text:
Are overlapping dissertations ethically acceptable?
Would it be ethical for students working on the same team to include in their Ph.D. theses results arising from joint publications, ending up in different dissertations containing almost identical chapters?
Of course, the students should at least mention that the common chapters are excerpted from a joint publication. However, wouldn't a significant overlap at least indicate that the students have been unable to come up with enough strong and coherent results to make their own independent dissertations?

Hi user4423, and welcome. We're looking for a single question with an objective answer. Which of your two distinct questions would you like us to answer? The one starting "Would it be ethical ... " (likely to be closed as not constructive) or the one starting "wouldn't a significant overlap indicate ..." (to which "It depends. Not necessarily" is the meaningful and correct answer)?

Hi, thanks for the feedback. Sorry for the confusion: There is actually just one question, which is the one given in the title. The two subquestions are intended to give more details about the general issue of whether it is considered appropriate to publish the very same results in two different theses. I found the answers received so far to be quite useful.

I think for a PhD thesis it is important the writer has enough original and new contributions to earn the PhD degree. When two PhD's work closely together, and write joint publications this can only work if it is clear that both PhD's have made new and significant contributions.
For example, in a publication which has both lab experiments and numerical modeling it is easy to see that both the lab-PhD and the numerical-PhD have done different things, which are put together jointly into a publication. In this case I would think it ethical that both PhD's get their degree based on the same publications. If, however, the overlap is not countered by the fact that the PhD's each have their distinct niche, I would not find it ethical to let two people get their degree based on the same work.
In the PhD theses the publications could be used as such by both PhD, but they need to have a different introduction and synthesis chapter as they worked on different aspects of the joint papers. In addition, I would explain the situation and how the collaboration worked in the preface of both theses.

Sounds very reasonable.

You should check your department guideline. In mine, only articles where the candidate is first author, and has accomplished most of the work (you need to provide letter signed by other authors) can be used. Also, it can only be used in one thesis.

Thanks for the note, this makes total sense. Unfortunately, the issue is not mentioned in my department guidelines.

Then I would discuss this with your supervisor, maybe even someone higher up in the department to get clear how your university deals with this, just to prevent any trouble.

One case of this that's getting a bit old but was highly influential in artificial intelligence was Phil Agre and David Chapman (PhDs at the MIT AI Lab in the late 1980s).  They did everything together, but wrote completely different dissertations.  They agreed in advance how they would divvy up the output.
Since a PhD has to have a novel contribution, I think this is the only way it can work. You specify your contributions in the introduction and conclusion, and these can only be contributions by one person, for one dissertation.
Personally, I had a little bit of overlapping text in two of my dissertations (which for bizarre reasons came out nearly the same time), but it was only the literature review, which at the time I didn't think of as a contribution, and I clearly stated the overlap in the later dissertation.  Also, I didn't claim that the thing I was best known for at that time (an action selection mechanism) was a contribution to either dissertation, just to be certain there could be no claim I'd made overlapping contributions (One was in Psychology & one was in Systems AI, so they really were pretty different.)
Basically, by the time you are ready for a PhD, you should be able to make any number of contributions.  So being productive and publishing articles is the main thing to worry about, and then secondarily following through, and following the rules, so you get your degree.  Your dissertation is not a documentation of your life's work – it's just one coherent document making a very clear academic contribution.  Hopefully two good students working together would make more than enough contributions that they can divide them up and each write interesting dissertations.

In math at Berkeley this varied by advisor. Some advisors insisted that theses consist only of solo work, while others did not. My thesis consisted almost entirely of collaborative work (though with different collaborators), and I think one chapter may have also appeared in a collaborator's thesis.  The advisor shouldn't sign off on the thesis if the student hasn't done enough work to deserve a PhD, but if you're going to do most of your work collaboratively after grad school it makes sense to me to do so during grad school.


text:
Is it ethical to use another university's journal subscription if yours doesn't have access?
As a graduate student, sometimes I really could use access to journals or databases of tables that for some reason my university doesn't subscribe to.  I have friends at other Universities that do subscribe to these journals and it would be very helpful to have the data.  Is it acceptable to ask them to retrieve the data?  I don't really want to start this if it's going to run me afoul of the rules.

My acces to safari books online is sometimes not available because all the paid sessions are used by others. Not quite the same thing, but shows how it could be possibly unethical. Ethics are supposed to be enforced by the individual first, to avoid getting into trouble with laws.

@tomshafer does your university have an interlibrary loan system? They generally get you data/papers that your university doesn't subscribe to, by taking assistance from other universities.. In case you are concerned about whether or not you are pursuing the right channels...

Not an answer to your question but a potential solution to your problem-- in addition to inter-library-loan, you can almost always email the corresponding author for a reprint.  In my experience authors are very happy that you are interested in their paper and will respond very quickly (like w/in hours) to a request.

Please note that many authors are willing to send you a copy of their paper if you can reach them (and that is perfectly legal, as this option exist in all the copyright transfer agreements for journals).

Is it against the law? Probably.
It it scientific misconduct? A breach of ethics? Hardly.
It is acceptable? It's your decision to make based on risk analysis. If you don't do it on a large scale (i.e. someone supplying many people with papers), it's probably okay. It is common practice.

Also, be aware that many journal articles can now be found either in pre-print or post-print form online (though this depends widely on your field), either on institutional repositories or on the authors' webpages. If you're merely missing access every once in a while, you can also nicely ask the corresponding author.

Against which law? Specifically, which jurisdiction? Since I'm pretty sure that click-through licensing holds no legal value in Sweden, I'm not so sure that paper-sharing is any more egregious than, say, going to the other University's library directly and reading the paper there...

@Mikael that's the point of “probably” in my answer. There are plenty of places where this would definitely unlawful, but I can't speak for every jurisdiction. Regarding the analogy with physical libraries, transmitting a PDF file is not like reading directly, it is an act of copying.

Our university and the journal host all say that access is provided to current students of our university only and that distributing to others or using for non-university related work is violation of the license agreement and subject to copyright infringement laws. So "probably" is "definitely" at least in the US for all journals I've ever seen hosted by Springer and ScienceDirect.

We have such a folder, digitally. It has 2775 papers and counting.

@tpg2114: In contrast the university libraries I know here in Germany are all also public libraries - everyone can get a library card (with the exception of Jena, where non-university people officially need to be Thuringians - this is commented on as completely ridiculous) and really everyone can walk in and read it there (you may need a card for orders from the archives and for accessing the computer places).

@cbeleites I think Germany is something of a special case. From the very little I read while there, it seems to me that the government has basically bought public licences for everything. I'm not sure whether transmitting electronically to someone abroad would be allowed though.

@JessicaB: (non-private) universities are owned by the respective state, so federal government is in this only indirectly. Maybe 16 states decided to have public access to their university libraries.  But also many research institutes give rather public access to their library (at least for reading, typically also for electronic journals), and many such institute libraries participate in large online catalogues (ZDB / GVK), which are actually not Germany-only but also have Austrian libraries.

... One thing that is maybe special in this respect is that Germany has a system where fees are collected from libraries, when buying copying/printer paper, copying machines etc. which are then distributed between publishers and authors. Copyright law here has an exception that allows single copies for research (making them yourself or having them made for you). But no, we're far from having access to everything. Our librarian does a fair amount of finding out who has the papers we can't access but need.

I would not explicitly acknowledge people who have helped you access papers that you don't have access to. As F'x pointed out, it's probably against the law, but it's unlikely to get you into trouble. The acknowledgement, however, creates more trouble than it needs to, because you're more or less explicitly stating that you got the journal in a clandestine way. So, I would just thank the person and leave it at that.

Definitely. If someone publicly thanked me for just giving them access to publications using my university's systems, I'd find it really awkward.

Well, it is anyways not the amount of work that would appropriately be thanked in the acknowledgements section of a paper. (Though I did thank our librarian for her outstanding help in the acknowledgments of my PhD thesis)

I think it predominately depends on how you are accessing the other university's resources.
If the resource is online and your collaborator grants you access either by sharing his username and password or through a network proxy, this is almost definitely in violation of computing guidelines of his university. To me asking him to knowingly violate a university policy, which is more or less reasonable, is ethically questionable.
If the resource is online and your collaborator downloads the resource directly, that isn't ethically questionable. If he then shares that material with you it may violate copyright law and it may be ethically questionable. If for example you chose as a collaborator/employee a student who has access to this resource then it is ethically questionable since collaborating with researchers outside the university is beyond the normal scope of a students "job" description and therefore they are likely misusing the resource. If you chose a member of academic research staff (or possibly a grad student) as a collaborator, then they are really doing there job by collaborating. I see no ethical issues with this. It might even make sense to include them in the acknowledgments of any resulting publication. Something along the lines of

I thank John Doe at the University of Good Library for help collecting
  reference materials.

I think that it would be against the law almost everywhere (because, in general, Institutions subscribe the journal / publisher so that its students / researchers / etc will have access to the material. The subscription is based in many factors such as how many persons will have access, how "important" the Institution is and will cite the provided articles etc.
And the Institution would grant the students etc. the access based on the same contract. I don't think that any can give an unlimited right to make copies and distribute and so on.
So, who would be breaking the law / the contract ? The friend who provides such access, in the first place.
A point that "softens" this is the fair use, but it has a very tenue line where you (or your friend) is doing a fair use or not of that access.
A solution: have you asked the Library of your University if they have some agreement with the University of your friend? Sometimes one Library can ask for books / journals / articles / etc from other Universities.

Wouldnt your profs be aware of the data and speculate where u got it from given they couldn't access it and you didn't pay a lot to have access to it?
Perhaps you could ask whoever may share their opinions or any official info concerning the said journals; your professors, whoever allows the permission for the journals you can access (your library), the journals themselves, and certainly permission from the other universities whose service you'd access. Not your friends- otherwise if you believe it can be interpreted as theft- then maybe it is , as they are not getting any business from you. You'll never know the reason why your university was excluded in particular until you do your homework and choose the all clear approach. Perhaps it was in fact intentionally excluded and then in that case then obviously it could be unethical and illegal!
Surely your only helping your self out but journals are in the business of making money and i believe the ethical choice would be to know beyond a doubt.

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text:
Is doing a favor for a professor in return for a favor strange?
I’m an undergraduate student and want to teach a workshop next semester, so I’m working with my school’s administration to get help with room reservations and stuff.
I have been bounced around between a few different head professors. Finally, I found the right one and got this email requesting my help with tutoring their friend’s daughter for an assignment:

Charles, I got your information - requesting to [start workshop next semester]. Let us talk about it in the coming days.
On unrelated note, my friend's daughter is [doing undergrad at X school]. She has requested for assignment help - private tutoring. If you have a hour or two today or tomorrow, it will be great to help her. This assignment is due [at time]. >
[Friend's daughter's name], if you have worked on these problems / have partial answers, you may want to share so that we can review and respond. Best wishes.

Is this weird? Will I get in trouble for cooperating?

@AzorAhai-him- United States, and faculty

@AzorAhai-him- the email reads as though he wouldn’t help me if I don’t do this unrelated favor for him, which doesn’t seem right. My ability to teach the workshop should be based on merit rather than readiness to do a favor. Not sure if I’d get in trouble or not for participating in the exchange at all, just seems like a no-no

Looks very strange. I would simply (politely) decline the tutoring and get back to the business at hand. They did specifically write unrelated so it's not rude/weird to expect to do exactly that. A quid pro quo type arrangement is unexpected and unprofessional.

For the question, could you clarify what "teach a workshop" means?

That is strange. But, do you think may be the professor only ask to see if you want to take a second job (tutorial) on the side (and he does not mean to force you to take the tutorial job) ?

If the assignment is due in a day or so, the chair may be struggling to find someone to help.

@mkennedy True, but not OP's problem.

Is it an opportunity for a paid tutoring gig that the prof is just passing along or more of a quid pro quo?

@BobBrown Of course. I was just wondering if that could be a reason why the professor made such a strange request (and that he may owe his friend a big favor).

There is a risk if you DON'T cooperate then your relationship with this professor is screwed, so regardless what "should" or "shouldn't" be happening, you need to evaluate how much the relationship with this professor is worth for finding rooms/running your workshop, and if for any reason that is worth lot, I would go ahead and help with this

It's not quite clear to me what kind of power the professor holds here - is the professor making a determination of whether you will be able to hold the workshop or not? Or is this just an administrative room scheduling issue that can't be realistically denied? The greater power the professor holds, the more problematic this becomes - this might not be a huge issue if you're asking him to rubber-stamp something, but certainly moreso if he's about to grade your term paper.

Yes, it’s wildly inappropriate for a professor to make such a request of a student who needs their help.
You can cooperate if you want, but maybe consider whether you want to be the kind of person who lets other people with more power manipulate him into doing their bidding. If this was a matter of life and death I might see a dilemma, but in this situation I have a feeling you could get your problem solved in a variety of other ways that don’t involve being a party to this sort of shady transaction. Good luck in any case!

I came back here to make this comment and I see that Buffy has written something similar in the comments on the question itself.  While I agree with your answer, I wonder whether there's a misunderstanding.  What would you, i.e. Dan Romik, think of OP asking how much this gig pays, alternatively writing, "My usual tutoring fee is $xx.xx per hour. OK?"  That is, is there a misunderstanding caused by the juxtaposition of two unrelated requests?

@BobBrown I don’t see any ethical issue with OP offering to do the tutoring as a paid gig (assuming that’s a paid gig they’d actually be interested in), if that’s what you’re asking, but I don’t see how that’s related to your last sentence. There may be a misunderstanding, or there may be a savvy manipulator who is careful enough to leave themselves the exit route of claiming a “misunderstanding” in the event someone finds fault with their behavior. Which of those two scenarios is the true one is unknowable given the currently available information.

(Regardless, the professor should not have sent an email phrased in such a way, even if the reason he sent it is utter cluelessness rather having unethical intent. So I stand by what I said about the behavior being wildly inappropriate.)

Intent is not an element to professional ethics violations, only an aggravating factor. Failure to understand that they are in a position of extreme power (handling an active request) and throwing in an "unrelated" major request is unethical, period. The minimal not-clearly-unethical would be (1) sending the request under separate cover (2) as a paid offer. Why on earth is the friend's daughter on the email?

@obscurans interesting, but could you support your claim about intent with a reference? Just adding “period” at the end of a sentence does not automatically make it true, period.

Thanks.  The "juxtaposition" I referred to is the offer of a tutoring assignment, perhaps paid, or perhaps not, along with what appears to be an offer to help OP with the event.  After several-re-readings, I've come around to your way of thinking.  By now it's probably too late, but I still think OP should have quoted a fee for the tutoring.

@DanRomik AAUP position Professors demonstrate respect for students as individuals and adhere to their proper roles as intellectual guides and counselors. [...] They avoid any exploitation, harassment, or discriminatory treatment of students.

"Unintentional" harassment is universally recognized as harassment - effect on the recipient controls. Same with discrimination. Same with exploitation. Academic freedom isn't the same as ethical freedom - if you're debateably unethical, you're already wrong.

The thing that makes this email strange (and a bit inapproriate when juxtaposed with the fact that you need them to help you) is that it does not specify the tutoring to be paid work, and therefore implicitly looks like a request for free labour.  If the professor intended to offer you an opportunity for paid tutoring work then they probably view that as an opportunity for you, rather than a request for a gratis service.
So yes, the email is strange, and it is a bad look.  It is possible that the professor just did not take due care to give all the relevant details and didn't notice this when sending.  I would recommend interpreting the email charitably, and assuming that the professor hadn't thought about the appropriate amount of money for the tutoring because they see it as a transaction that does not involve them.  Alternatively, perhaps the professor does not know how much money the friend intends for the tutoring, and so expects you to get the ball rolling on money discussions if you're interested in the work.  As to how to proceed, if you want some paid tutoring work, you could pick an appropriate figure to charge and then write back and offer to tutor for that amount; if you don't want the tutoring work, just decline or ignore the request.

To add to this answer, why would any professor extort a random student to obtain something that is of entirely marginal benefit to them? Does anyone actually believe that a friend's daughter's homework assignment is so important to this professor that they need to bend a random student's arm over it? A much more likely explanation is that it didn't even occur to them that their email could be construed this way. Yes, the email is weird, but so is reading it as an attempt at extortion.

@AdamPrenosil: I agree 90% with your comment (+1), but there are sometimes cases where managers in enterprises add on little jobs for their subordinates (or others like students who need their help) that go beyond the scope of legitimate work for the employer.  Yes, the benefit is marginal (e.g., pick up my dry-cleaning, feed my cat, tutor my friend's daughter) but this type of practice is not unheard of.  I think that's why people look at an email like that with suspicion.

The key problem I see is that while the OP is not getting a direct response to their question, just the vague suggestion they'll "talk" in the "coming days", the (unbelievably) "unrelated" question of tutoring his friend's daughter comes up to be done in the next day or two.
This seems like an almost explicit condition for talking to the student.
There's the breach of confidentiality as well, but I'd personally consider this a disciplinary hearing level of abuse of authority.  Someone working for me would be have their work emails checked for a pattern of such abuse in the past.  I'd be contacting former students to see if they were put under similar pressure.
At the very minimum this would (from me) generate a formal warning letter related to ethical appearance as well as practice.  It's not enough to be ethical, you have to be seen to be ethical in such a position.

"She has requested for assignment help - private tutoring"

"This assignment is due [at time]."

These are absolutely shocking lines.  It's practically an admission that the professor expects the student to complete the assignment for her.
We've seen many scandals about wealthy people's kids having work done for them and even having exams done by other people.  Let's not be naive about how bad this email looks.
And I don't think anyone in a position of power like this gets much benefit of the doubt.  They should be avoiding this exact kind of communication like the plague.
In short, this (IMO) stinks.
Someone suggested it's a marginal benefit to the professor, which is not really true as it could be a case of arranging free "tutoring" for someone else in return for some other favor.  That, I regret to say, is how some people conduct their "business".

You're making a lot of speculative inferences based on next to no information and treating them as fact. The inference from the fact that the prof writes "This assignment is due [at time]" to "an admission that the professor expects the student to complete the assignment for her" is particularly strange. Yes, people in positions of authority should take care to appear ethical in addition to also acting ethically, but you're taking the uncharitable interpretation to the extreme imo. It's not even clear that the professor has the authority to deny the student's request if they are displeased.

@AdamPřenosil We're going to have to agree to disagree.  My view is that basically all of these statements individually are "bad smelling", so to speak, but the combination is just awful.  I find it hard to believe any of us would not be furious to find someone under us wrote such an email.  Remember it's not enough to not be caught doing something unethical, these days you have to have a clean appearance as well, and this is a long way from clean looking.  Many businesses would fire someone on the spot for such a letter.  YMMV of course, but that's my view.

Even if it were of marginal/no benefit to the professor, it's still a wildly inappropriate way to make such a "request" for all the other reasons you pointed out. If anything, it would be even less clear why the professor is doing this if they're getting little or nothing out of it.

Yes, I consider the message weird for several reasons.

Apparently, the message is addressed both at you and the professor's friend's daughter. This is very inappropriate, as it's a breach of privacy: it is of no concern to the professor's friend's daughter that the professor and you are in negotiations about teaching a workshop.

Since the message has been addressed both to you and the professor's friend's daughter, she is aware of the fact that the professor has asked you to help her. This is an impolite act of the professor, because he's put you into a potentially face-threatening situation: as the recipient of the request is immediately involved, it may be harder for you to refuse than it would be otherwise.

Even though the passage of the message in which you're asked a favor is prefaced by "unrelatedly", the message may be read as an implied quid pro quo – as you say in the title, it may be interpreted as asking you a favor (to assist his friend's daughter) in the expectation that you feel obliged because you have received a favor (support in teaching a workshop). If this was indeed the intention, would be very unethical behavior, and you may even argue that it was a kind of extortion given the power imbalance between the professor and you.

The message doesn't mention payment – only "private tutoring". This omission is either unprofessional (if it was simply an oversight) or exploitative (if the favor is indeed intended as unpaid tutoring).

What I would do in response to a message like this is this: I'd simply delay my answer until the deadline for the assignment has passed. Given that all this appears to be on very short notice, a delay of a day might be enough, and would still be within a completely acceptable response time. Then, in the reply, I'd say something along the lines of

Sorry, I was kept very busy yesterday due to [valid reason that doesn't reflect negatively on me]. I sincerely hope that [professor's friend's daughter] was still able to complete the assignment without my assistance.

The rest of my response would be about the workshop, and not the tutoring offer.

Nice answer and +1, but about your suggested email reply, I would not advise OP to either apologize (“sorry”), explain or excuse the totally normal delay in response time (“I was kept very busy”), or address the friend’s daughter’s tutoring need in a way that implies that OP might have been willing to help had it not been for being busy (“I hope she was still able to complete the assignment without my assistance”). When someone tries to manipulate you, just say no or ignore the request and you minimize the risk of getting drawn into their web of manipulations in the future.

Of course this is inappropriate: Someone in a position of power is demanding a personal favor from a subordinate in exchange for doing their own job. The "unrelated note" they mention is not actually an unrelated note, in the same way that it wouldn't be unrelated if you said, "Sure, I'd be happy to tutor your friend's daughter. On an unrelated note, I'm going to busy next week, and it sure would be nice if I got an extension on the upcoming homework assignment." (Or, on the stick rather than the carrot side, "You've got a nice business here. It sure would be a shame if something were to... happen to it.") It's a quid pro quo outside the normal professional relationship a professor is supposed to have with their students.
That having been said, the professor may just be unaware of how unprofessional this is. They're in a position of power over their subordinates; undergrads and grad students are completely dependent on professors and have very little recourse in situations like this. Maybe they're used to getting undeserved perks or think they actually deserve them. (For that matter, I've run into more egregious demands along these lines, though they're not things I can post about in public.)
Unfortunately, there's not much you can do in this situation--- which is exactly why it persists. You can decline, but that's possibly going to sour your relationship with the professor and block you from getting the workshop. (And no, the professor isn't going to explicitly say that they're denying you the workshop for that reason, but that doesn't mean it isn't actually the reason.) It may also sour your relationship with other professors; they do talk among themselves, and you're not going be around for those conversations to defend yourself. Going to the head of the department or other administration figures would be a waste of time; no department is going to throw a professor under the bus for an undergrad without much more egregious and much more explicit evidence.
So, unfortunately, that means you're stuck deciding whether teaching your seminar and cultivating good relationships in the department are worth a bit of sleaziness and graft. It's unfortunate, but welcome to academia.

"Sure, I'd be happy to tutor your friend's daughter. On an unrelated note, I'm going to busy next week, and it sure would be nice if I got an extension on the upcoming homework assignment." – I would be very curious to see how he'd respond if the asker did reply with that... (Though I wouldn't actually recommend replying in this way.)

Just apply Hanlon's razor here, and be positive.
If you are interested in the private tutoring, you can write (of course adjust to the specifics):

Thank you for the reply I am happy to join. Let’s talk end of the week.
About the private tutoring, I am glad to help, and tomorrow looks good. Should I negotiate the rate directly with (daughters name) or (friend name)?

Only send this reply to your professor, so if Hanlon's razor failed and he wants out of that, he can do it in a facesaving way.

Academicians are often very busy people. I interpret this as he sees you are suitable for tutoring since you are interested in that workshop and take action. Thus he refers you to that person as a possible tutor. One email to you both probably means I don't want to get involved, do whatever you want. When there is exploitation going on, you will receive a different email while the other person gets something else.
I am almost sure what he/she is after is to get rid of this chore, not to get a service for free. I sometimes get requests like that, though I will, in person, advice my student not to do it for free, just to make sure they will not misunderstand it. It is not a direct favor, but I am just trying to get rid of the request and in the meanwhile I am giving better students a chance to earn some bucks. I think this might be the case here.
I think it will be a good idea to take on the task, asking some tutoring charge, something that is not excessive (if you ask too much it will strain the relationship between your professor and his/her friend) or immediately respond you will not be able to help due to some reason. Only accept if you are sure you can handle the task.

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text:
Two students having separate topics chose to use the same paper format
Two students decided to consult each other about term paper formatting, because the teacher didn't provide guidance on the format. They used the same format, but different topics and totally different content on the subject matter. Is deciding to use the same format plagiarism?

Seems pretty unlikely. Double spaced, single tab indent, Elite typewriter font seems pretty universal in the old days. The content is the issue, not the format (unless you use Comic Sans, but plagarism isn't the issue there).

Unless choosing or designing a format is a part of their assessment, you could avoid this in the future by requiring a standard format such as APA or MLA.

What do you mean by same format? Do you mean the physical layout on the page? Or do you mean the same basic headers/sections? If it's the latter, could they have found the same reference in their research? If they googled "basic outline for research paper" (or something) they would have very similar looking formats.

A kindergarten question, and seven kindergärtners answering, and getting highly voted for it. ?!?

Interesting. I wonder if this is a way to catch plagiarism: As an instructor, you don't give a uniform format for students to follow. Then watch out for similar formatting for a possible sign of plagiarism - if they copied format, then check if they maybe also copied content ?

@Karl What do you think of Is doing two PhDs a good path?

No.
If using the same format were plagiarism, then almost every article published in a journal is plagiarizing, since they all have the same format. Plagiarism happens when some kind of intellectual idea is involved, and the format used is not an intellectual idea.

"and the format used is not an intellectual idea" isn't that a bit too general? Can't actual templates be subject of copyright?

You'd be hard pressed to find a template that makes sense for which there is no prior art.

@Džuris Copyright and plagiarism aren't the same. There may easily be a copyright on, e.g., a Latex template, but that would still not make using this template without attribution plagiarism in the academic sense.

If so, then everyone choosing to use a predefined LaTeX format would be guilty of plagarism.  Many journals provide their own, universities provide them for theses and dissertations, some instructors even provide them for papers & homework.  Indeed, do you know that the students actually consulted or created a format, rather than simply used the same template?

This is absolutely not plagiarism, unless the topic of the class was design related.  The template used to format a paper is irrelevant to the evaluation of the paper's actual content.  Students share format templates and download such templates from the internet all the time.  Microsoft Word itself comes with numerous templates for various kinds of documents.  None of these are problematic.

Imagine if this wasn't the case. That would be a lot of very disconcerting information.

Even if the topic is design related, copying a term paper design may be cheating on a homework assignment, but almost certainly not plagiarism.

Even if the topic is design related, as long as it's not the design of the paper itself, it should not be considered anywhere near "plagiarism", when the paper is only a medium, not the content.

I don't see how the formatting could be subject to plagiarism if formatting/design itself is not a topic of a paper.
Note 1: However, I can see how similar/same formatting can be used as an additional consideration when deciding on an actual academic misconduct case.
Note 2: Consider explicit instructions of the teacher:

This is an individual project. Do not consult each other on any subject regarding this paper.

And two students use a very distinct template and [for easiness of this post] admit discussion the choice of the template with each other. Even in this case, it is not plagiarism; however, the students slightly violated the "letter of the law" (whether the "law" is lawful and reasonable is outside of the scope) regarding the project. But I believe the "spirit of the law" is to not penalize the students for this at all.

Are they being graded on their report template?
If constructing a template forms part of the coursework, and the template structure (have you nested styles? used appropriate "keep with next" on headers? and so on) is submitted and forms part of the marking scheme, then yes this would be plagiarism.
If they're being graded on the content of their report, and the most that happens with the template is that they might be docked some marks if it prevents the report being understood, then of course it isn't plagiarism. Plagiarism involves copying graded/evaluated content. Unless this is a course on how to use Word, the template isn't part of that.

So it would be plagiarism to use LaTex to format the paper, if there was any marks awarded based on how the paper looked?

@Ian If the course was teaching you how to use LaTeX and part of the coursework was to construct your own language style, it certainly would be possible to plagiarise someone else's work. If writing a template is not part of the content of your course, then of course not. Like I said, if the only requirement for the template is that it makes the content comprehensible, then it's not relevant.

It wouldn't be "plagiarism" in this case either. That's like saying it's "plagiarism" to copy first-graders' sums-of-numbers exercise answers. It's not something novel enough to count. ... unless, that is, the homework involves some complex LaTeX trickery or something, but I really doubt it.

Short answer, no. Longer answer, I took a class last semester in which I had to write several papers or several paragraph essay style answers. I don’t think the professor ever required a specific format, but he always insisted that the papers have things such as indented paragraphs and a reference page so, formatting was the student’s choice. I always wrote my papers in Google Docs and used the MLA template provided and I know that others in my class did as well. The vast majority of the papers we did write were run through Turnitin - which checks for plagiarism - never set off any alarm bells for plagiarism either using the MLA or APA formatted templates.
Now, I am also certified in my state here in the US to teach Emergency Medical Technician courses and, as such, could end up encountering situations where plagiarism could be an issue. I would not consider using an identical format any type of plagiarism, particularly if it were one of the “big” paper writing formats like APA or MLA. Perhaps if I had specified to each and every student that they must, independently create their own format, then I’d raise an eyebrow at students with identical formats.

Ask yourself why plagiarism is forbidden. It is usually for a few main reasons:

falsely amplifying a supposed "fact" without having done any work yourself is evil on epistemological grounds, because it leads others to think there is more evidence in favour of something than actually exists;
pretending to have more intellectual ability than in fact you have is epistemologically evil again, because it leads people to believe that your other output is better-grounded than it really is;
humans are social animals who do not take kindly to being deprived of the credit for their work (and indeed the incentives of our societal systems may become even more misaligned than they already are, when work is routinely uncredited).

Merely copying someone's formatting only violates one of those reasons if the formatting itself is a key subject of the work.

Could they have worked together on a previous project? It is very rare to create new templates, and if they worked together last academic year, they might just both have kept the same template?
Even if they haven't: it is a very strict interpretation of plagarism for "sharing Word template" without any actual content to be plagarism.

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How long typically are paper reviews? Is there such a thing as “too long”?
When you review papers submitted for publication, is there an “optimal” length for reviews?
In my experience as an author and referee, I have seen a large range of review lengths (for reference, a paper in my field is typically between 3 and 8 printed pages):

Zero length: for some of the papers I have authored, the reviewer just clicked the “accept as is” or “reject” checkmark on the review sheet, without adding any comment (at least, not any comment visible to me). It's not so common, but it has happened.
Short length: a lot of time, I received reviews who consisted of a single paragraph.
Medium: one full page, maybe two pages.

Although I have never received any such long reviews, I have myself written on a few occasions reviews that exceed two pages, including once or twice a four or five-page review.  These were cases where the paper was good, but could be (in my view) much improved and some of the aspects/consequences had escaped the authors' consideration.
I am wondering how useful reviews of various length are to editors. I often consider that “unmotivated” reviews are useless, as they do not give any real insight about the paper to the editor. For example, if the editor gets two conflicting non motivated reviews, how is she to decide?
On the other hand, although I write some from time to time, I have never received long reviews, so… is this something frowned upon?

I'm not an editor but as an author, I certainly prefer a long review full of detailed suggestions to a one-line "your paper does not meet quality standards, reject".  Sometimes a reviewer ends up contributing more to a paper, than one of the "minor" co-authors.

I know one of the journal reviews went on for a year and finally the author had to push the reviewers a lot to get the final results. This is the longest I have seen.

@user2915398 I was thinking in term of length of the written review, not time-wise

This is awesome! I have been wondering about that as well. And may I suggest the answerer to also mention if you are speaking as an editor or not? I value all inputs and tips but would also love to know some norm from the people in the industry.

Reviews can be of quite varying length but obviously the extremes indicate some problems.
A review consisting of "Accept as is" would be highly suspicious in my mind (as an editor). It usually means the reviewer has not done any work, essentially no manuscript is that close to perfect (although it may of course happen). A review of "Reject" without additional comment is equally pointless (I am then assuming the journal has some form of quality check before accepting for review). An absence of comments is just a big warning sign since there is no perspective on why the MS is either perfect or perfectly worthless.
Considering the length of a review, it is governed by two factors: the quality of the manuscript and the personality of the reviewer. To some extent longer reviews indicate more questions to be resolved. At the same time some reviewers may be more nit-picking than others so that also influences the length. Based on my experience as an editor, I would say, as a rule of thumb, that at least a page of (single spaced) comments would be a basis for a descent review for a normal manuscript (15-20 pages double spaced excluding references, tables, figures) in the field experiment/observation based science where I work. A review of more than three or four pages of (single spaced) comments would be unusual and probably involve comments down to spelling issues. "A decent review" involves providing clear and constructive comments that will allow the editor to value the manuscript and the author to improve the manuscript.
So I would not say that a long review would necessarily be frowned upon, it clearly depends on how constructive it is. If someone spends a lot of effort improving language and grammar (which does not necessarily constitute the expectations from a review) that could be very useful. Normally such comments may be made as revisions in a file rather than a written report. So length is not a major issue, constructiveness is.

As an editor, how do you handle zero length reviews? If the review just says "accept" or "reject", do you grudgingly follow the reviewer's advice, or do you look for another reviewer?

Most likely add another reviewer. The least one can do as a reviewer is to justify why one makes the recommendation. It does not necessarily have to be a long justification, but should provide a perspective on the paper and why it should be accepted/rejected.

Speaking from the point of view of an editor: One of the best reviews I ever got was longer than the paper. The author, a young researcher, had proved three theorems, one of which I recognized as a known result. So I asked the original discoverer of that known result to referee the paper. In my cover letter, I mentioned that I recognized one of the theorems as his, and asked whether the other two theorems had enough novelty for a publication. It turned out that the other two theorems weren't new either. The referee could easily have just given citations for those two theorems and recommended rejection. Instead, he gave me (or, really, gave the author) a long, clear explanation of the state of the art in that subject, and he suggested some open problems that the author could try working on.

I at least once wrote a review longer than the manuscript itself. The paper pushed some buttons of mine, and I couldn't stop writing. (Similarities with answers of mine here at Academia.SE are coincidental.) I think I recommended rejection and explained in considerable detail the non-trivial error the authors had fallen into. The editor appears to have appreciated this review; he still has me review regularly and introduces me as "the guy that writes reviews longer than the original paper."

An "accept as is" option is useful after resubmissions; it signifies that no more work needs to be done. However, it is unusual to see that happen in an article on the first round of submissions. (I've had that happen precisely once in my career.)
Otherwise, I would say that the more detailed a review can be, and the more precise the suggestions for improving the paper are, the better it will be.
One to two pages is typically the norm; however, I have submitted a few three- to four-page reviews when I thought an article was already quite good, but could be better.
On the other hand, if a paper is already of relatively poor quality, I will explain the methodological or other significant flaws, but skip over an analysis of minute points; (it's simply not worth the time to rearrange the furniture when the roof is going to collapse any minute.)

As an author, reviewer, and key reader of a respected engineering journal, I can offer some perspective.  The shortest review I received was one I solicited from a highly-respected professor at a prestigious university.  His review was basically “This manuscript is not written well enough to be reviewed.”
The longest review I have received as an author was about five bulleted comments some of which were optional revisions and some minor but necessary clarifications; the shortest was one minor comment approving the manuscript.
As a reviewer I have on several occasions completely rewritten a non-English language author’s manuscript as a gratis professional service.  To my surprise, I received thank you letters from the professional society publications chair and the editor thanking me for my ‘laudatory’ service. I took that to indicate my effort was unusual.
My shortest key reader review summary was to a VERY famous author who after a 22 page derivation, which he summarized as ‘simple’.  With feigned seriousness, I ‘required him to remove the word ‘simple’ since he was on this uncustmary occasion communicating with mortals.

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What corrections can you make on galley proofs?
When I receive galley proofs of a paper, I look at possible errors introduced by the copy-editing team. But while I proofread the article, there are sometimes small mistakes I'd like to fix, which were not introduced by them (i.e. they were already present in the accepted version of the manuscript).
Usually, the proofs are accompanied by instructions saying that extensive changes should not be introduced at that time, and any such changes would have to be approved by the editor (hence, I suppose, delaying publication). However, the limit is not very clear to me. What is considered extensive changes? In particular, what do you think of the following items (from my experience):

Slight changes in wording, to improve clarity
Updating a citation, because an “in press” or “ASAP” article now has page numbers
Adding an important (but not crucial) citation one had missed, in the introduction
Adding a citation to a paper that has been published since the manuscript was submitted; possibly adding a short sentence to the text

What I have done so far is change everything that I think should be changed to improve the paper (including all the above items), and let the typesetter decide whether he wanted to send it back to the editor. I never received any complaint or comment on my changes, which could indicate that it was the correct course of action.

A galley proof should primarily be proofed for typos or mistakes made in the type-setting process. It is not the time to change phrasing, exchange figures, add or remove blocks of text or anything else substantial.
Adding references might seem like a useful addition but the problem is that it would then be possible to add references without the knowledge of the editors and of course the reviewers and hence possibly make changes that could have affected reviews etc. The case of adding anew paper is a similar problem since it may change the paper in ways that the editor and reviewers have not seen and therefore ok'd. If such changes are wanted (they are probably seldom needed), it would be best to at least check with the editor if that would be appropriate. In short, no changes should be made that alters the content of the paper. All such details should have been checked and if necessary corrected before submitting the final revised manuscript for copy-editing and proof production. Any corrections called for by the copy-editor and editors after submitting the final version is of course to be made.
Updating of references are usually also acceptable, to, for example, add the publication year (from e.g. "in press") or adding doi, page numbers etc. if these were not known at the time the final version was submitted.
The proofing stage is not a stage where many changes should be made. The manuscript that is submitted for proofing should be considered the last chance to make any substantial changes. What many do not realize is that all changes done after a type-set proof has been produced may cost the journal money, apart from the extra time and trouble it causes. So, as an editor, I often have the feeling authors just send in their final manuscript without checking figures and text properly and then waiting for the proof to make final changes but is, in fact, an abuse of the system.
Regarding your last paragraph, the type-setter is usually not a scientist and has no idea of what changes might mean so to think that the type-setter would act as some form of intermediate editor is not right. In cases where type-setting is done in-house it might be a professional doing the type-setting but I would still say this is not the way to handle the type-setting/proofing stage.
So, anyone, make sure the final submitted manuscript is checked, complete and correct.

Checking the final manuscript is necessary, but it doesn't in itself answer the question: things may have come to light after the final submission, which could not have been foreseen (a new paper coming out, e.g.). But the rest of the answer is useful, given your experience as editor.

All the cases you mention (minor wording changes, updating/adding references) seem entirely fine to me.  It's best to do this only when it's important, but in my experience it's standard and acceptable.  Peter Jansson's answer suggests what's acceptable might vary between fields or journals, however.  (His point about making changes after reviewing is valid, but I'm not so worried if the changes are relatively minor.)  All I can talk about is my experience in mathematics as an author and editor.
The book Mathematics Into Type (first published by the American Mathematical Society in 1971, and last updated in 1999) says the following on pages 53-54:

If an author makes changes in first proof amounting to more than 10% of the original cost of composition, these changes are usually considered excessive.  Many publishers ask authors to bear the costs in excess of 10%.  These excessive correction charges may be the result of large sections of text being deleted or of changes in wording or notation.

This agrees with my memory of the traditional standard for excessive changes.  It's not clear what, if anything, this 10% figure means nowadays, or how widely it's used (although web searches lead to some mentions of it in guides for authors).  I wouldn't take it too seriously, but it does give an indication of what was considered acceptable in the past.  Note that it didn't mean you could rewrite 5% of the article, since those changes would require resetting a lot of the surrounding text as well, but it meant you had some flexibility for making a few small changes.
Going back further, in 1943 the AMS said this:

It is important that galley proofs be carefully read and corrected by the author, since it is only the author who can detect errors which are due to an imperfect manuscript.

So at least back then the official AMS position was that authors should correct their own mistakes when reading proofs, and not just new mistakes introduced by the typesetter.  (I don't know of more recent references that discuss this explicitly.)
Of course I agree with Peter Jansson that articles should be carefully checked at the time the final version is submitted, with changes to the proofs being considered a last resort rather than an opportunity to delay the checking.


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How can I help a PhD student who's dead-set on keeping methodologically wrong results?
I am helping a STEM PhD student with a statistical approach they developed for a key thesis chapter. Unfortunately, the approach is fundamentally flawed and makes no statistical sense. (I'm a freelancer with a statistics PhD and first got this as a programming job before finding the issues.)
I've tried to explain why the approach won't work, but to no avail. The person keeps on looking for workarounds, which I know won't work either. The approach should be ditched entirely.
What's a reasonable solution in such cases? Should I suggest potential alternatives for the chapter—and I can think of a few? If so, how should I proceed ethically?
There is a related question, focusing on a midterm marking.

Added in response to comments:
The job began as statistical programming help, which I later discovered to be for the client’s PhD (as usual, the initial job details were scant). The student client is the one paying;  I’m unaware of the funding source. My previous academic clients obtained university grants to cover the fees and noted my contributions, primarily code, in their thesis or paper acknowledgements.  For me personally, correct execution is a matter of professional ethics.  I also do not wish to abet violations of academic ethics.

Are you their advisor or otherwise have a say in accepting their thesis?

You note that the student will need sign-off from their advisor for their wrong approach. So: what's the advisor's stance in this situation?

One possibility would be for them to post their approach at CrossValidated and solicit feedback. There are some statistical experts there. Perhaps they could sway your student.

The relationship s a little unclear. You're doing a statistical "programming job" for this person's PhD? Are you paid by the student, or someone else? Will you be an author? Is this a common arrangement? Is there a question of professional ethics if you execute on a flawed methodology?

@DanielR.Collins Important questions, thanks!  I've amended my post.

Does it "invalidate the overall findings"? Is it OK to have a PhD thesis with shortcomings and inaccuracies?

How obvious is the problem in the student's approach? I can imagine that in some fields  even the advisor might not be cognizant enough about the finer points of statistics. In a STEM field you would expect that not to be the case, but...

LEAD. HORSE. WATER. DRINK.

@JyrkiLahtonen: I have seen lots of extremely bad statistics happening in psychology, which I am most exposed to, and psychology students get treated to two full semesters of stats. Medicine is worse. Statistical understanding in biology is so mind-blowingly inexistent that I recently put out a call on LinkedIn to please consult a statistician before you run your animal tests, because you may well kill animals only to have utterly unusable data afterwards, which any statistician could have told you beforehand by a glance at your project plan. (Based on a question at CV.)

My concern exactly @StephanKolassa. The teacher of my only relevant course warned us (while explaining basic statistical testing of hypotheses) about medical professionals coming to us with data based on N=17, asking for a stamp of statistical signfigance. I did keep the lesson in mind when carrying out telcomm simulations.

Beyond suggesting that they speak with their advisor about it, you have probably done all that you can do. You can't force someone to take good advice.
If you suggest alternatives, I'd make the statements general. If you give minimal advice that they can follow up on it will benefit their learning. Don't do their work for them. Suggesting directions is the proper course, even for their advisor.
--
Edited to add: An advisor can and should certainly stop a student from taking a wrong direction. They have both the authority and responsibility to do so. They can also point to a better path, but they shouldn't provide solutions to the student's problems. They should let the student work out the solutions for themselves. That is also my advice to faculty in general when approached by students with questions. Let the student find their own insights.
The OP here doesn't have the authority, but should still respect the learning of the person by not doing their work for them.

+1 Though "Suggesting directions is the proper course, even for their advisor" is not enough for the advisor in case the student uses the wrong methodology. I am usually quite "laissez faire"-oriented, but it is the adviser's job to nip scientific malpractice in the bud. In the case here, as an advisor, I would make a very strong point not to do this. If that doesn't help, I would tell the student to keep me off the author list. If that is a persistent pattern, I would also suggest that the student gets an advisor whom they are prepared to listen to. OP, however, has none of these recourses.

@CaptainEmacs, I agree, actually, but saying "NO" is different from doing their work for them. Put them on the path, but don't walk them down it.

I understand you now. Perhaps make that more unambiguous?

A slight complication: the student’s PhD is not in statistics, but that thesis part requires intricate and specialist stats. In any case, except for the most basic outlines of alternatives, I’ll require approval of their PhD supervisor.

It doesn't matter that the thesis isn't in statistics. If the error brings the results into question it is a serious issue.

Re "you have probably done all that you can do": Well, you can write a CYA letter (and have them sign it (for having read and understood it))

After a certain amount of pushback, the next natural question is, at what point will you remove yourself - and most importantly, your name - from this work? It appears as if this person is disinterested in taking the proper steps to rectify this matter. If that is the case, then I would say it is obviously time to part ways.

My current instinct is that the person created and got wedded to what they thought was a brilliant theory. Now it’s hard to come to terms that it’s not the case. And it’s also big efforts to change course.

Step away as soon as you're convinced beyond a reasonable doubt that you won't be able to change their mind (or that their supervisor won't force them to follow your advice) (you could tell the student that you're happy to resume the work if they change their mind, e.g. if their thesis examiners force them to drop the improper approach — although by that time it will probably be quite painful for the student ...)

The proper venues for expressing your doubts about a student's work is in supervision /progress meetings. If you're not part of those meetings, then all you can do is try to convince your PhD student client, or ask them to let you make your case in their supervision meetings.
Of course, there is always a venue for a scientific critique of their work: you could publish an article or commentary stating your case. If the consequences of the student publishing a flawed article are high, you could consider doing that.
One important thing to consider is that you might be wrong (I doubt it, but it's not impossible). If you believe that the approach is flawed, then you can withdraw as a coauthor or named contributor.
FYI, under the CRediT authorship model, programming software qualifies you for authorship, so check if the journal subscribes to that model!

The simple approach is 'the customer is always right'. You gave them advise but they chose to ignore it. They are paying you so it is their problem, not yours.
Now you said you see it as problematic for your professional ethics if this methology is used as is. In that case I would recommend talking to the student and telling them you would like to talk to their advisor about the issue. A three-way talk is fine, a one-on-one with the advisor works as well.
First, it seems a bit unusual to me that a PhD student is paying someone external for statistics help. This could be fine, depends on the area but then the PhD advisor should definitely be aware of this and probably will also be the source of the money.
If the advisor is not involved with this this would be a major red flag for me.
If the advisor knows about your involvement they can respond to your advise and the students argument. Even if they don't have the statistics knowledge to decide the case they know that you have that technical knowledge, that is what you where hired for. So let the advisor arbitrate (and depending on the results decide whether you want to continue working with them).

And get it in writing?

It sounds like you have already done what you can to help the situation. It is not your job or responsibility to force a correction. If the method is flawed to the extent you say then the PhD advisor and/or committee should catch it and force a correction. Get paid for your work this far and disengage.


text:
Frustration with machine learning and deep learning research
I am doing a M.Sc. in artificial intelligence (AI), and I am collaborating with a computer vision company in a deep learning (DL) research project that will eventually become my master thesis.
Basically, my project is trying an approach that could, hopefully, achieve better results than the approach that is already in use in the company. (For those familiar with deep learning, I try to generate synthetic data for a task where data acquisition is expensive.)
However, after a lot of time and effort spent on this approach I see that the results are terrible. This new approach gives me worse rather than better results than the existing one, which means that at this moment all the effort has been a waste of time.
I feel stuck as I can't make any progress. Every single idea that I have that could potentially lead to improvements makes no difference. This is even worse than if I had worse results as in this case I could at least know what doesn't work. I feel that it is very hard to get any additional knowledge on this task as the experiments that I do don't validate any hypotheses. After months of working on this model, it still seems to me a complete black box.
As a result I end up working much more hours than agreed on in the contract, only to fail in generating any relevant new information or progress that I could report to my boss/advisor. I am spending a lot of time reviewing the code thinking that there must be something wrong.
Sometimes I wonder if my whole project is simply doomed to fail. Maybe this idea is simply not doable for the current task. And even though I feel that my advisor is aware of this danger, I can't help but feel that a failure of this project is a failure of me as a professional. After all, how can I be sure that I tried everything or that I correctly implemented those ideas?
Before this I was sure that being a AI researcher was the career that I wanted to follow. But now I feel that I can't cope with this level of anxiety and frustration. If it is like this for a M.Sc. project, I wonder how it would be for a Ph.D.
Did anyone have a similar experience in DL or even in another area? Can anyone with a Ph.D. share some thoughts whether things will continue to be like that?

"All effort was a waste of time." Not true. You learnt something about your model/method, even if it was not what you wanted or expected to learn. It sounds like you are not getting much help from your advisor. How often do you meet them? What kind of feedback do they give you?

This sort of thing is unfortunately common. Things should work, but don’t, and there could be so many reasons why: the algorithm, the implementation, the data, … Sometimes a second set of eyes is needed, nearly always a thorough code review is in order. Talk to an experienced colleague to see if any of your core assumptions are misplaced. Debugging gets quicker with experience.

Deep learning needs real data and 2. Synthetic data generation is often in the realm of graphics or physical modeling, this problem is probably one outside of ML. I was on a similar project and opted to terminate.

In my experience most of research is about failure, it happens to all of us, all the time. Some failed efforts help you go in the right direction, and some are just dead ends that only get you frustrated. You might learn to tolerate failure and cope with it as you gain experience (also to identify dead ends quicker), but it will not go away. Talk to your supervisor to figure out whether this particular project is failing spectacularly and you can expect better times ahead. If not, then you need to assess if you're willing to put up with the frustration.

Research projects fail all the time: that’s why it’s called research rather than homework.

I think you are plunging right into the heart of the most hyped topic in the field. Get the AIMA book and read it cover to cover. There are so many topics and subtopics there. Make sure you understand the whole landscape before you choose what to pursue specifically.

In a different domain, can you imagine the number of masters, PhDs, postdocs, etc that aim to cure cancer? How many of those do you think achieve that goal? Some parts of AI have become an experimental science, and should be judged by those standards.

My very limited experience with machine learning is that a lot of it does consist of trial and error and guesswork, and the ways to work out why something works or doesn't work are quite limited, and generally you have to just keep trying things until it does work.

onda activate SimEx

@MarcGlisse  Umm... not so much.  If any of those aim to cure cancer they are immediately informed that it is not a realistic or feasible goal.  I assume that the goal in the OP's project is somewhat more tractable than that.  If you're coming from CS, the OP's project likely isn't prove P = NP or something quite so pie in the sky.  I don't mean that it necessarily can be done, but it likely isn't something that is so obviously unlikely to be doable in a short project.

How do other known approaches perform?  There isn't only one other known approach, right?  Their current approach is likely the best that a full-time paid professional could come up with, so I don't think you should be disappointed by failing to do better.  It may not be an appropriate goal for a thesis to beat such an arbitrary benchmark.  Maybe doing a survey of existing methods (other than the company's) first will 1) give some concrete data to use in the thesis and 2) inspire a better approach than the currently failing one.  Start simple, how good is least squares on this problem?

Negative results are still results and should be published. Some fields have dedicated conference tracks for those.

As I wrote in this old answer, "it didn't work" is not the end -- the end is knowing why it didn't work.
The caveat with AI/ML is that things are moving so quickly that "nobody got time for" clever ideas that didn't work. In other fields, a negative result might still lead to a strong publication, but this is not the case in AI/ML. Even things that do work but don't outperform the state-of-the-art are unlikely to get much attention. This is the nature of the game, and it is something to accept if you move forward in this field.

After all, how can I be sure that I tried everything or that I correctly implemented those ideas?

This is the main thing I would focus on. A general rule of thumb in research (at least, in fields where something is being created or calculated) is to do many "sanity checks" before running the complicated, conclusive experiment or calculation.
In computer-related research, a mistake that I often see novices making is that they write hundreds or thousands of lines of code and then turn it on (often running it for several days!) and expect it to work. I always suggest the opposite: start with the simplest possible thing (e.g., 16x16 thumbnail images) and verify that that works as expected. Keep simplifying until it does the right thing (and ideally, until it runs very quickly, allowing you to run experiments in near-real time). Then you can slowly add back the complexity. In this way, you can ensure things are implemented correctly, and if it ends up not working, you'll have some intuition for why the original idea didn't work in practice.
Finally, a subject-specific comment: if you are using GANs to make synthetic data, you should know that GANs are highly finicky (e.g., to the hyperparameters). You may need to run many experiments before you find the magic numbers that achieve the desired performance. This will require many GPU-hours -- all the more reason to run many "sanity checks" before unleashing all your experiments.

‘ A general rule of thumb in research is to do many "sanity checks" ’ – that's a general rule of thumb in basically any discipline that creates something. Engineering, programming, politics, even music. Unfortunately, quite a lot of research is actually doing a rather bad job at it.

"the end is knowing why it didn't work." Unfortunately, this is seldom possible in the area of deep learning. As the OP wrote: "After months of working on this model, it still seems to me a complete black box.", which is understandable, since these neural nets consist of millions or even billions of parameters. Nevertheless, there do exist tools that allow you to "peek" inside a network, such as Cockpit.

@mhdadk - not sure I agree. Yes, DL is famous for its black-box problem, but it's still possible to gain intuition by looking at which experiments gave good results and which experiments didn't.

@leftaroundabout - good point, updated. I imagine sanity checks are useful  to many "theoretical" STEM fields as well (even if only a calculation / theory is being created), but maybe not in research outside of STEM.

@cag51 But if they're all giving bad results?  You may not have a lot of differentiating data in such a case.

Maybe. In my experience, I can usually suggest some simple tests that either uncover the problem (usually a bug or misunderstanding) or give some insight into why the idea didn't work out as we expected. But AI is a big place, it's possible there are some settings where you can't really do this.

"I always suggest the opposite: start with the simplest possible thing (e.g., 16x16 thumbnail images) and verify that that works as expected." This was my intuition, to train on a tiny subset of the data, to see how my model performs at that level. For some reason my thesis advisor instructed me not to do it. Eventually I spent months training for nothing. Now I'm stuck and in need to simplify my training as I wanted to do in the first place...

Yeah, the exact details of how you identify a relevant simple problem will vary widely. But at some level, you want to start with something that should definitely work, verify that it works, and then incrementally make changes and test them. Sometimes you might be doing something so novel that this is impossible, but in that case (1) it'll take an order of magnitude longer, and (2) you'll spend most of that extra time doing very tedious debugging, some of which might require some serious software skills that not all students have.

It's no surprise that many promising ideas don't work out. Indeed

Maybe this idea is simply not doable for the current task.

For a related story in mathematics, see What to do when you spend several months working on an idea that fails in a masters thesis?
"Machine learning" is a trending topic. Lots of smart people are thinking about it, and lots of students are signing up. There will be many deadend trails in the search for the few that really lead somewhere useful. Only you can decide whether you can tolerate the frustration natural in that search.

I agree with everything in cag51's answer. But I wanted to add that your own assessment may in fact be correct; "But now I feel that I can't cope with this level of anxiety and frustration." You are right, a PhD will likely be 4 more years of this. You might not enjoy that very much.
That's ok, you will be more than qualified to begin a myriad of PhD's in neighbouring fields. You are actually at a great stage to be having this realisation. Off the top of my head computational biology often gets people coming in from more computational fields to transfer knowledge. Biology degrees don't always include much background in either computational modelling or big data, so there is real demand for interdisciplinary talent. Also, I suppose it should be mentioned that you could always go into industry for a year, earn a much stronger salary, then come back to this decision. That is not an uncommon route.
If you do decide that on balance this is still the thing for you, then I agree with the advice in cag51's answer. You can make this work, it just might mean a slightly different strategy. And lots and lots of persistence.
On the other hand, I did a masters in physics theory. I did well in the masters, and got good PhD offers in theory, which was tempting. But in truth, doing research in theory was spoiling my enjoyment of physics. So I went and found a computational PhD and loved it. Now is the moment to work out what you want your next 4 years to be, because you are at a very unusual point in life when you are almost guaranteed to get what you chose.

It's all fine and well until

I can't help but feel that a failure of this project is a failure of me as a professional

Indeed, the subtle art of being in academia is digging golden nuggets of knowledge from otherwise "failing" (well, most of the time, anyway) projects. Most of the time, if the spec is detailed enough - e.g. "achieve X with Y precision on Z dataset by time T", it is met by the virtue of the "unknown" portion of the work being already done. That is, no one claims to finish a completely novel and creative work in a given timeframe. New ideas usually don't work and often many man-months are "wasted" on them... That is, until you realize the value there is the knowledge obtained in the process.
The issue is not specific to ML/AI, what changes is you don't have your expectations as high in many other fields; the level of hype around ML has research tied much closely to real world applications where people expect workable results, something that may take decades in more "traditional" fields.

Re: "if the spec is any detailed, meeting it is a largely retroactive effort": too many typos for me to guess what you mean here. s/as value/has value/.

Sorry, re-read it again and failed to spot typos. Probably rather poor wording instead.
(Acknowledge X as Y seems to be a legitimate usage, too - in this case, X=knowledge Y=value).
Sentence in question meant just retrofitting the spec to already obtained results to a large extent as opposed to coming up with novel results matching the desired spec.

I see what you mean re. X as Y.  But then Y is an adjective, such as 'valuable'.  Yes, I was struggling over the wording.

@Lodinn "is any detailed" doesn't make sense, or at least it's not idiomatic English.  Also "knowledge as value" - more natural to say "valuable"

@Matthew Not necessarily, at least, Cambridge gives an example of "Historians generally acknowledge her as a genius in her field". As a non-native speaker though, I appreciate your input and have amended (or at least attempted to) the answer.

Thank you as well @BlokeDownThePub. I don't get why you can say "is it any good?" but not "is it any detailed?", but at any rate, I'd better take it over to the relevant se or ask around instead :)

Yay. It’s improving!

Try these steps:

Go back to your basic assumptions (what modifications may yield improvement) and test them out one by one, not together.
Start with small modifications of the original state-of-the-art training process and only increase the amount (or amplitude, or sample size) of customizations gradually to see when the results start to deteriorate rather than improve.
Remember that when generating fully synthetic data, it may be difficult to obtain the same statistical properties as in real data - this is a common problem of data generation, and it may seriously harm the trained model performance (as measured on real data, of course). That's why it may be better to do augmentation instead: start from a real sample and disturb it in different ways, rather than create samples synthetically from scratch. With this approach, some of your ideas may still apply, and you will have a much bigger chance of actually improving the accuracy, because you can easily fine-tune how much of the perturbation is applied, going gradually from 0% to 100% and observing changes in the performance along the way.
Don't train on synthetic data alone, but combine a (larger) sample of synthetic data with a (smaller) sample of real data, to let the model observe and learn the less obvious real-world characteristics, ones that you didn't manage to replicate in a synthetic generation process. Optionally, you can also put a larger training weight on real samples to compensate for a smaller relative size of this subset.
Make sure that your evaluation setup is correct and that you're measuring what you really want to measure.

Last but not least, keep in mind that "research" is all about trying many different ideas and seeing most of them fail, just to spot one that actually works. This is particularly relevant for Deep Learning, where the complexity of algorithms is very large and still growing with new advances in the field. Sometimes, the best thing you can do is accept that your idea doesn't work and proceed to the next one. :D

This may not be the answer you (or other people) might want to hear, but I think the real reason is that the performance of an AI mostly depends on primarily the intelligence behind its design and secondly the resources given to it. For deep learning algorithms, this intelligence factor includes the intelligence behind the deep learning algorithm as well as the intelligence behind the heuristics used in encoding the input data in a way that the deep learning algorithms can perform well on that data.
If the previous solutions used by the company had a lot of intelligence behind them, likely due to being designed by intelligent people who imbued some of their intelligence into heuristics on which those solutions are based, they would be able to easily outperform any unintelligent application of deep learning to the problem.
There is no cheat for this; you cannot get artificial intelligence greater than what went into its design (after controlling for resources). So do not be afraid to try to invent your own heuristics based on your own intelligence and your current experience with the problem! Deep learning can be used to fine-tune your heuristics, or help you search for heuristics. Or you may use heuristics to adapt existing deep learning algorithms to your problem. Whatever it is, do not assume that deep learning can somehow create intelligence out from nothing.
The point is, there is something very wrong if every change you make seems to have no effect on performance. It strongly suggests that the deep learning algorithm is so swamped with noise that it is producing some kind of uniformly random results. And this situation is impossible if you were using heuristics, because using your own intelligence you can work through examples of bad performance and tweak the heuristics to make improvements!

In this field not many improvements are achieved with large changes. Often times, the current state of the art is very good. But that does not mean there is no room for improvement. Sometimes, these systems are not well optimized. Or in some cases there are a group of special cases that are more prone to failure. You may attack the problem from these angles.
For instance, if you believe the system is not well optimized, you may try to find more optimal parameters. Start with the current system and slowly add/remove more features, adjust parameters of the current system. Change one aspect at a time to see if any of these make any difference. The difference does not need to be large; smaller improvements can lead to more smaller improvements and at the end of the day you might end up with a significantly better optimized system.
For the second case, you must make carefully controlled experiments to find cases where failure is very common. Try to identify why they fail and integrate alternative detection methods to these cases. You might segment the feature space and if a sample falls into that space, you can use a different set of feature extraction or a classifier system to identify those samples.
Also do not constrain yourself to deep learning approaches only. Machine learning is a vast field with many methods. Try other classifiers or combine deep learning with classical techniques. Analyzing the distribution of the data can open up interesting insights. Do not shy away from formulating data distribution to solve for optimum classifier for the case. DL approaches are quite easy to set up to work well, but often times they fall behind to carefully curated classification systems. Many practitioners in this field think that it is a silver bullet to all problems, but that is not the case.

The idea of a Masters degree in AI is a pretty large reach for a university to award.  Most AI is pretty piecemeal and working on magic.
The truth is, despite marketing hype like Hadoop and such, there is no general solution to unstructured data except a full-blown AI solution to correlate data with experiences in the world.  Visual processing gives you structure along three dimensions (two spatial).  But, much like Edwin Abbott Abbott's Flatland, missing a dimension can force very large errors out of your decision-making process.
You can try to do your learning with more than these dimensions (adding another camera at an orthogonal angle, for example) -- that's one answer.  Or, you can implement a full-blown AI model which is ready to be developed in the world and uses a multi-layered, Markov model, for tracking all probabilities on all inputs to create excellent guesses, assuming your input data is ordered (and not noise).

Sorry to be blunt, but I am afraid your question has to nothing to do with machine learning or deep learning - or even computers at all for that matter.
Once you see that what is happening to you is what happens to anybody designing/performing/interpreting experiments, you'll be halfway through figuring out what is not working as expected, and how to make it work. Most importantly, you will understand if this is what you want for your career. In fact:

Before this I was sure that being a AI researcher was the career that
I wanted to follow. But now I feel that I can't cope with this level
of anxiety and frustration. If it is like this for a MSc project, I
wonder how it would be for a PhD.

Doing research is about hitting your head 90% of the time on things that do not work, and patting yourself the 10% of the time it actually works. "Hitting your head" typically means everything from questioning the underlying hypothesis being tested, to the assumptions of the test, to the experiment design, the data taking etc.etc.etc. The ability you need to grow is the methodology to systematically attack all those elemnts separately, and temper the frustration when you don't see the light at the end of the tunnel yet.


text:
How to answer 'Is your research going to be in biology text books one day?' question during faculty interview?
I am in the field of neuroscience/immunology, and I am applying for an academic group leader position. During most of my interviews so far I've been asked general questions about my fit into the department, funding acquisition, collaborations, how I would distinguish myself from my competitors, etc. Once, a person asked me the following question:

Is your research going to be in biology text books one day?

This is obviously a provocative question because none can tell upfront if their research will have such a strong impact on the scientific community; and speaking frankly, anyone making such a bold claim cannot be taken seriously. Of course you need to show that you feel confident with your topic and your research plan is promising and innovative. How did you answer weird questions like the one above during your faculty interviews?

I am a immunologist, not a futurologist, Jim!

As a sidenote, a variation of this question I have received in the past is "Can you see yourself have a Wikipedia entry eventually? What would it say?". Answering these questions pedantically that you can't predict the future ... also communicates something to the hiring committee, but it's probably not the message you want to send.

If you plan on writing a textbook and making students use it for your class, then why not?  I had a calculus teacher who did this...

@user4574 A book requires much more (time and work), than a couple of papers. As an example, the 416 pages of Modern Fortran by Milan Curcic is a work of 3 years, briefly described in Writing a technical book with Manning in 2020. The free excerpt Exploring Modern Fortran Basics and a tutorial video are very welcome, yet were not anticipated.

"Chances are -- if I get this job!"

Becoming the leading textbook example of unethical biology experiments is probably not what they are asking about, I presume?

The question is clearly not please accurately predict whether your research will be mentioned in standard graduate texts for biology students in the year 2050.
Rather, the question is presumably I am giving you an opportunity to explain how your research relates to topics of fundamental importance that can be appreciated by everyone in the field.
You can give an anodyne answer to the effect that nobody can predict the future and blah blah, but I imagine that the impression that this might create in the person asking this question is that your research, while valuable, is of a more specialized nature and its importance is limited to a particular subfield.

An anodyne answer is precisely not what OP should give. They can be assertive about the work they do and they should be, even if they can not vouch for textbook-level achievements.

To expand on this: an honest but substantive answer can draw a line from your own work to something textbook-level: “Realistically, my own current work on mutation rates in roadrunner feather mites is too specialised to directly appear in any textbook. But we hope it will help to resolve current uncertainties about roadrunners’ placement in the cuckoo family, and perhaps give new insights for parasite-based taxonomic methods more broadly. So it contributes to refining our overall taxonomic classification of birds and other vertebrates, as presented in textbooks now and in the future.”

These questions aren't only about the value of OP's subfield, they are also about ambition - answering "well, I don't think my research really goes to the level of standard textbooks" may sound a lot like OP doesn't really have ambitions beyond making reasonable contributions to their current sub-niche-in-a-niche. Which might well be true, but it's frequently not what a hiring committee wants to hear.

It's 2023, all of the low-hanging fruit has been picked. Pretty much everyone's work is limited to a particular subfield

@ScottishTapWater All the obvious low-hanging fruit has been picked. It is - retrospectively - quite surprising that much low-hanging fruit is seen only today. Some becomes only accessible with the right tools, but then is easy to pick, once identified.

@CaptainEmacs - To a point, but you have to admit that paradigm-changing research is less and less common due very much to the fact that our existing theories are getting closer and closer to describing base reality. By low-hanging-fruit I didn't mean it in the traditional easy sense, more things that have huge scope just by virtue of being fundamental to a field since the field was new

@ScottishTapWater That's what I used to think. But even without changes in the understanding of, say, fundamental laws of physics, there are things that really open up new fields of research and surprisingly so. I have seen some. And even in physics, new holes increasingly appear. I remind of Kelvin's “only two small clouds” [remaining] on the horizon of knowledge in physics. Keep your eyes peeled. Of course, not even the most brilliant scientist can hope for that. But every good scientist should be ready for that.

Maybe I'm not conveying my point well enough... I'm not trying to say physics is complete and we're done. Clearly there are huge holes in it that are going to need to be fixed (just look at gravity/qft). It's more the ratio of active researchers to big unsolved problems is quite small. The exception, as you've said, is new fields of research. However, even most "new fields" are really just subniches of what would have previously been considered already niche fields. That being said... I think I was just being grumpy and pessimistic yesterday even if I do somewhat believe the sentiment of it

On a similar tack I once read - possibly written by Hamming himself IIRC - that Hamming used to ask candidates: "What's the most important open question in your field?"  - followed by: "Why aren't you working on that?"  I believe that wasn't to tell the candidate he was lightweight by not tackling the most important question in his field but instead to give him an opportunity to explain why his interest was important and worth working on.

This question is similar to "where do you see yourself in five years?". It's an invitation to explain what is important to you. They are asking about your personal values and ambition. You state "Of course you need to show that you feel confident with your topic and your research plan is promising and innovative." but I don't think there are many wrong answers, as long as you are sincere.
Maybe you value making science aproachable; in that case, you could talk about how high-school biology text books are too convoluted and how you would use tiktok to teach biology to kids.
Maybe you want to do niche, theoretical research; in that case you could joke that you would pitty the poor post-grad student if they had to study your papers. Explain that you hope your real contribution will be elsewhere (medicine for example).
If you are engaged in risky experimental research, you might answer truthfully: "I hope so, but I knew I took a big risk when I started this line of research." The interviewer should appreciate your capability to critically self-reflect.
In short, this is a great question to get to know you as a person, as long as you are willing to open yourself up.

I like this answer (+1). "where do you see yourself in 5/15 years", even though I word it differently, allows me to let a candidate (Industry, not Academia) talk about their mid and long term goals. I specifically highlight the fact that this is not a tricky question (none are, which I also clearly state) and allows me to see my replacement ("your position"), or anything but my replacement ("whatever happens, not your position") or something in between ("manager") or something outside the typical path ("research in X"). If the candidate trusts you it is a wonderful way to get to know them.

(cont'd)  I had cases where the answer was "your position" (not that blunt of course) and all but two changed their mind when faced with the position (I was giving them opportunities to see how it is). The ones who stayed are now CTOs of large companies. The absolutely key point, however, is to make sure they know this is not the idiotic "what are your weaknesses" kind of question which mostly shows that the interviewer is really, really junior (or that the company has a fucked up hiring process)

"where do you see yourself in 5/15 years?" -- "In your seat."

"Where do you see yourself in 5 years" is a very common question during the interviews. I've heard of a guy interviewed for a postdoc position in our institute who replied: "I see myself as a full professor and winning the nobel prize"... he didn't get hired.

"I will work toward the end that it might, hoping that it does, but expecting that it probably won't. Prediction is hard, especially about the future."
What more can you say? But laugh when you say it.
Such questions require speculative answers. Be positive.

«One can certainly plan research, but not the results!» Seebach, D. Organic Synthesis—Where Now? Angew. Chem. Int. Ed. Engl. 1990, 29, 1320–1367. https://doi.org/10.1002/anie.199013201 (the author's copy) right on the first page.

Say something like: I have discovered what the co-receptor CD8 actually does in regulating the antigen-recognition pattern of the TCR; and this will undoubtedly enter the textbooks, but probably not under my name.

This is the type of question that can be used to test your type of personality and your abilities to frame your research in a larger picture.

Are you a specialist, or a generalist?
Do you think in the details, or do you think big?

Basically, Can you think of hypothetical outcomes that create a path of how your specific research might influence general textbook topics in the field? You are asked to speculate here, so do it as good as you can. Just don't overdo it and make sure you show your sense of reality, by framing it as a speculative possibility.
The right strategy is also case dependent. In general, when you apply not all chefs like generalists and people that think big, some times they just want a worker for the dirty details, a PhD/Postdoc who does exactly what they say without having too much initiative and ambitions. But sometimes they do want independent researchers who are able to write their own Funding proposals, and who can frame realistic research in a way such that it sounds important enough to get funding.
Probably it's best to be yourself, give your natural answer, but keep the context and interest of the other party in mind.

Answer: I don't know about textbooks, but I'm convinced that in 100 years' time, my research will feature in the high-school curriculum in whatever medium they'll be using at that time.
Background: When I was learning German at the end of the previous millennium, I was given some old German books from the 1930s and they happened to be university-level Chemistry text books and they contained Secondary-level education Chemistry by that time, and that was just 60 years, so yeah!
Note: My grandmother who gave me those books had no idea about the value nor their content; she only knew that they were German.