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As an undergrad, I'm just starting to get a feel for the academic world. Obviously, the ability to communicate well in a collaborative relationship is extremely important. As a fledgling STEM experimentalist, I'm beginning to learn like one.

However, can someone educate me on why there can sometimes be a communications barrier between researchers of different types (e.g., experimentalist versus computational versus theoretical researchers)? What types of obstacles cause these communication barriers? What are some examples of common sticking points?

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I'm a computational chemist. I've worked with many experimental collaborators with wildly varying experiences. Barriers certainly can and most definitely exist. Here are some of the major ones.

  1. Lack of understanding: This is perhaps the most common and basic reason and results from a lack of experience or knowledge in experimental techniques by computationalists and vice versa, quite simply because they were not trained in this area. As a computationalist, it is natural that I may misunderstand how certain experimental techniques work (especially the state of the art) because I do not spend most of my time working on it. This can result in me over-trusting a piece of experimental evidence, or underestimating the time and effort it can take to conduct the experiments. And believe me, this can definitely happen the other way round. One example, an experimental collaborator asked for some 'quick calculations' in a month and I had to point out that it would easily take a year and all of our computational budget to complete it.
  2. Politics: In a collaboration, there is always the question of who is the major contributor. Yes, it is possible to have multiple corresponding and first authors, but it rarely changes this fact. In my field (chemistry), usually the experimental side is the major participant of a collaborative work. This means a computational PI will benefit significantly less, and his student will usually not be a first author. This generally leads to conflicts regarding contribution of time and resources given the unequal recognition of work. Sometimes, in order to solve this issue, particularly for long-term collaborations, the experimentalist and computationalist can 'take turns' directing the project and claiming the major contribution to a paper. (I am not supporting or criticizing this phenomenon, but it definitely exists in the field.)
  3. Geographic/temporal reasons: Quite simply, many if not most collaborations occur between groups in different universities and different countries. The simple fact is you cannot communicate them like you would with your group members in the same room. This results in the need to schedule meetings and email communications which are often difficult (professors are busy!) and are invariably an inefficient way of transferring information. I've has cases where several weeks or even months of work were invalidated/or made irrelevant because I was only informed of new experimental findings many months later in a meeting.
  4. Different objectives/expectations: There is some overlap with the political reason, but sometimes it can be purely due to academic reasons. It can be common for experimental and computational groups to have different expectations for what knowledge/results a work is supposed to yield. For example, a study can be trivially easy for a computationally group (and possibly uninteresting) to accomplish (via modelling), but can be extremely difficult for an experimental group to synthesize/characterize. Alternatively, a relatively simple reaction for an experimental group to conduct can be a multi-year effort by several researchers (or it can even be unsolvable/impractical by current techniques!).

Is it possible to overcome these barriers? Certainly. I've had collaborators who are well aware of these issues (from experience), and end up being very enjoyable to work with. As an aspiring researcher, the best thing you can do is keep these issues in mind when collaborating with other groups. If possible, try to learn the techniques which your collaborators are currently using, and maintain a steady stream of communication with whoever your direct counterpart is (usually, if you are a student, keep in contact with the corresponding student in the other group instead of the professor directly - though you can/should CC him and your advisor to keep them in the loop).

Edit: I decided to add some examples of hypothetical collaborators from the perspective of a computational researcher (for fun). Any resemblance to actual persons, living or dead, or actual events is purely coincidental.

The Good: The well-informed and reasonable collaborator. Knows their stuff (even on your side), up to date on literature, knows what is practical and not. Knows if you are BSing them or not. Won't do you wrong if you do your job. Keep them well-fed with updates. Expect to do good science with them.

The Bad: The villain/antagonist. Only works with you because you are part of the same grant or project, and only sees you as a way of getting higher impact factor. Only interested in positive results, gives unreasonable deadlines, does not like to hear the phrase "but that can't be done!' Never satisfied, keep him well-fed, but for your own survival.

The Ugly: Nice guy, easy to work with. Okay with whatever you have. Takes anything you offer, but not terribly interested in the science behind the results. Likes you because you can help him get higher impact factor journals. Expect him to disappear one day when he has found someone better or no longer needs your help.

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The main source of the "friction" in talks among computational, theoretical, and experimental researchers is language. One group may use the term very differently from another.

For instance, I can say that I "calculate" a given quantity using a simulation. To me, this implies that I have performed a simulation, and know that the results that I get have a certain amount of inherent uncertainty because of the intrinsic variability of the particles I'm studying and that a slightly different starting point will lead to a very different result. However, all of that nuance may be lost on a theoretician or experimentalist who hears "calculate" and thinks I've just crunched some numbers and have a "one size fits all" answer to the problem.

However, the problem is not merely among the different groups, but also within them. Even different computational scientists can have difficulty understanding one another without some "translation." For instance, I can remember my two computational advisors arguing over a point for a while before we all realized that they were talking about exactly the same thing. The only difference was that one was using the "language" of control systems, and the other was using the "language" of materials science to describe the same phenomenon.

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  • "The only difference was that one was using the "language" of control systems..." It seems like you just discovered the zeroth law of control systems engineering: in the language of control systems theory, everything is the same as everything else! – alephzero Sep 13 '17 at 7:57
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I'm a computational epidemiologist, and so work fairly heavy with both "experimentalists" in the form of either observational epidemiology or clinical trials, computational folks, and more theoretical mathematical biology researchers.

Generally, I've found this collaborative research to be quite successful, but there are some difficulties:

  1. Difficulty understanding scope. Often, I find clinical colleagues end up wanting to add lots of detail to computational models that vastly increases the difficulty in implementation without really understanding what that means. Equally, my more theoretical colleagues don't have a good grasp of the nuances of things like IRBs, how long data takes to collect, etc. and vastly underestimate how hard and expensive it is to actually collect data.
  2. Competing pressures and priorities. Is 15% effort on a grant a ton of support, or barely covering your time? What's needed for tenure and promotion? What's a "good journal"? Do the theory people get out a bunch of papers early, while the experimentalists sit on their hands waiting for data.
  3. Difficulty understanding what's interesting. For example, there may be an intensely important clinical questions on really unique data sets that actually use pretty mundane methods from a computational/theoretical perspective.
  4. Different tendencies in different fields. Do you use LaTeX? How familiar is everyone with programming? Are papers the currency of choice, or presentations? What even is publishable?
  5. Different Incentive Structures. Are the theoreticians penalized for having papers with multiple co-authors? Are you evaluated based on student outcomes on grants, etc.?
  6. Timing and communicating. "What data do you need?" "I don't know, what data do you have?" is probably the most common conversation I end up having.
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    I think this is probably the most relevant answer to my work. Cultural differences like these are far more important than I first imagined, and I think to first order, most conflicts should be written up to this, rather than a collaborator being "good" or "bad." – AJK Sep 13 '17 at 17:53
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I'm more familiar with this sort of collaboration in industry than academia, but a common cause of problems is simply ego and arrogance.

When the results of theory and experiment disagree, you might characterize as "good" collaborator as someone who assumes that his/her work is wrong and everyone else's is right. "Poor" collaborators do the opposite, of course - and in general, there are more bad than good ones around.

If everyone involved looks hard enough for the mistakes in their own work, the apparently different results usually fit together somehow. If everyone's primary objective is to defend their own work, this often never happens.

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    +1 for highlighting personal attitude in the problems for collaboration – llrs Sep 13 '17 at 12:24
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I think you can look to physics for an answer (caveat - I am not a physicist). From what I've gathered, the disconnect between theoretical and experimental physicists is quite stark. As an outsider, it appears that the experimental scientists get to build and play with big, expensive, dangerous toys and get a lot of the glory. Meanwhile, theoretical physicists do much of the office-based ground work that fuels the experiments. So the experimental researchers argue 'its all theory until you prove it,' while the theoretical researchers say 'yea, but where would you be without us?' Secretly, the experimentalists wish they'd formulated the theories and the theorists wish they could play with big ray guns.

It all boils down to who gets the lions share of the credit - the experimentalists that carried out the experiment or the theorists that laid out the plans? These kinds of arguments are common in many scientific arenas.

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    I am a physicist and what you say I used to think as a student and postdoc. But, now I'm aware of the divide and I'm trying, with patience and whatever resources I have, to bridge the gap. It's not a linear process and it's quite complicated mixing psychology and physics, but people I've seen succeeding end up harnessing interdisciplinary groups and doing research where they both get to play with the ray gun and know how and why it works. – user21264 Sep 13 '17 at 6:10
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    " they both get to play with the ray gun and know how and why it works." - but don't forget that it often takes a really smart guy to ask the most perceptive and useful "really dumb questions" that unblock the log jam! I worked with an old Prof at a French university who would say nothing at all for literally an hour in a meeting, and then announce in broken English "Well, I know nossingz at all about zees topic. BUT... " and you just knew that what followed the "But..." was going to demolish most of the muddle headed thinking that had been going on during that past hour! – alephzero Sep 13 '17 at 8:03

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