What productive academic work can you do with minimal attention in a small (<30 minutes) block of time?

Question

Ok, here is a major issue I have with much of my work (which is experimental/computational research): I am a graduate student, and spend much of my day doing 5, 10, 20 and 30 minute experiments (both biological and computational), which require set up and monitoring, but result in small to medium size hunks of time when I'm really not doing much (essentially just checking every 1-2 minutes to make sure everything is still working). During these periods of time I either a) attempt to do other work or b) procrastinate. Both are not great, since either a) I'm constantly shifting my focus from the latter project and end up making mistakes in it or b) I'm procrastinate (usually by reading articles, twitter, etc.)

Does anyone have advice for how to deal with these small, awkward period of times (< 30 minutes) - is there something which you find useful to do that you can also shift your focus from incessantly?

As an example: http://xkcd.com/303/

Answer 1

ff524's answer is awesome as usual, but the core problem for you may be that most of these suggestions are not, or at least not directly, useful to your research. If, as you say, most of your day is spent in this way, even "productive procrastination" may be too much procrastination and too little actual progress.

In that case, you have two options:

1. Learn how to get actual work done in those short chunks. Being able to context-switch without getting thrown off completely is definitely a skill that can be learned. You will probably never get as efficient as when you can devote your full attention to the task, and you will likely need to double-check what you did while multi-tasking, but getting things done slower than usual is much better than not getting anything done at all.
2. Automate better (and, hence, increase the time between needing to check up on your results). In my experience, if you need to actually check every other minute or so what your experiments are doing, then your tooling is not good enough. Many things can be scripted so that they basically run from beginning to end on their own. Further, you can configure a system monitoring tool so that it (for instance) sends you an email when something abnormal happens. Of course this requires non-trivial IT skills, but in my experience most students working in experimental sciences are able to grok these things quite quickly if they devote a few days to it (believe me, the time necessary to learn how to automate pays off manifold in the long run).

In practice, you probably want to go for a combination of both of these options. Try to increase the time your experiments are chugging along on their own. In parallel, train making the best use out of this time.

Edit: This question has been added by the OP in a comment. I think it is interesting, hence I added it to my answer:

what can you do in the case of active code development? For example, when I am actively developing a piece of code to analyze data, that code may take a few minutes (up to 10) to run, after which I assess if it is working. How do you automate that process?

This has actually more to do with standard software engineering practices than the sciences, but I think it is a helpful concept nonetheless. When you are trying out different implementations, with a large possibility of error, make sure that your application fails fast. That is, make it so that your application does not take ten minutes to fail, but does the complex, error-prone stuff directly in the beginning. Two simple examples from my own research:

Example 1: Say you do research in Machine Learning. Your application first trains an artificial neural network (ANN) on your data (easy as you are using an external library, but takes ~15 minutes due to algorithmic complexity), after which you do some postprocessing (trivial, executes fast) and a statistical analysis of the results (executes fast as well, but relatively complex, error-prone code). If you now always run the entire application and have the code fail during the statistical analysis, you are always losing 15 minutes for every run for a step that you already know works. A better solution would be to train the model once, and store it to disk. Then write code that only loads the ANN from disk and fails directly after that. Almost no dead time anymore. When the statistical analysis is working, you can revert to do everything in the expected order.

Example 2: You have written a complex, multi-threaded testbed, which is running distributed over multiple physical servers. You know you have a synchronization issue somewhere, as your application non-deterministically dies every couple of minutes. You have no idea where exactly. Hence, you repeatedly execute the entire application, wait for the error to happen, and then debug from there in different directions. Given that the error only happens every few minutes, you spend most of your time waiting. A better way is to take a page out of good software engineering practices. Make sure to unit test all components in isolation before throwing everything together. Specifically try to cover exceptional cases. Learn how to write good mock objects. Some amount of debugging of the integration system will still be necessary, but you will not spend hours debugging components that are fundamentally broken.

Answer 2

Given that you can't do something that requires your full attention during this time, I would at least advise to follow Matt Might's advice to procrastinate productively on meta-work:

• Read articles (or answers on Academia.SE!) about how to be more productive as a grad student.
• Read a few pages of a book on data analysis, visualization, academic writing, or some other research skill.
• Use the time to review your schedule, make to-do lists, schedule a meeting, etc.
• Try a relevant Coursera course that has video lectures broken into small bits
• Put some thought into a tiny bite-sized aspect of your research or research presentation: How should I organize this manuscript? What is the best way to visualize this data?
• Unfocused literature search: check in on your favorite journals or conferences and identify interesting papers (to read later, when you can give them your full attention)
• Do a favor for someone else: Even if you're not teaching or grading this semester, I'm sure someone in your department is, and would be happy to give you a pile of quizzes to grade.
• Update your CV and/or web page.
• Start preparing a presentation on your current work. You'll present it to someone, somewhere sooner or later, right?
• Think about your ultimate career goals.
• Use the Internet to look for promising potential collaborators at other institutions.
• Try out a new programming trick, LaTeX package, or software tool.

Answer 3

A great deal of things we have already learnt are slowly being lost by our brains. Small chunks of time like this are ideal for a bit of memory consolidation. I would suggest a quick revision session on things you want to know better.

It's best to select knowledge that will be most useful if remembered. Ignore any knowledge that is easier to just look up on Google (e.g. 5 Biggest Cities) and focus on knowledge that will help in your day-to-day (e.g. Chrome Keyboard Shortcuts).

How to create a summary of key information for learning

• Keep the 'bits' of information small and 'easily learnable'
• Write a dot-point summary of a topic you wish to understand better
• Highlight favourite passages on your Kindle then download them from kindle.com
• Look up book summaries from websites like getabstract.com
• Use Evernote to collect information you wish to learn in notebooks (their Web Clipper is awesome)

Once you have a summary

• Use small chunks of time to review the material
  • Don't spend too long or your brain will switch off
• Use Spaced Repetition methods
  • Material you find hard to learn should be repeated more often
  • Material you find easy should be repeated less
  • You need to wait between sessions to allow it to sink in

Good luck!

Disclaimer/Self-Promotion:

I am the creator of www.revisy.com - a tool that helps automate all of these steps.

Answer 4

I'd like to add a point of view from experimental science. I do spectroscopy of biological samples and I have experiments like that as well.

Abstract:

• do not underestimate how strenuous experiments can be: if done well they often require you to keep up a high level of concentration. This is exhausting, and particularly if awkward timing is involved.
• Decide your priorities: do experiments only or start an experiment whenever that fits your other work schedule.

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My experience is that there are some people who cope with such situations relatively calmly: they vanish into the lab, do their experiments, don't go into fits because of the lost time, and reappear after the experiments are done. Other people (e.g. I) absolutely hate the situation, because tons of other stuff needs to be done, and don't get done while you are there waiting for the experiments in minute time chunks that don't relly let you do anything.

Personally, I found 2 feasible ways of dealing with these situations, and the big idea is to decide beforehand where the priority is.

• Experiments have high priority: In that case, I consider the time and concentration being reserved for the experiments. Any other things that happen to get done are a surplus. I find that most items from @ff's list shift my focus too much. Which in the end leads to chaos in the experimental data (e.g. slightly differing names of files which require manual adjusting afterwards etc.) But setting up the measurement schedule for the next experiment, getting the notes and the samples ready for the next experiment, etc. is OK. (Though many of my experiments are in the dark, so preparations to be done in the same lab are not really feasible). Other than that, I do things like checking (e)mail, schedule meetings, preparing TODO lists and working off tiny tasks. If the waiting time is more like half an hour I may clean up my desk/the lab, but e.g.
  I don't like reading papers or doing a literature search on an alarm-clock schedule. Such work I do only when

• Experiments have lower priority: I often have experiments where I need to change samples after measurements are finished after, say 30 min, but nothing really bad happens if I change the sample 30 min or 1 h later. In that case, I often decide to run the experiments on a lower priority, while reading or writing papers, doing literature search, etc. So I do something, and when a natural break occurs after a chunk of work is done, I go and change samples before I start the next chunk of work.
  This way, I get maybe only a third or a quarter of the experiments done compared with the "high priority" experiment situation, but other work is possible as well.

All in all, the first scenario is the more emotionally exhausting the more you dislike the breaks and fret about all the work that doesn't get done. It is the more feasible the better you can arrive also mentally in a state where you concentrate on the experiment only. Experiments are serious and exhausting work as well. One reason is that you have to be far more concentrated than at the computer*: If I make a typo, there's backspace. If I type an awkward sentence I can change it later. If I pipette wrong, I have to start from the beginning or even take out everything and clean my instrument. And this is the more strenuous if awkward schedules are involved. And weird little waiting times are in my experience as exhausting as an experimental schedule you can barely keep up with.
* most times: obviously, data analyses need to be set up correctly. But even there, e.g. literate programming allows to double-check later that everything was done correctly. This is not possible for the experiments - there I have to be concentrated so I'm sure afterwards that I did everything correctly even though I have at most very limited possibilties for double checking.

If you have to do your experiments in that way, then the best way to deal with it is to also mentally accept this and make it your task. Make sure that the experiments really stay top priority if you decided that way: don't read SX if you realize that this in fact leads to the experiment waiting for you when you decided that you should wait for the experiment.

The second way is of course only possible if if the experiments allow this. And if the lab has low enough use so your colleagues don't kill you for not making fullest use of the vaulable lab time you reserved.

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In terms of automatization of (biological) lab experiments, I for example print out "experiment forms" that have a predefined structure for my note taking so don't forget any of the parameters. This helps me to make fewer mistakes.

In some cases I even write custom measurement programs, but this effort is usually only worth while if you know that large series of experiments follow (for totally non-scientific and awkward reasons: the instrument software often allows only very restricted programmed interaction, and if there are not many experiments following, it is not worth while to go down to the low-level control the SDK offers - if there is an SDK available at all).

Answer 5

If you are

• a) Constantly shifting your focus from the latter project you overcharge your brain with additional tasks in the moment and possibly what your brain really needs from you is some rest, that's why you end up making mistakes because by the time you get to the lab projects your brain is already preoccupied with new tasks and have used up a lot of memory doing new operational memory tasks
• b) If you procrastinate by reading NY Times and Twitter you are also overloading your brain with new information that requires some analytical work(although it is of course incommensurate with the amount of work you need for academic journals, but precisely because the Times loves using such words as incommensurate on its pages you have to constantly query your memory and thus, keep your brain busy), so here we are, getting back again to the same problem-you are using your operational memory, the brain gets tired and by the time you get back to the lab work it is nearly impossible to focus.

• c) I hate to suggest that...but...is there a possibility of a short nap or just sitting quietly in your office and not exposing your brain to any additional flow of information? Plus, Twitter, email checking, Facebook, are all good examples of instant stimulation but that's a topic for another conversation.

• d) In a nutshell, trivial things like smells, sounds from the street, someone's voice etc. are all examples of additional information that you are exposing yourself to that inadvertently bombard our brains on a daily basis and they make it even harder to focus and get back to the actual projects.