your peers the instant you get a PhD, or magically start thinking of faculty as your peers the instant you get a faculty position. People become your peers–in your mind, and in theirs–when you treat them as your peers. Like Morpheus, Neo’s mentor in The Matrix said, “Don’t think you are, know you are”:

Not that I recommend actually trying to kick your mentor or role model in the head. In fact, I’m sure I speak for Meg and Brian as well when I say: please don’t kick me in the head.

Don’t get me wrong, I’m all in favor of learning from others. And I certainly don’t think you should go it alone or ignore what everyone else thinks and does! And I’m also well aware that you need to grow into seeing others as your peers, and having them see you as their peer. That’s where mentors come in, I think. Like everyone, I’ve had mentors in my career, and I’d never have gotten anywhere without them. Two important features of mentoring relationships in the sciences is that they’re mutually-agreed, and that (at least in the case of advisor-student relationships) they’re designed to end. Both the mentor and the person being mentored* agree to their roles, and (at least in advisor-student cases) agree that they’ll cease to fill those roles once the mentor decides that the person being mentored has achieved some agreed goal or standard (like graduating). This makes it much easier, I think, for the person being mentored to come to see their mentor–and by extension, others like their mentor–as their peer. In contrast, if you see someone as a role model or hero, it’s up to you to decide if or when you’ve achieved the standard that you think your role model is setting, or what to do about your inability to achieve the impossibly-high standard you think your hero is setting. Which might be totally fine–or might be setting you up for imposter syndrome.

And while I haven’t learned only from my mentors–far from it–I’ve learned from others whom I think of as peers, not role models or heroes. I don’t know that I really have anyone in science I consider to be a role model or a hero. Again, don’t get me wrong. I have huge respect and admiration for lots of people in science! And I often ask for advice from others who know more about something or have more experience than I do. But I still think of those people whom I respect, admire, and ask for advice as my peers. I don’t emulate or copy anyone–I’m my own person–and I don’t see anyone as being my superior. After all, people whom I hugely respect ask me for advice too! And I think and hope that they respect me just as I respect them.

Do the people you named know that you consider them peers, mentors, role models, or heroes? If not, how do you think they’d react if they did? Would it be a problem if they didn’t consider themselves to be what you consider them to be? For instance, I do a fair number of “advice” posts, and for that reason I’ve had more than one grad student tell me that they consider me to be a sort of surrogate advisor or mentor. Which I find tremendously flattering, but also kind of scary, much as Meg finds it flattering but scary to be considered a role model for how to be both an academic and a mom. I mean, yes, I am confident in the advice I’m giving, or else I wouldn’t give it. And I am glad that students find my advice useful and take it seriously. But it is just advice, not gospel. Further, it’s haphazard rather than systematic advice; I make no attempt to offer advice on everything students might want or need advice on. It’s not tailored to anyone’s specific situation; what’s worked for me won’t necessarily work for anyone else. And it’s no substitute for ongoing guidance from someone who’s agreed to mentor you. In short, unless you’re my student or I’m on your supervisory committee, I can’t really be your mentor. So while I’m sincerely pleased and flattered if you like the blog, and find the advice you read here useful, try not to think of me as a surrogate advisor. Because I’m not.**

And while, like Meg, I’d be very flattered if you took me as a role model in any way, be a little careful about that. By which I mean, be careful about the respects in which you try to emulate me (or anyone else in science). Much like Charles Barkley, I’m not paid to be a role model. I’m paid to wreak havoc on a basketball court publish papers in leading journals, get grants, and do the other sorts of things academics at research universities do. Don’t get me wrong, in many respects I’m sure I (like all of my colleagues) set a good example. I mean, it’s not like I act illegally or anything! But it’s not like I go out and consciously try to set a good example for others either. Perhaps some of my colleagues do, but I don’t. Plus, in at least some respects the example I set could well be considered a bad one rather than a good one, depending on your point of view! Just because I dunk a basketball publish in Nature doesn’t mean I should raise your kids always do things you should emulate, even if you are or want to be an academic ecologist. For instance, if you believe academic ecologists need to get more serious about doing policy-relevant research on climate change, then should should probably see me and people like me as hypocrites rather than role models. If you think academic scientists have a moral obligation to publish publicly-funded research in author-pays open access journals, then you should probably see me as whatever the opposite of a role model is. If you think arguments among scientists are mostly a bad thing, that they mostly arise from closemindedness and represent a breakdown of science and civility, then you should probably see me as part of the problem (and there are some people who do). Much of my blogging is aimed at a rather narrow audience, and if you’re not part of the intended audience reading it might even be counterproductive. Heck, I may not even know how I do what I do, which makes my example pretty much impossible to emulate (much as Meg says she feels stumped when asked what the secret is to being both a tenure track academic and a mom). So by all means have role models. But try to use their example as a means to help you become what you want to become, or just as living proof that it is (somehow!) possible for you to become what you want to become. As I’ve said before, academic ecologists actually are a pretty heterogeneous group in many ways; there’s more than one way to be one. Don’t feel like you have to emulate anyone, or that you have to emulate anyone in every respect. Choose your own path and walk it as best you can.

And while I’d be very surprised, and of course hugely flattered and humbled, if anyone thinks of me as a hero, I really hope no one does. I’m just a science prof with a blog. There are lots of people like me in all the ways that matter. Probably including you. So don’t think of me as “Dr. Famous”, someone you should hesitate to approach, or treat with deference, or could never measure up to, or whatever. And feel free to interrupt me if I’m eating lunch.

UPDATE: I’m actually a baseball fan, not a basketball fan; I only used the Charles Barkley commercial because it seemed both fun and relevant. Plus, I couldn’t think of a relevant baseball commercial. But I just thought of one! So here’s an illustration of the perils of trying to model yourself after others too much:

*The mentee? What’s the right word here? Not “trainee”. “Pupil” or “student” doesn’t seem quite right either. Teachers have pupils or students, and a mentor isn’t quite the same as a teacher…

**Don’t think of me as a guru or oracle either. Maybe that’s a fifth category I should have considered: scientific gurus and oracles. People whose advice you choose to follow unquestioningly (even if you don’t understand it), but whom you don’t try to emulate and with whom you don’t have a mentoring-type relationship. People you just treat as infallible sources of answers to whatever questions you might have. I definitely think you shouldn’t have any scientific gurus or oracles.

Answers to reader questions, part 3: what we’d say to Congress, tropical vs. temperate systems, and more

For previous posts in this series, go here.

If you had 5 minutes to stand up in front of Parliament/Congress and say whatever you wanted, what would you say? (from Margaret Kosmala)

Brian: Fund elementary and high school education in the sciences and math better. For all of our angst about disbelief in climate change, lack of concern on biodiversity loss, etc, this is basically an educational problem. Basic math and science literacy is our best friend. And its a good investment for the country too (in terms of jobs, wages, etc).

Jeremy: Tough question. Part of me wants to dodge it, on the grounds that nothing I or anyone says in 5 minutes in front of Congress or Parliament is ever going to matter squat. But I know that wasn’t the point of the question, so I won’t say that. Part of me would want to say something about macroeconomics. I know this is nothing to do with ecology, and I’m certainly not going to claim special expertise in economics. But personally, I think the biggest and most important public issue of the moment is the ongoing hangover from the financial crisis, the weak-to-downright-counterproductive policy response from national governments, and the resulting horrendous collateral damage to the lives of millions of people (and to funding for science and education at all levels). But part of me says that people like Paul Krugman can talk about macroeconomics much better than I can, so I should stick to talking about something on which I have some expertise and credibility. So if I was sticking to topics on which I have expertise, and I was talking to the Canadian Parliament, I’d tell them to reverse the current Canadian government’s systematic destruction of research and information-gathering capacity. Hacking away at the NSERC budget, closing the ELA, dropping the long form census…the list goes on and on.

What are the main obstacles to creating accurate mechanistic predictions in ecology? (from Konsta Happonen)

Brian: I just did a post on the mechanistic piece of predictions. Suffice it to say here, I think the main obstacle is too narrow a definition of mechanism. As for predictions in ecology, I think the main obstacle is cultural. “Discovering something new” is way more valued than “testing a theory” (which is what predictions really are). And then since most theory in ecology is what May called strategic theory (really simplified caricatures of reality), testing most of these theories tends to not result in making and testing predictions but rather in saying “yes this force is or is not going on”. Now strategic theory is a good thing, but in my opinion it should not be the only thing. You have to turn it into other more predictive kinds of theory eventually.

Jeremy: I don’t have much to say in response to this question, but I did want to comment briefly on Brian’s response. I actually think testing theoretical predictions is quite valued by leading journals and funding agencies. Our journals and grant applications are filled with tests of hypotheses! But yes, those predictions or hypotheses often are derived from strategic theory, so they’re typically not quantitative. Also: I had thought the distinction between “strategic” and “tactical” models was due to Richard Levins? Am I misremembering? Probably. They say the memory is the first thing to go…

Community ecology has something of a reputation, both within and outside the field, as being particularly susceptible to bandwagons and fads. Do you think that’s true? If so, why? Is it a bad thing? And if it’s bad, what, if anything, can be done about it? (from yours truly!)

Brian: I’m not sure I buy the basic premise. Other branches of ecology have fads too. Eddy towers to measure CO2 flux in ecosystem ecology cost a lot more than community ecology bandwagons and yet, when I talk to people in the field, it is clear that there are some real limitations and lack of creativity around this approach. Climate change is a fad sweeping through all fields (just saw a paper showing the evolution of a gene polymorphism that got published in PNAS this week  because it had links to climate change). And evolutionary ecology is very fad driven. Even physiological ecology and population ecology have fads. I seem to recall Jeremy wrote a post about how the r/K formulation of the logistic equation is worse than the r/alpha formulation, yet we’ve used the r/K formulation for decades – that’s a bandwagon. And over in evolution they certainly have their band wagons (phlogenetics, -omics, etc). Groups of humans are all that are needed to have bandwagons. (Jeremy adds: just to clarify, the post on r/K selection that Brian refers to is here. Brian’s slightly misremembering it; I didn’t argue that one formulation of the logistic is better than another. But I don’t think this undermines Brian’s point.)

How do you justify the cost of research without vague promises of future applied/conservation value? (from BEC)

Brian: This really unpacks to two separate questions:
1) How do you play the game to get the grant
2) Morally how to you justify to yourself spending money on a topic
For #1 it depends totally on the funding agency. For NSF you do pretty much exactly as you say – vague promises of future conservation value are what NSF wants – too much emphasis on the applied side and you won’t get funded. I will say though as a reviewer for NSF that I tend to give higher scores for broader impacts when the social infrastructure is credible (i.e. you have list of names and organizations that you have already worked with vs a vague promise to publish it or share the data with the forest service). For USDA grants things have to be much more concretely applied with obvious tangible benefits
For #2 – Basic research has shown over and over again to pay off, so I don’t think there is an inherent need to justify basic research. But I do think there is a pendulum swing going on where society is less and less willing to fund basic research without obvious links. I think this will be an increasing trend to demand more real, tangible relevance in the future. Personally, I am happy with that. And in the mean time, its really a personal decision, and I’m pretty happy with that.

Jeremy: I have various old posts on this (see here, here, and here). Of course, those posts are about justifying the value of fundamental research in general, not about how to justify some specific piece of fundamental research in a grant proposal. When it comes to specific grant proposals, I think you either tell the funding agency what they want to hear, or else you just say what you really want to say and live with the consequences. And if you don’t like either of those alternatives, well, you’d better find a question and study system that lets you kill two birds with one stone. By which I mean a study system that’s a good model system for asking fundamental questions, but which is also a system in which the answers to those questions are of direct applied relevance. There are such systems. For instance, think of the work of Bill Murdoch and colleagues on California red scale. Great fundamental population ecology–but also of direct applied relevance because red scale is an important agricultural pest on citrus trees. Indeed, such systems arguably aren’t even that rare. I think there are a fair number of applied problems out there that are really fundamental problems but haven’t been widely recognized as such. For instance, at last year’s ESA there were multiple talks from really good fundamental ecologists (e.g., Jon Shurin, Val Smith) working on algal biofuels. Most people working on algal biofuels come from non-ecological backgrounds and can’t see basic ecological problems that are totally obvious to an ecologist. This sort of blindness to fundamental issues on the part of many narrowly-focused applied researchers creates an opportunity for you as a fundamental researcher when you approach funding agencies, because your proposal often will look really novel and groundbreaking. Using trophic cascades to control zooplankton grazing in algal biofuel reactors–what a novel idea! Cancer is an evolutionary problem–what a novel idea! Etc. I suspect that in future an increasing amount of fundamental research will be such “dual use” research, if only because funding agencies are indeed (and unfortunately) under increasing pressure to support research with direct applications to problems we already know we want to solve.

Why don’t ecology journals accept submissions in TeX? (from Margaret Kosmala)

Brian: They should! One of the most insidious aspects of the technology revolution is that journals have increasingly pushed the production process onto the authors. We are now responsible for the production quality of the figures. For most of the copy editing. And, while you would think this trend would lead to journals forcing authors to typeset with TeX, ecologists as a group would revolt, so journals do the next most lazy (more importantly cheap) thing – demand that everything be done in Word so that they can offshore the typesetting to India to somebody with 2 weeks of training who can run some macros to import into their typesetting system (India is in fact where most typesetting of academic – especially for profit – journals happens these days).

Jeremy: What Brian said. I don’t know TeX and am disinclined to learn; I have much better things to do with my time. And I’m also annoyed with having the production process pushed off onto me as an author. So while I’d have no problem if journals wanted to accept submissions in TeX, I’d be really annoyed if they required it. So I suppose the answer to your question is “the attitude of people like me!” But cheer up–I’ll retire eventually!

Are there qualitative differences between tropical and temperate systems? (from calimans)

Brian: Yes, but we don’t know what they are! Not a very satisfying answer, but after 15 years studying global ecology, its the most honest one I can give. The diversity is so markedly different in the tropics that we have to say there are qualitative differences (and diversity increases faster in the tropics than productivity, temperature or any reasonable explanatory variable). The extent to which this diversity is a cause or an effect of the differences can be debated (the answer is both). But there is no decisive evidence of what causes these differences.

Jeremy: No. Tropical and temperate organisms all evolve via mutation, selection, migration, and drift. New tropical and temperate species arise only via speciation from existing species. Their abundances change via births, deaths, immigration, and emigration. Etc. Plus, there’s no sharp division between the tropics and the temperate zone; it’s a continuous gradient (perhaps a nonlinear one, but a nonlinear gradient is still a gradient). So I’d say the tropics are merely quantitatively different, not qualitatively. But I suspect I’m just interpreting “qualitative” differently than Brian, and that Brian and I don’t actually disagree substantively.

In many ways, I think that the question of whether the tropics are “qualitatively” or “quantitatively” different than temperate regions is like lots of long-debated questions about whether some obvious or large difference is “qualitative” or “quantitative”. I think such debates usually are either uninteresting semantic debates, or else they’re really about something else. For instance, consider the question of whether humans are qualitatively or quantitatively different from other species. It seems to me that the really important–and really obvious!–thing is that humans are very different in many ways from other species! And it seems to me that we can describe, quantify, and work out the implications of those differences without ever deciding whether they’re “really” qualitative or “really” quantitative differences. So if you insist on setting that hugely important and hugely obvious fact to one side in order to focus on whether those differences are “qualitative” or “quantitative”, I have to wonder why you insist on doing that. Is it because you just care a lot about semantics? Or is it because you’re actually trying to bolster your position on some other substantive issue, like a particular religious view of humanity’s place in the universe, or a particular view on the ethics of animal research, or whatever? I’d say much the same about the tropics vs. temperate question. Why does it matter whether we say the differences are “qualitative” or “quantitative”?

What advice do you wish you’d been given as a grad student? (from sjd)

Brian: #1) Think consciously about time-management skills. They are important in grad-school. They are critical to survival as you move on. But they are a learned skill, not something we innately know how to do.
#2) Ask for help. Science is fundamentally a social enterprise. And nearly everything one does after a PhD has elements of collaboration. Yet somehow we expect PhD students to go it alone. its not good training for their future. And much of the “right way to do things” is really just a social convention. As a grad student the most go-to group for help is probably your peers. Especially the ones a year or two ahead of you. Ask them lots of questions. And most advisors are alot more amenable to request for help than graduate students think.
#2a) When you’re in your last two years or so, be bold about reaching out to big names in your field outside your university. Email them and invite them to come to your presentations at conferences. Go up and talk to them. You’d be surprised how often this turns into an invitation to tag along for lunch and then a postdoc.
#3) Follow your heart scientifically – it is easy to get caught up in the stress for jobs, but in the end the answer is good science, and good science comes from following your heart (and its more fun along the way). Its not a guaranteed solution, but I believe it is the best path.

Jeremy: All the advice I wish I’d gotten as a grad student, I got.

What piece of literature (scientific or not) has been most influential in shaping your view of the world? (from sjd)

Brian: A very broad question! And I always freeze at pick your favorite X type of questions – there are so many choices! At the broadest level – I would have to say the science fiction writer Robert Heinlein’s work that I read as a teenager. He’s way too sexist and libertarian for me now (I haven’t let my kids read his stuff), but his scientific/technological optimism certainly pervades my world view as a citizen and a scientist. Within science, I guess I would have to say two books: Rosenzweig’s Species Diversity in Space and Time (1995), and Brown’s Macroecology (1995; I started grad school in 1997) were what defined the direction I have gone as a scientist. They created a space and gave permission to pursue macrecological questions that hadn’t existed before those books came out. As to how science should be (and is) done, I’ve frequently cited Lakatos in this blog (his lecture on pseudoscience and the demarcation problem is a good entry point).

Jeremy: I have an old post on this.