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The title to this question is the best way I can think offhand of phrasing it - but to anyone with a more widely accepted phrasing, please feel free to edit it.

Basically it's about a common experience in research where we have had all the requisite facts for some time (in some cases for years) but nonetheless miss viable conclusions that could yield opportunities to advance our work. As frustrating as the usual struggles of research are, the self-reproach felt after finally realizing something we could easily have obtained earlier is so much worse.

After one such occasion in my life, I had to ask myself hard questions about my research methodologies. I wondered if I had been unwise to ignore other research students' practice of keeping an index-card file. The index-card system was not simply a memory aid - it also allowed one to make multiple cross-associations between items of interest. Although perhaps 95% of the effort of maintaining an index-card system for one's research was in vain, the final 5% effort could save one's blushes.

More recently, reflecting on a research topic from earlier in my life, I came to a superficially counterintuitive but rationally interesting conclusion after putting 4 long-known facts together. Putting aside the suggestion of neglectful supervision of my research - I know few PhDs with genuinely adequate supervisors let alone ones who would constructively criticize the researcher - I have to wonder what would have been if newbee PhD candidates were first taught some means of collating their research facts and logically/sensibly constructing conclusions or exclusions from these.

May I ask PhD researchers here if today's academia provides any 'bootcamp' or practical methodologies for research reasoning prior to formally beginning their work ? The methodologies I have in mind might be things like:

  • Writing out facts as premisses and trying to link them logically together
  • Sketching out concept associations or 'mind-maps' of facts or ideas
  • Speaking aloud some conclusions in the hope that, on hearing them, implications may be stimulated
  • Dialogues on blocking issues with a knowledgable colleague
  • Dialogues with common-sense people without expertise in one's research domain
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    Don't neglect non-technical things like taking breaks, getting enough sleep, exercise, and such. Insights often come when you don't force the mind to work with detail.
    – Buffy
    Jan 19 at 14:11
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    Not at all. It is based on learning/brain research.
    – Buffy
    Jan 19 at 14:32
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    Talking with knowledgable people. It's easy to missing something big or small when just mulling over alone. Also, without being an expert on the philosophy of science, "putting together simple facts to arrive at conclusions" sounds very much like the Baconian approach to science which has been abandoned in favor of more hypothesis-driven approaches.
    – cheersmate
    Jan 19 at 15:30
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    "... pick lice ... research rivals ..." - Collaboration is an immensely important part of research. If you don't trust (any?) other people enough to discuss ideas or even the state of the art with them, you're probably not gonna have a good time. Of course there are rivals, but you can't rule out everyone or you'll never have insightful discussions and miss out on a lot of ideas. If the standard approaches fail, discussions with people from outside the field might help.
    – cheersmate
    Jan 19 at 15:47
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    Which general area is your research? Physics vs sociology for example, which probably have very different approaches.
    – puppetsock
    Jan 19 at 17:11
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That sherlock holmes trope where discoveries are made through some dispassionate application of pure crystalline logic is nonsense. If you want to do that you should be a protagonist in a fiction novel.

Turning disconnected facts into knowledge is creativity. If there was a universal tool for creativity we would all use it.

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    I don't know what your field is. But if your own research is all about creativity then you are in a very fortunate situation. For most of the rest of us, our research is mostly monotonous though necessary data-gathering, reading the work of others and validating/invalidating concepts hypothesized on the phenomena involved. Creativity - if we encounter it - is the final result of our efforts, not an ongoing endeavor.
    – Trunk
    Jan 25 at 13:32
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It sounds like you're in the "frustrated" stage of research. And you're asking if there's some kind of surefire algorithm for gaining insight. I think it's fairly obvious that there is not, otherwise it would be used and those using it would have a crushing competitive advantage in almost anything they do.

That said, I think we can do a bit better than just saying "be creative". Creativity can be helped along. You've actually already mentioned some of them.

  • Teach what you know. The act of putting together a lesson that others will understand, requires you to structure your raw disconnected knowledge. Teaching can be teaching a class, writing a tutorial for novice users in your field, or a popular science blog explaining what you're doing to lay people.

  • Present your work to colleagues who are somewhat into your field. Or even other people in your institution who aren't exactly in your field, but smart enough to ask good questions. Let them ask questions about stuff that seems fuzzy. Even poke holes into your ideas. And return the favor for their work. This doesn't have to be a mammoth undertaking - lunch seminars are a good venue for this.

  • Teach about your work to children. This forces you to come up with examples and analogies to explain what you're doing that are completely different from what you would normally use for educated adults. (Side benefit: you'll be prepared next time you have to explain what exactly you do during a family party.)

  • Get a big blackboard, or a notebook, or mindmap or whatever. Not every tool inspires everyone. Try out stuff to see if it inspires you. Don't feel childish for enjoying a particular style notepad or whatnot.

  • Apply your work in smaller projects and prototypes. Build something that uses some of the techniques and knowledge from your research, but that's free-standing from your core experiments. Seeing the principles in action may spark new ideas.

  • Get enough rest. Go easy on the caffeine. An actual rested, relaxed mind has got something going for it.

  • Have some hobbies outside of research. Sports or something physical like going for works. Only trying to focus on research can just make you more frustrated and blocked. Taking a step back and coming back with a fresh view later on can help.

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  • Thanks for your list. Yet what was overlooked here was something known and rationally connectable to the primary research drive. I can envisage someone doing all the above healthy practices, then coming back to their lab and continuing to fail to question a foundation assumption in this research effort. Widely shared assumptions are much harder to challenge. And all researchers tend to to get bogged down in experimental problems (moreover after mini-triumphs in overcoming them!) only to lose sight of the main goal of a research programme - which is always technique indifferent.
    – Trunk
    Feb 8 at 15:57
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After looking around, it seems that the best tool to assemble research facts logically would be an expert system using a language like Prolog. This would allow facts to be entered and queries to be made so as to make deductions from them.

Care would have to be taken in how facts are entered into the system. Primary or "core" facts, i.e. basic tenets of the research programme, would have to be classified differently to subsidiary and extraneous facts. So one would have to structure facts so their level of importance (i.e. their advance from the primary objective towards it realization) is recorded: this might also allow a fact hierarchy view to be constructed to help the researcher form overview and inviews of the project to that point. All queries would have to reference the importance level related to the query - otherwise one would have the classic researcher's problem of getting bogged down in details that may be irrelevant to the project's ultimate objective.

The expert system must have a capacity to amend an erroneous or incomplete "fact" (e.g. an assumption based on a common understanding of some phenomenon) whenever a new fact combines with others to bring it into question. Core facts would naturally be as contradictable and correctable as subsidiary facts. The impact of such corrections may have dramatic effects on the fact hierarchy and may indicate a whole new direction for the project.

But the problem here is the same as that without such a resource. If we consciously knew the assumptions we (and all others in that field of investigation) were making, we would already be half-way towards making a serious leap forward.

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