Stack Exchange Network
Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
Q&A for work
Connect and share knowledge within a single location that is structured and easy to search.
One of my student researchers is likely falsifying some results/data. What are the consequences?
Relevant info and background:
I'm an engineering Post Doc at an American university. One of my roles is to basically function as a 'project manager' for a couple projects that have a number of Graduate-level RAs working on them. I have a good relationship with all the RAs and all are hard-working.
I'm convinced that one of the graduate RAs is falsifying computational results/data (also called "rigging data" by many) in some cases. Note that the individual appears to be doing this for only some cases, not all. I have many reasons to believe this, but here is a few: (1) inability to replicate various results, (2) finishing the work at a pace I think is not feasible, (3) finishing his work at home where he surely does not have the software environment to actually complete the work. There are also other reasons I believe this to be the case, but you get the point. I'm also convinced this has occurred for over 1 semester, so I probably need to report this since I am responsible for overseeing all the work. However, the student in general is a good person and hard worker. He has passed the preliminary exams and is finished with all classes - I'd hate to see him expelled from the university since he's this far into the program.
I have some questions:
What do you think could be the maximum punishment for this grad student/researcher? I'd feel terrible if it resulted in expulsion. I would think that you would have to receive at least one warning from the university before an expulsion, except in very extreme cases. I'd be fine if this resulted in suspension, and even losing funding, but for anything more I'd feel bad. What is the standard maximum punishment for these cases? Also, what is the most likely punishment?
What is the punishment for me if I don't report this problem? For instance, say I just pretended ignorance. It is extremely unlikely I would do this, but it's worth asking.
How common is this? I would think this happens once in a while - a grad student decides to be lazy and fabricate a small portion of the overall results to avoid working the weekend or something. An experienced professional would know this is seriously wrong, but not necessarily a mid-level PhD student.
Any advice from people with experience in this, professors, grad students, principle investigators, etc would be great
- supervision
- research-misconduct
- 83 This is a very serious issue. If you are confident the student is falsifying data, you have an ethical and professional obligation to report them, even if it ruins their career. Frankly, if they are falsifying data, the worst possible thing would be for them to continue into an academic career: at some point, their deception will be found out, and then all of their work since they received their degree will be discredited and they will probably be permanently ostracized from the academic community. The consequences they would receive if reported now, however severe, would be less damaging. – Kevin Jun 1, 2016 at 23:13
- 20 ...Thinking about your question some more, though, it looks to me like while you have good reason to be concerned about the student's behavior, you don't have strong proof that the student is in fact falsifying data. I would move forward cautiously. I'm not very experienced (I only have my terminal masters degree), so I'm not entirely sure what to recommend doing. Perhaps you could meet with a more experienced researcher in your lab to discuss your concerns and get advice on what steps forward you should take? – Kevin Jun 2, 2016 at 0:38
- 75 The "finishing his work at home" thing doesn't read as very strong evidence to me, unless there's more to the situation that's not apparent. Lots of work can be done by remote access, using tools like SSH, remote desktop, VNC, LogMeIn, etc. I even know physical laboratory experimentalists that have full remote access to their equipment and sensors. Unless there's some unique resource necessary for this work, that's strictly inaccessible over a network, you would need to rule out actual use of such mechanisms. – Phil Miller Jun 2, 2016 at 0:49
- 29 You should probably address the non-reproducible results before anything else. The most common reason people make non-reproducible results is honest mistakes, but even if the mistake is honest, once you're aware something is wrong it's not really honest to publish the data as if you think it's true. Even if he's not intentionally falsifying results this is a problem in itself. – Owen Jun 2, 2016 at 10:01
- 9 Even if (1), (2) and (3) are correct, I would first assume (without knowing more specifics) that the student is working honestly but incorrectly (in line with @Owen's comment). Students, and even faculty, make mistakes all the time. I would express my concerns to the student about the correctness of their work, and discuss in detail what they did to figure out what exactly went on. – Kimball Jun 2, 2016 at 12:14
12 Answers 12
You have suspicions, but the evidence, as you sketch it here, is circumstantial. You need hard proof. Then you can (and must) act.
Falsifying data is a capital crime in academia. It wastes time, possibly years of other people's work. Don't let it get through. This person, if they indeed falsified data and would come through with this, will taint anybody and anything they had to do with - you, your group, your department, your university. Their results will be worthless, and so will be the degree you bestow on them.
You would feel sorry for that person if expulsed; but how sorry would you feel for a person who for 2 years will try to reproduce this grad student's results and fail for no fault of their own? How about their life and career? An honest mistake is one thing, but faking data? You are feeling sorry for the wrong person here; you'll spare the guilty and will let the innocent being impaled? A grad student is sufficiently mature to know better than to produce "synthetic" data.
How about the person abetting such a fabrication? Frankly, if caught, depending on the power structure that person may get away with a milder penalty "for not knowing what was going on", but in principle they should get the same, if not a harsher penalty, because they certainly cannot claim they didn't know that this is wrong; and they know the repercussions.
How common is it? Hard to say, but there were a number of large scandals (Jan Hendrik Schoen comes to mind), there is probably a halo of minor such attempts. From my own anecdotal stock: I once heard the conspiracy theory that spectroscopists would intentionally introduce "innocent" wrong factors into published formulas that could be interpreted as honest mistakes to prevent competitors from progressing. I didn't believe it, however, once I had to use such a formula from a paper, and to be satisfied I rederived it and some of its "brothers" myself in a tortuous process taking several weeks; lo and behold: I found that one of them had an integer factor wrong. It goes without saying that I have no real reason to assume it was intentional, but the conspiracy theory still lodges in the back of the mind.
Bottom line: if he really fakes data, letting this happen is not an option ; but the evidence must be carefully and (important for fairness to the accused) confidentially vetted to establish whether this is indeed the case.
- 42 Great answer, but I disagree with "you must have incontrovertible proof". It is perfectly fine and indeed desirable to report strong suspicions based on less-than-solid proof to the PI, the dept. chair, or anyone else who has the ability to investigate the case and determine if misconduct occurred. Of course, in that case, when reporting suspicions the OP would make clear that they are suspicions and may turn out to be wrong. My point is that when suspicions are strong enough there is an ethical duty to report them, just like there is a duty to report a strongly suspected crime to the police. – Dan Romik Jun 2, 2016 at 3:42
- 2 @DanRomik In principle, I agree with you: "incontrovertible" may be too strong. Still, the evidence must be carefully and - initially - confidentially vetted and should be sufficiently close to certainty - more than anywhere, reputation is central in science. And even if one is wrongly accused and thus wrongly perceived as falsifying data, that person's career will take a dive, whether deservedly or not. I think this is the case one needs to worry about, not about harshly treating someone who has provably falsified. The knife cuts both ways. – Captain Emacs Jun 2, 2016 at 11:32
- 15 why not talk to the student and communicate your concerns with him? Based on the OP's question, it seems like he does have very legitimate cause for concern. However, going over his head and getting the administration involved would be a step I would take AFTER communicating with the student and letting him know that his methods "don't seem as rigorous as expected." If he ignores OP's admonishment/advice, then OP has to do what he has to do. But OP is, in fact, the superior directly in charge. – sig_seg_v Jun 2, 2016 at 11:53
- 4 @CaptainEmacs thanks for agreeing. I agree that "the evidence must be carefully and initially vetted". That is the point of having an investigation, which is what will happen before the student can be punished and certainly before any misconduct is made public (if indeed it ever is). US universities have well-oiled machinery for carrying out such processes, so I see no reason for the vetting of the evidence to be done by OP. OP will of course aid in the investigation by providing information and expertise, but acting as an investigator is way beyond the scope of a postdoc's job. – Dan Romik Jun 2, 2016 at 14:38
- To summarize, I suggest changing or removing the last sentence of your answer to make your otherwise great answer more precise. – Dan Romik Jun 2, 2016 at 14:39
This misconduct is considered the ultimate misconduct in the research community. The offender is often stripped of his credentials and because of the tight knit nature of the scientific community, even if the credentials are not stripped the researcher may never find work as a researcher again. It will impact the ability to secure funding in the future.
If you are aware of it, as you claim to be, you can also be affected, ESPECIALLY if your name is on or associated with the paper. Additionally, if you are the one who secured the grant, this could backfire for you trying to secure grants in the future.
This is not common or uncommon, some people purposefully falsify data to support their hypothesis, but it is not always inaccurate. Sometimes researchers choose to only highlight some information and not other so that their hypothesis is supported and this is a more grey area.
BOTTOM LINE: If you know your student is falsifying data, then don't allow them to do so, for their career and for yours.
Communicate with the student. Let the student know your concerns.
The question seems rigged to determine what penalty may be appropriate, and how to kindly dish out the pain.
However, if we show good faith, then maybe we don't need to be quite as secretive. Say, "This resembles trouble. Here are the concerns." Then, if the student is innocent, the student may be able to explain things, and learn importance of proactively make things more clear so that suspicions don't grow into bigger problems than warranted.
If the student did do something wrong, maybe the student can correct things before they get further out of hand. The situation may be more correctable before more resources (including time) get spent on a road that may be wrong.
In education, the goal is often to help people do better. A common assumption is that people are typically inexperienced, and mistakes may be made. The goal isn't to try to maximize penalty for people who may be struggling with new skills. The goal is to try to get people in a good situation, including experience doing things desirably (including doing things properly, and successfully).
So, to re-cap this quite simply:
- if you're absolutely convinced that something is completely wrong, then go through the formal steps of handling such problems (reporting the issue, and whatever consequences follow through).
- (If this communication results in more trouble being discovered, be ready to shift over to the first bullet point, as needed.)
- 3 My concern here is that if the student has falsified data, your suggestion is to give them a heads up. This will enable them to adjust their methods to avoid being found out in future. Falsifying data is not a teachable moment or a mistake. It is deliberate and deeply immoral, and should quite rightly permanently taint a researcher who does it. – MJeffryes Jun 2, 2016 at 16:07
- 15 "I cannot reproduce the results you obtained. Please write down your methods and provide to me all tools required to reproduce." is not giving him a heads up to adjust his methods to avoid being found out in the future. It is either bringing him back on the right track (not falsifying data anymore, for fear of being found out), or it won't change a thing, and then you can still decide to go to the chair/dean/whatever. – Alexander Jun 2, 2016 at 17:46
- 6 @MJeffryes : Giving them a heads up is completely intentional. Attempting to keep actions secretive would be a more adversarial move, and I don't recommend that until you start to determine that adversarial actions are required. At this point, I'm recommending to take the friendly approach. The intended goal is helping to correct an apparent problem. If you declare enemies too quickly, you can eliminate some potential opportunities to still resolve things while on more friendly terms. Don't try to begin the punishment process before non-speculatively knowing the penalty is warranted. – TOOGAM Jun 3, 2016 at 2:08
- 1 I wonder why none of the other more voted answers opt for this. Why isn't talking with the student the first option? Somehow it's implicitly assumed that whatever he's done he'll continue doing regardless... – hjhjhj57 Jun 3, 2016 at 21:29
What do you think could be the maximum punishment for this grad student/researcher?
Whatever the maximum punishment is, that punishment has been decided by the people running the university. If you consider your university to be a reasonably well-functioning institution (and I would hope you feel this way about the place where you have decided to spend several years of your career), you need to remember that the people making such decisions have much, much more experience than you in handling all different kinds of academic misconduct. Thus, the punishment is likely to have been well-calibrated over many years and based on a large amount of cumulative experience. What makes you think that your personal judgment on this question is more wise or likely to be correct than such a body of accumulated knowledge and experience?
By not reporting your suspicions, you would essentially be saying "I know better than everyone else what needs to happen to this student, so I will usurp the institution's right to properly bring the student to account for his actions and just act based on my own gut feeling to save myself from the feeling of guilt over the punishment that the student would receive (even though any such punishment would be 100% the student's fault)." This line of thinking is simply wrong. The punishment is not, and shouldn't be, your decision. You have a duty to report the misconduct, and by not doing so you would be making yourself complicit in all its many potentially harmful consequences, which were described quite well in the other answers.
- 6 +1 for "I usurp the institutions' rights to bring the student to account". – Captain Emacs Jun 1, 2016 at 23:33
- 18 Completely disagree: rules on the institution level are indeed built on accumulated experience, but not necessarily with the interest of either science or the PI. Rather based on maximizing the interest of the institution under their legal, financial and political constraints (which can be opposite to the interest and values of the OP). – Dilworth Jun 1, 2016 at 23:43
- 3 @Dilworth in principle you may be right, which is why I added the caveat "If you consider your university to be a reasonably well-functioning institution ...". If OP has serious cause for concern that the university is staffed with incompetent or corrupt people, that might call for extra caution. However, the default assumption should be that large US universities have well-tested and reasonable procedures for handling misconduct. Thinking that one knows better than everyone else is a common human cognitive bias; in this case it would almost certainly be an incorrect assumption to make. – Dan Romik Jun 2, 2016 at 14:29
- 2 Even large US universities have interests that may directly oppose the interests of the OP. It is not a case where the OP and the university have both the same goal, in which it is correct to assume that the university knows better than him how to act. It is about the possibility of completely contradicting goals. – Dilworth Jun 3, 2016 at 14:29
You don't need to kick up a big fuss about it.
While it is definitely the case that any case of data fabrication is worthy of the levels of punishment it incurs in academia, it is not very clear that this is actually happening here. And in any case, the repercussions of scientific falsification should be very clear at any level, even for undergraduate students.
Inability to replicate results is extremely common in all scientific fields, and the overarching likelihood is that the analysis or experiments were carried out incorrectly for some reason. In the vast majority of cases, that is all there is to the story.
Simply deal with this problem as you would with any other inexplicable scientific result. Walk through the entire protocol, troubleshooting all potential issue spots, and exclude variables as required. In the extremely unlikely case that you find that the student was actually falsifying data, you must report it, but it seems unlikely to me that it is going to be the case.
If this person is falsifying data now, this person will continue to do so later as a PI. While you're sure to feel bad about it, science as a whole requires you to address the situation. When the public loses faith in science, we all suffer.
There are many ways to address this in a discreet manner (to ensure your intuition is accurate). Why not have this person walk you through the data/analysis step by step from ground zero?
- 2 How do you know the person will continue to do so later? – Jin Jun 1, 2016 at 22:20
- 4 You don't know in any absolute sense, but it is the least assumption. – dmckee --- ex-moderator kitten Jun 1, 2016 at 22:26
- 13 @Jin It is expensive to verify results (people do not get funds to reproduce known results); therefore, trust is absolutely central in science. If someone falsifies data once, he cannot be trusted anymore. Everything that person claims to find out, especially an expensive to produce result, needs to be independently verified anyway; their testimony is unreliable; so why waste attention and grants on them ever again? A person once caught taking a bit of money out of the cash register showed a "fluid" morals once, they won't be let handling the cash again. – Captain Emacs Jun 1, 2016 at 22:44
- 3 @Jin The only question is whether the OP has hard proof that the person falsified in the past . It is irrelevant whether they will do later - for which I explained the reason above. Also, usually universities will have procedures in place to punish such transgressions, but what the precise consequences are, will depend on the uni and cannot be answered on SE. But you asked "How one knows that the person will do it later?" - and what I am trying to say is: it's not relevant whether they will really continue this or not - only that the costs for everybody in the future will be as if they do. – Captain Emacs Jun 1, 2016 at 23:14
- 2 @Jin I would agree if it were just about you and Jack; but it's not. I thus fine-tune my example: Jack has stolen from you. You give him a dire warning. People know he has handled your money in the past (you didn't mention anything to them) and thus think he is honest. They leave their wallets lying on the table. Jack is around. Do you warn them off? – Captain Emacs Jun 1, 2016 at 23:50
In terms of immediate authority, I assume you and the student in question both ultimately report to a professor. I expect that professor is one or more of the PI on the supporting grant, the student's thesis advisor, and your supervisor. I really hope you have a strong, trusting relationship with this professor, for a few reasons:
- they will be the first line of investigation and response in dealing with this situation, and likely carry more personal/reputational and institutional responsibility for it than you do
- the student has likely worked with them longer than you have (postdoc there maybe 1-2 years, vs ABD student)
Basically, you don't want to end up in a position where your actions lead the professor to hold this against you. That could lead to withdrawn/non-renewed funding for you, withheld or weakened recommendations for future positions, and so forth. You really need the professor on board with the suspicions before any wheels of process start moving.
If there's some administrator responsible for this sort of issue that you know and trust not to jump the gun, you could potentially speak to them first to get your concerns on record before bringing them to the professor, to avoid the risk of the professor trying to sweep them under the rug and/or throw you under the bus.
Edit to add 1:
Ultimately, though, resolving this situation now, while the student is still pre-PhD, is in their best interest. If they aren't doing anything wrong, then they'll learn how to conduct their work in a more traceable, transparent, supportable, and reproducible manner. If they are, there's at least a chance that they can get straight without a permanent black mark on their career. Once they've gotten that degree, any such allegation could lead to it being revoked, grants they've received being suspended or cancelled, etc. This is the last point in their career where they can learn appropriate boundaries and reasonably hope to rehabilitate themselves.
I think three statements that you make are just your impressions and as you know that these are your impressions, you are not completly sure that student in question falsifies the data. Otherwise, I think you would not have asked the question here.
The best thing to do in order to be sure 100 % is to replicate all results with this student in your office on your computer. Otherwise, I think your statements are just your own impressions, without any solid evidence.
If you see that data is falsified, then you should report it.
- 1 +1 This answer addresses an important aspect of the question. Whereas in the lab sciences the experiments are never 100% reproducible due to inevitable small environmental factors, in the computational realm everything, if properly documented, should be able to be verified and reproduced. Even random algorithms like Monte-Carlo can be exactly reproduced, especially in the testing stage, by seeding the pseudo-RNG with the same seed everytime. So if you want to make sure the student is doing his work: just get the code from him and run it on your own computer. – Willie Wong Jun 2, 2016 at 13:25
- 2 @Willie Wong, Fully agree. The code that uses student would be useful to understand if there is any falsifaction. As in programming stuff, it is more difficult to understand the code of others than writing its own, I think it is better to reproduce all results with the student. By doing this, OP can understand also the methodology used and can verify the data used. Unfortunately, in some fields like economics, most of papers are not reproducible ; timeshighereducation.com/news/… – optimal control Jun 2, 2016 at 13:46
- @WillieWong, I have the impression that computational experiments are also prone to variation due to hard-to-control environmental factors, including software versions and initial states of random number generators. Avoiding these pitfalls should be possible, as you say, but it doesn't seem straightforward to me ( journals.plos.org/ploscompbiol/article?id=10.1371/… ). – Vectornaut Jun 2, 2016 at 17:11
- 1 @Vectornaut: initial states of random number generators can be controlled by properly seeding it as I wrote. See, for example, the documentation for the Julia language . Software versions can be documented; and if open-source software is used, the older versions can usually be tracked down in the appropriate repositories. Avoiding these pitfalls is in fact quite straightforward (and in fact the rules in your linked article make it even more so). Compare to the laboratory sciences the amount of documentation... – Willie Wong Jun 2, 2016 at 17:27
- 2 ... is not more than what would be expected to go into a lab notebook keeping track of the conditions underwhich experiments are run etc. The fact that some individuals find it "hard" is more an indication that some computational scientists are not given the appropriate data management training that typical laboratory scientists would be given. In this day and age it should be the responsibility of the head of the lab (either the professor or the lab manager) to hold the students accountable for good data management practices. – Willie Wong Jun 2, 2016 at 17:30
I agree with Captain Emacs 's answer, but there is something missing that I feel is important, namely:
Ask the RA directly whether he is fabricating any data, and while asking tell him why it is wrong to do so, and also that if he really does it and anyone finds out he can be expelled. At the same time tell him that at this juncture the best thing to do now is to redo all tests properly, meaning that he records all the random seeds used so that his data is completely reproducible.
After that it is likely that the problem will be resolved more or less satisfactorily, because it is generally difficult to write a program that looks normal and yet find a special random seed that causes it to have special behaviour. (It is possible but increasingly improbable for larger-scale tests.)
Falsifying data is a big no-no. It's on par with (and possibly worse than) plagiarism. It can ruin careers, and can lead to a whole host of huge problems (we don't need any more Andrew Wakefields). So if the student is doing this, you absolutely must report it and cannot feel bad.
That said, from the information you provided, there really isn't strong evidence. If I were on that jury, I would acquit without a second thought.
1) Working from home: Can he connect to a network to access the needed software? Can he run the program in the lab, get the raw data (say in a text file, or spreadsheet) take it home and do post processing/analysis there?
2) Not replicating data: I've written programs and ran simulations that performed beautifully and satisfied all the tests. But when I get to the group meeting, it fails. Why? Because I changed something that "wouldn't affect the results or the existing tests" (Ha!) between the time I originally got it working and the meeting. Or maybe an initial guess was changed. It might only take a few minutes to fix on my own, but in a meeting/high pressure environment I can't fix it right there. To me, that seems like a plausible explanation. (And I'm assuming there's no randomization in the code, I've had Monte Carlo approaches give significantly different results depending on the seed used).
3) Working faster than you expect: I see two possible explanations for this: a) The student is better than you think. b) The student is worse than you think. For (a), perhaps the student is able to crank out code fast, when he hits his stride and has a good mental map of where to go and how things should fit together (this "gunslinger" approach can be effective, but also can let bugs show up that make data replication difficult). Or he has written scripts to run several computations simultaneously or overnight. For (b), perhaps the student "hacks" everything in the code. Hardcodes things that should not be hardcoded, for example. Messes with things that shouldn't be messed with. This can give the illusion of working fast, but results in unmaintainable or inconsistent code, essentially borrowing time from the future.
Obviously, you have access to more information than we do, so perhaps these explanations don't apply. I would suggest talking to the student about the results, though not in an accusing way. Ask him to explain the results, explain what he did, and how he did it. Look through the code that he uses with him, make sure you both understand it. Perhaps there's honest mistakes to be corrected. Maybe there isn't a problem. If he seems to have no idea what he did or can't explain the procedure, then you probably want to bring up your concerns with the PI. But, under no circumstances can you let data falsification continue.
Tell the student that this doubt exists but in a one-on-one situation
To clear this doubt, ask him to make his results fully reproducible . It is in his own interest to show that he did not falsify anything. Show him that there is "immediate danger" that this gets investigated.
If he did not falsify anything (and your doubts were wrong), then this is a viable route. It requires some effort, but of course it can be done (and should be done, anyway). Then no damage is done, you only force him to work more transparently.
Allow him to redact work, if no harm has been done yet
This is the only "easy way out" that is in my opinion acceptable. In particular if nothing has been published outside of the university, you can allow him to redact falsified material, in order to replace it with real work. This may be punishment enough at this stage: It may set him back half a year towards graduation! But it may also require additional measures, depending on the severity.
He may then learn a key lesson here: while you may get away in highschool and maybe even undergrad, once the work gets more closely reviewed, misbehavior, copypasting and data fabrication is likely to be discovered, and this is not a good way of working. A backlash could come any time, and may ruin his reputation.
In the case that he admits cheating on this project, I would consider also reviewing earlier work, too.
If harm has been done, you want to redact anyway
If anything of this has been published yet, your name or your professors name is likely to appear on it, or at least be associated with it. In this case, you really will want to have this resolved...
At this point, it may be necessary for your own reputation to trigger a formal investigation; partially to clear yourself from any responsibility.
In a nutshell,
1) you must get a decent proof of misconduct, and
2) if/when you get it, you should report it immediately, even if this means the definite end of his career
Falsifying data is a cardinal sin for a scientist, and as many others remarked, it may waste years of work for other scientists try to replicate (and possibly improve) the faulty results. I was once caught in a situation where my student was trying to replicate somebody else's results, and it seriously impacted her PhD work, since the original authors' selective reporting (they later found that their solution works only in very limited cases, but did not share that finding until much later).
Now, before you rush to your superiors , I would advise you to talk to student about his methodology. Explain him that as (de-facto) project leader you have responsibility to guarantee that all results conform to scientific standards and that after you went through his work, you suspect there might be a problem with his results, as a result of unintentional mistakes or inexperience on his part. Start with this - if he made unintentional mistake (or even multiple mistakes), he will probably more than glad to learn from them and work hard to correct them. For a PhD student, this is sufficiently vague and yet serious that, if he is honest, he will work really hard to correct the problems (and redo the experiments). In that case it is your decision whether you trust him enough to have him on the team, and if you don't want, you should simply explain to your superiors that he does not fulfill your criteria, since he makes too many mistakes, and you do not need such people on the project, period.
I understand that this is a very difficult task for you, since you will have to waste your own time to go through his work and make sense of it, and probably you have your hands full with other work.
On the other hand, it may be pretty easy to spot if he is really dishonest or trying to hide something, because if he took a shortcut the first time by falsifying the results, I very much doubt he will "waste" his time correcting them - more likely he will try to weasel out or start making excuses, which then really means a red flag, and gives you a really good grounds to either confront him directly (usually it won't be necessary, as he will probably start digging his hole deeper and deeper) or just go and report him to the superiors. Because, if his mistakes were unintentional or result of carelessness but he does not feel he needs to correct them, he still deserves to be reported and sanctioned - refusing to learn from mistakes that others point out and refusing to correct them is almost as bad as falsifying data.
I do advise against going to superiors based only on a hunch, because an accusation of falsifying data may ruin his career even if he is not guilty, and further graduate students may become reluctant to work with you, fearing the same treatment (and some may even interpret your actions in a way that you got rid of competition down the road, which is the last thing you need).
And, since you are his superior, you are guilty if you do nothing, and the problems get discovered in his further career. You must act.
You must log in to answer this question.
Not the answer you're looking for browse other questions tagged ethics supervision research-misconduct ., hot network questions.
- Can campaign promises be enforced by a contract, or has it ever happened they were?
- How to lock the view to stay in camera view?
- c++ or Mathematica for large eigenvalue problem
- How is this function's assembly implementing the conditional?
- Integrating slower growing functions to find a faster growing function
- A trigonometric equation: how hard could it be?
- Cannot open an .mbox file with neomutt (although it works with mutt)
- Transformer with same size symbol meaning
- Build the first 6 letters of an Italian codice fiscale (tax identification number)
- Why does SQL-Server Management Studio change "Execute Query" into "Save Results"?
- Reducing the gap in the Lemma environment with enumerate
- When I move the bones and they bend, they can't bend where they can do it clearly
- NES Emulator in C
- A man is kidnapped by his future descendants and isolated his whole life to prevent a bad thing; they accidentally undo their own births
- Calculating Living Area on a Concentric Shellworld
- Which ability checks are rolled for a shove attack?
- Is the barrier to entry for mathematics research increasing, and is it at risk of becoming less accessible in the future?
- Dispute cancellation fees by 3rd-party airline booking
- Why does White castle into Black's attack in the King's Indian, and then run away afterwards?
- Does Japanese advertises selling something with full price?
- Why is the magnitude of the cross product equal to the parallelogram spanned by the two vectors?
- "Mandatory reservation" on Off-Peak ticket?
- Find the limit of an integral using Darboux/Riemann sums
- Why don't professors seem to use learning strategies like spaced repetition and note-taking?
PhD thesis and fake results on papers
I am nearly to the end of my PhD and I am probability writing this post when it is too late. The studies I did during these years were faked by my supervisor: he wrote two papers and he added a fake graph on each on of them. Now that I am at the end of this PhD, he said to me that I can write my thesis not necessarily based on the paper results, but I could just write my personal results as they are. I am doing a job like this, however there is my name (as first outhor!) on that two papers. The question is: even if I do not fake anything in my thesis, is there any possibility that the commission would ask and blame me some "strange" results resulting in that papers?
Hmm, this doesn't sound good. Any reason you decided to publish papers with fake results? This is an impossible situation really. 1. If you come clean and retract your papers, you probably won't get a PhD and will never work in Science again. 2. If you add these results to your thesis, it's likely no one will ever know that these results were fake and you can on as normal. 3. If you don't add the results to your thesis, people will definitely ask why eg in your viva, you could then say it was work that your supervisor did. It's your decision to make. It's too easy to say do the right thing (option 1) in this case, because there's a lot at stake for you personally, and you were probably manipulated in one way or another by your supervisor, so it's not entirely your fault. On the other hand, the integrity of Science depends on the integrity of its researchers. Whatever you choose to do, you need to distance yourself from this work and your supervisor as soon as you can.
I think I agree with ToL. Get away from that person and do not continue that terrible practice that he has taught you to do that utterly discredits science. If you're not coming clean then I think ToL's 3rd suggestion might be best. You'd have to prepare the answer about it being your supervisor's work. They will ask why you are first author so you need to have some response (I guess you could say you did most of the writing - if you did?). But can I ask why your supervisor has made this suggestion about not including those papers? I mean - why should he care if those papers are included in your thesis or not?
He knows that I can hardly discuss and defend a fake result like that ones he added on the two papers. For this reason he says to me that I do not necessary have to write down everything is reported on the papers. He could add the first fake result on the first of the two papers as at that time I just trusted him but, after a discussion, I realized how he figure out that result... Later on, he published a further paper, without asking me any authorization, I just realized I was author of a paper after it was already pubblished! I will not mension the second paper at all on my thesis. There is even more! He just published a third paper on my data, without let me know anything. And the name of the first outhor (which should be me!) is wrong! If someone ask me anything about that paper I will answer: "I don't know anything about that and I am not the author of that paper as that is not my name!" You can imagine the troubles he is giving me as I can hardly figure out a logically coherent test of about 100 pages...
I would seriously feel like exposing him and making him lose his career. But it isn't fair that you should suffer yourself (and it is likely that you would - eg by not getting your PhD and losing your own reputation). I think he knows this - which is how he has trapped you. Sort of like an abuse case - the abused person doesn't tell because they think they will be implicated somehow (and in this case - I think you would - because you made some mistakes eg by letting him publish in your name). How about this... - Can you exclude all the "fake" findings from your thesis - Successfully defend your thesis and be clear to the examiners that the papers not included (if they ask about them) aren't actually your work - even though they are in your name - Then once you have received your PhD on the basis of your work (none of which is made up) - just move away and start anew somewhere - never listing those papers as yours (and if anyone asks, say they were published in your name without your permission) This may be what ToL has already suggested - just written in a different format.
Quote From guaio1: He knows that I can hardly discuss and defend a fake result like that ones he added on the two papers. For this reason he says to me that I do not necessary have to write down everything is reported on the papers. I still don't understand this... I mean... how did you (or he) write a decent Discussion section if the "fake" results don't even make sense/can't be defended logically? I am not suggesting that you do include them in your thesis. I am just trying to make sense of the situation. Probably my ignorance comes from not knowing your field (and it is probably wise not to disclose it here!).
I just know that he provided as final proof of the phenomena a graph that is totally invented! Behind that graph there should be some further measurements and calculations, none ever did anything like that. And you can write down a small paper without engaging in details, but you can't write down a thesis on it! Long story short, I will write my thesis based on my personal considerations, that comprend a quite large introduction, a review of similar papers, and dissertations about my measurements. I can add only one of the three papers already published (the first one!), except the last graph, that I would substitude with some qualitative considerations. Other way I could include that graph, if someone ask me details about it, I can say that I wasn't the author of it (as he suggested me once long time ago) I still don't know whether this is possible or not... However, I am not that kind of person who fakes results and I alternate a kind of depression with intense writing and studies... That's what happen on a PhD like this
I would ensure that your thesis does not include anything faked. If there is any subsequent investigation into these papers then it's hard to make the case to take away your PhD if it's based on only the legitimate data. On the papers, frankly, if what you report is 100% accurate then there's a good chance, it's not the first time your supervisor has engaged in research misconduct. It seems pretty blatant for a first offence. Does your discipline make use of PubPeer? https://pubpeer.com/ It might be worth checking to see whether queries have been raised about any of these three papers or any other articles by him. Serial cheats tend to get found out, and then you risk being dragged in. So I'd consider once you have your PhD being that whistle-blower yourself. With that possibility in mind, you might want to consider whether there are ways to correct the scientific record without full retraction e.g. through a correction. It depends how egregious the fraud is. Even if you decide not to, make sure now that any email evidence / early versions of the papers, anything you have that points to your innocence is stored somewhere off the university network, so you can always access it. Final thought, if these papers have any potential to cause harm by not being retracted e.g. this is a Macchiarini type of issue http://www.bbc.co.uk/news/magazine-37311038 then I do think you have a moral duty to set the record straight.
Guaio1, how long have you known that these data are fake? Is it only recently or has it been for a while? If this matter came to light and it was being investigated or there was some form of review, you might be asked these questions. It is a serious ethical and practical dilemma you are in, and I agree with the others in that you do need to distance yourself from your supervisor and any false data and claims made. (Including ensuring your thesis does not rely on any of this data). Is there any way you might be able to get some legal advice on this issue?
I really feel for you Gauio. You are in a very difficult position. you have a few options at this stage and all involve a bit of risk. 1) include forged data in thesis and hope know one ever notices 2) write your thesis without fake data, and be prepared to explain why you don't have the additional graphs and analysis that were in papers in your thesis 3) just walk away now and start a new Phd somewhere else. so option 1; if you or your supervisor ever gets found out or investigated you will lose your Phd and your academic reputation. This could happen next year, 2 years time or 10 years time . you will never know and it is largely out of your control because it depends on careless and risky your supervisor decides to be in the future. You can move on and distance yourself from him, but if he proceeds with this line of conduct there is a chance he will get found out down the line. google Deidrick Staple_ he is a social psychologist who forged most of his data, was eventually found out and 10 Phd students who had over the years completed Phd with him got their Phd award taken off them. maybe he never gets found out; but there are a lot of ways he could. If he is too careless people will doubt his findings, if not now, maybe in the future (I think this is what happened to Staple) if he puts other students in your position, they may be forced to 'out' him. Option 2: this to me is a better option, less likely you will get your phd award taken away from you (if it is awarded). but here's the rub__ how do explain the difference between the papers and the thesis without arousing suspicion?? i don't know your field and maybe if your examiners haven't read your papers you could get away with it... but they could easily google you and find your papers when they are reading the thesis. so how do you explain it?? and if your arouse their suspicion, you will then be accused of including forged data in a published paper... I'm not even fully sure of the consequences of this but I would imagine its the end to academic career and you may not get your phd. Option 3: very drastic, but you start again and this time 4 years you have phd and nothing to worry about. response continued in second message
continued from abve How many years have you spent at this phd,now? If it's two or less, personally I would walk away for sure. I know even two years, they have been blood, sweat and tears years, but in grand scheme of things I would chalk it down to bad luck and walk away. If its more than two years the choice becomes difficult and you have a lot of soul searching to do. everyone is different and everyone has different motivations and reasons for wanting an academic career. personally I don't think any career is worth the kind of drama and turmoil this may bring. If it were me and I'm in year 5, I would still walk and just go do a practical masters and move on with my life . but factored into my decision is the fact that I don't love academia anymore, and wouldn't like all that stress of waiting to be found out, It would suck, but hopefully i could get a job to fund masters and maybe even talk to the university about allowing fee waiver for practical masters, given the circumstances.
The time spent on this PhD is already too much. I just kept going trying to figure out how to manage this issue and only now I've got the idea of writing this here.
Were those papers written right at the start of your PhD career/based on data from earlier on, eg from a masters? It is just that that could be a reason for not including them in your thesis. I know that there is a rule that says something like only work done for the PhD directly can be included in the thesis - eg not borrowing from your masters (or you would be getting double credit for it - which isn't allowed). Is this a loophole you could use? Or - were other people substantively involved - eg. other authors? I think if they had substantial involvement - eg. analysed the data (which it seems like your supervisor did)... then you wouldn't include it in your thesis as it wouldn't be entirely your work. Could this be the reason you give if asked about why they are not in your thesis? You could say it was entirely joint work - ie. 50% each if there are only you two as authors - so you couldn't claim full credit for it by including it in your thesis? The good thing is - if you don't include them in your thesis then if your supervisor's terrible behaviour ever comes to light and there is an investigation, then you would know your thesis is sound and you shouldn't lose your PhD. I still think that if you can get away with not including them in the thesis and have a strong and solid reason WHY, then you'll be OK. Because your thesis will be "clean". As someone else has said, you could be the whistleblower yourself. I think that is something to decide once you have your PhD and are confident that the content of your thesis is completely sound. Personally I would just make a new start elsewhere but make sure I kept evidence of everything to prove my innocence the best I could later if needed. The most important thing is to decide now is surely about your thesis. All the best
I second newlease36's advise. If possible, write your thesis excluding fake data. If asked, say that you only put in data if you contributed significant effort in generating it.
More consequences of fake data, read http://www.abc.net.au/news/2014-12-12/university-of-queensland-professor-on-fraud-charges/5964476
Post your reply
Postgraduate Forum
Masters Degrees
PhD Opportunities
Postgraduate Forum Copyright ©2024 All rights reserved
PostgraduateForum Is a trading name of FindAUniversity Ltd FindAUniversity Ltd, 77 Sidney St, Sheffield, S1 4RG, UK. Tel +44 (0) 114 268 4940 Fax: +44 (0) 114 268 5766
Welcome to the world's leading Postgraduate Forum
An active and supportive community.
Support and advice from your peers.
Your postgraduate questions answered.
Use your experience to help others.
Sign Up to Postgraduate Forum
Enter your email address below to get started with your forum account
Login to your account
Enter your username below to login to your account
Reset password
Please enter your username or email address to reset your password
An email has been sent to your email account along with instructions on how to reset your password. If you do not recieve your email, or have any futher problems accessing your account, then please contact our customer support.
or continue as guest
Postgrad Forum uses cookies to create a better experience for you
To ensure all features on our website work properly, your computer, tablet or mobile needs to accept cookies. Our cookies don’t store your personal information, but provide us with anonymous information about use of the website and help us recognise you so we can offer you services more relevant to you. For more information please read our privacy policy
Cookie Acknowledgement
This website uses cookies to collect information to improve your browsing experience. Please review our Privacy Statement for more information.
- Administration
- Toggle Search
- Find People
Understanding Misinformation: The Tale of Fake News and Fake Reviews
Type of degree.
Computer Science and Software Engineering
Misinformation has been long issues in the global communities because of the booming usage of social networks, online retail platforms and so on. The wide spreading of the massive amount of misinformation has recently become a global risk. Therefore, effective detection methods on misinformation is required to combat bad influence. In this dissertation study, we make the following three contributions by focusing on two types of misinformation detection, namely, fake news detection and fake review detection. The first contribution of this study is the fake news engagement and propagation path framework or FNEPP, in which we devise a novel fake news detection technique from a social-context perspective. The widespread fake news on social media has boosted the demand for reliable fake news detection techniques. Such dissemination of fake news can influence public opinions, allowing unscrupulous parties to control the outcomes of public events such as elections. More recently, a growing number of methods for detecting fake news have been proposed. Most of these approaches, however, have significant limitations in terms of timely detection of fake news. To facilitate early detection of fake news, we propose FNEPP - a unique framework that explicitly combines multiple social context perspectives like news contents, user engagements, user characteristics, and the news propagation path. The FNEPP framework orchestrates two collaborative modules - the engagement module and the propagation path module - as composite features. The engagement module captures news contents and user engagements, whereas the propagation path module learns global and local patterns of user characteristics and news dissemination patterns. The experimental results driven by the two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework. The second contribution of the dissertation lies in an emotion-aware fake review detection framework. Customers are increasingly relying on product reviews when making purchasing decisions. Fake reviews, on the other hand, obstruct the value of online reviews. Thus, automatic fake review detection is required. Previous research devoted most efforts on examining linguistic features, user behavior features, and other auxiliary features in fake review detection. Unfortunately, emotion aspects conveying in the reviews haven’t yet been well explored. After delving in the effective emotion representations mined from review text, we design and implement the emotion-aware fake review detection framework anchored on ensemble learning. The empirical study on the two real-world datasets confirms our model's performance on fake review detection. To investigate how people perceive fake and real reviews differently in terms of emotion aspects, we prepare 200 real product reviews and 200 fake reviews, and random assign 20 reviews to each participant to determine the level of authenticity, credibility, and believability based on 1 - 100 scale. The results from an LIWC-22 emotion analysis intuitively demonstrate people's perception on fake reviews from the aspect of emotions. The last contribution of the dissertation study is a two-tier text network analysis framework. As the global COVID-19 pandemic boosted the demand of online shopping, the number of online reviews increased dramatically on online shopping platforms. More often than not, customers have the tendency of referring to the product reviews before making buying decisions when products are not physically presented. Fake reviews are designed to influence buyers' purchasing decisions. Existing research devoted their efforts on designing automatic fake review detection systems; however, a text network analysis on fake reviews is missing. To close this technological gap, we construct a two-tier text network analysis framework guiding the investigation of the network-level characteristics and text characteristics of fake reviews. We conduct the extensive experiments driven by the Amazon product review dataset using Gephi. We unfold key findings on guiding the design of next-generation fake-review detection systems.
https://etd.auburn.edu//handle/10415/8328
Get the Reddit app
An online community for sharing academic works and discussion of issues and events relating to academia and the related political, economical, and social structures. This is NOT the place to ask questions about your homework, your particular school or professors, or to get admission advice! Survey posts must be approved by mods in advance, must include contact/IRB info, and must be specific to academia.
Publishing fake results ?
Is it possible to publish fake results in journals ? In my field most of the results are in the form of graphs like matlab generated plots and seems to me that in this case it’s easy to publish fake results. Cuz I have read some papers and their proposed Methodology and all seems convincing but there results seem to good to be true. Can reviewers actually figure out whether the results are fake or not ? Do false results get published ? If so, how often ?
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IMAGES
VIDEO
COMMENTS
I'm convinced that one of the graduate RAs is falsifying computational results/data (also called "rigging data" by many) in some cases. ... An experienced professional would know this is seriously wrong, but not necessarily a mid-level PhD student. Any advice from people with experience in this, professors, grad students, principle ...
This is an impossible situation really. 1. If you come clean and retract your papers, you probably won't get a PhD and will never work in Science again. 2. If you add these results to your thesis, it's likely no one will ever know that these results were fake and you can on as normal. 3.
My dissertation was mostly fake data and plagiarism. Remorse. [Remorse] I wonder how often this goes on out in academia? My thesis topic involved experimental research, but I could never get my setup worked out correctly, and to meet an initial deadline I just made up data rather than admit my experiment wasn't working. But then for the next ...
Altered data. More often, fraud involves adjustments to data to fulfil the desired results, rather than complete fabrication. Another fraud that took more than a decade to expose was the damaging work of Andrew Wakefield, a physician who, in 1998, published a study in the Lancet that showed a connection between autism and the measles-mumps-rubella vaccine.
While the proportion of those willing to use fake data "was not high, it is also not zero", he added, stating that most of the 36 Dutch academic leaders they also interviewed predicted that cheating would be unthinkable for PhD students. While the study was confined to the Netherlands and Belgium, Professor van de Schoot said he believed ...
Accused of fake data in PhD thesis! Post-PhD I finished my PhD 2 years back with a few publications in average IF journals. To the best of my knowledge, all my work is genuine. ... Now, folks in the lab (experimental biology lab) are unable to replicate it and I am being doubted on for faking results intentionally. Due to the rush in completing ...
If you cheated and want to share how you did it and why, please email Senior Staff Writer Allie Conti: [email protected]. Today we hear from a beleaguered small-town 40-something professor who ...
PhD Dissertation. Department. Computer Science and Software Engineering. Metadata ... The experimental results driven by the two real-world datasets demonstrate the effectiveness and efficiency of the proposed FNEPP framework. The second contribution of the dissertation lies in an emotion-aware fake review detection framework. Customers are ...
Thanks. There was a massive scandal of submitting fake results to Nature a few years ago in Japan. Reviewers caught them. The main author's PhD was stripped off because her thesis was also full of plagiarism. Her supervisor who was the director of the centre where she was employed at committed suicide and died.
AmVess on Dec 2, 2013 [-] He faked data in his graduate thesis, in applications for National Institutes of Health and American Heart Association grant, and in two published papers, so his behavior is part of a rather inexcusable pattern. Doctorate work is supposed to be difficult, and that's part of why it is the pinnacle of academic achievement.
The University Grants Commission has found three cases of plagiarism in writing PhD thesis, including two involving vice chancellors of different universities, in the past three years, the government told Parliament Thursday. The information was shared by Minister of State for Human Resource Development Satya Pal Singh in response to a written ...
Abstract. This article surveys the library and information science (LIS) response to the problems of. fake news and misinformation from the 2016 U.S. presidential election to the end of 2018 ...
Time to write your PhD thesis. This resource will take you through an eight-step plan for drafting your chapters and your thesis as a whole. Image. Organise your material. ... The results of the study demonstrate the benefits of young people's engagement in arts activities, both in and out of school, as well as the connections between the two ...
Several world leaders, including Vladimir Putin, stand accused of plagiarizing their PhD dissertations. Whether they resign, deny or ignore the allegations says a lot about the country they run.
Three cases of fake PhD thesis reported, two involved VCs: HRD. New Delhi: The University Grants Commission has found three cases of plagiarism in writing Ph.D thesis, including two involving vice ...
The experimental results show that our proposed framework, FNDF, can indeed identify fake news more effectively than the existing SOTA models, with 23.2% and 4.0% significant increases in F1 scores on the two tested datasets, respectively. ... Automatic fake news detection on Twitter. PhD thesis, University of Glasgow. Full text available as ...
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. ... The results of this project demonstrate the ability for machine learning to be useful in this task. We have built a model that catches many intuitive indications of real and fake news as well as an application that aids ...
WSDM Cup 2019 Fake News Challenge dataset, and the MM-COVID dataset. Experimen-tal results show that enriching the BERT language model with the BM25 scores can help the BERT model identify fake news significantly more accurately by 4.4%. Moreover, the abla-tion study on the end-to-end fake news detection framework, FNDF, shows that including the
Fake Results In Phd Thesis | Top Writers. First, you have to sign up, and then follow a simple 10-minute order process. In case you have any trouble signing up or completing the order, reach out to our 24/7 support team and they will resolve your concerns effectively. 100% Success rate.
We have thousands of returning clients that use our writing services every chance they get. We value your reputation, anonymity, and trust in us. 1 (888)499-5521. 1 (888)814-4206. Level: College, High School, University, Undergraduate, Master's. Essay, Coursework, Research paper, Discussion Board Post, Questions-Answers, Term paper, Case Study ...
Fake Results In Phd Thesis - ... High School, Master's, PHD, Undergraduate. User ID: 102506. Hi,I need an urgent assignment done. 4.8/5. User ID: 123019. 724 . Finished Papers. 535 . Finished Papers. Coursework Fake Results In Phd Thesis, Awesome Essays, Content Ghostwriters Websites Online, Student Finance Cover Letter, Research Paper Ideas ...
12Customer reviews. 4.8/5. ID 4595967. Finished paper. ASSIGNMENT. Fake Results In Phd Thesis. Kaylin G. REVIEWS HIRE. 1 (888)814-4206 1 (888)499-5521.