text:
Should I withdraw a paper from arXiv if it does not contain any new research?
I had submitted a copy of a reading-cum-review project to arXiv in my undergrad sophomore year without really thinking of the consequences. The paper was announced and eventually moved to the General Physics section.
The issue with the paper is that it does not contain any new research and the review is far from being exhaustive. Besides, it was typed in a very unprofessional manner with too few references for a review paper.
Now that I am a grad student and need to associate a real journal paper to my arXiv account, I was wondering if having such a paper on arXiv hurts reputation in the long run. Is it usual to have casual reports uploaded on arXiv, or is it advisable to withdraw it and start afresh?

You're actually asking several sub-questions:

Can I withdraw the paper?
Should a paper containing no research be un-published/withdrawn (regardless of venue)?
What should I do with this specific paper?

1. Papers can't quite be removed from ArXiv
You cannot completely remove a paper from ArXiv once it's been published there. The ArXiv withdrawal policy says:

Articles that have been announced and made public cannot be completely removed. However, you may submit a withdrawal notification for your article.

You can definitely submit such a notice if you decide you want to withdraw; here's an example of a withdrawn paper - where you see the withdrawal notification. But, again, the paper is still accessible, forever.
The example is taken from this answer by @ff524
2. "No new research" does not make a paper unworthy of publication
Many papers offer a different view of existing research findings; or consolidate, compare, contrast or compile other pre-existing results, hopefully making them more accessible to the reader.
Your other reasons for withdrawal seem more pertinent.
3. What should you do?
For the paper you described, you could write up a withdrawal notice; however - don't be too harsh on yourself in phrasing it. Mention that this was an undergraduate research project and that, in hindsight, it isn't sufficiently exhaustive.
But rather than a complete withdrawal, you could just change the description on the paper's web page on ArXiv, indicating the same thing, as a sort of a caveat.

Unfortunately after article announcement you cannot remove full record from arxiv. Further information can be found here.

If I understand correctly, withdrawing the paper would remove it from search results on arXiv. I guess that would be enough for me. Is there any obvious way to access the paper after withdrawal?

@Arole, withdrawing the paper does not remove it from search results. If you use the advanced search for "Comments" field containing "Withdrawn" then you will get plenty of papers, and you can verify that you can find them e.g. by searching for their title or author.

@PeterTaylor, thanks for demonstrating what "Withdraw" actually means. So the only use of withdrawing a paper that I can think of is to acknowledge a major error in the paper. I guess this makes withdrawing the paper quite meaningless in my case.

You can update the file, so the problems with the typing at least could be fixed. At the same time, add a note saying it is an undergrad project.
You cannot completely remove it, as mentioned in other answers. Withdrawing it suggests it's actively wrong, rather than simply not as good as it should be.

I can't imagine that this paper can hurt your reputation big time. Why would anyone bother as long as the paper is not wrong or obviously stupid? People frankly have better things to do than even read papers that are (apparently) not particularly interesting, not published, and of hardly any impact. I'd probably add a remark in the abstract that this was an undergraduate project and the review is too limited by your own present standards (not as a withdrawal note, just as information for people who read it if there are any), and leave it at that.


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Can the decision of a submitted manuscript of someone that already sent a paper which didn't pass plagiarism check be delayed by journals?
As I already shared in this question, I have submitted a paper in January
of the last year to a high-rank journal and the decision was "reject and resubmit". I worked very, very hard and prepared a significantly improved revised version of the manuscript with a near 50 pages response letter to the reviewers.
Unfortunately, the policy at my university is that the advisor must be the corresponding author. Two other students of my advisor--which I don't know personally--submitted a paper to the same journal a short time after the decision of my manuscript. After less than a week, the journal sent a letter to my advisor, which is also the corresponding author of their manuscript, that their paper didn't pass the plagiarism check. By my investigation, I found out that they copy-pasted some parts of their introductory paragraphs from other papers. My advisor sent a letter to EIC telling him that this problem was due to not having a plagiarism checker software. He is the second author in my paper and third author in the other paper and corresponding author for both.
I have now waited a long time for the decision of my manuscript. One of the professors of my university told me yesterday that they may not have sent the paper out to reviewers and might make you wait for a long long time. Then, if you mail them, they will reject your paper. It is a punishment for your advisor. Otherwise, they will make you wait for 12 to 15 months just for punishment and then reject it without review .
It was a nightmare for me as I am under immense pressure for my job and my graduation. I agree that my advisor is an irresponsible and ignorant person. But I didn't do anything wrong. Just very very hard honest work.
Can they delay the decision of a submitted manuscript of someone that already sent a paper which didn't pass the plagiarism check? Is there any such policiy or behavior in journals?
What should I do?

Wait. You wrote a 50 page response to the reviewers? How long is the paper itself?

@It is 14 pages although highly compressed.

I know of no such policy - but editors are humans, too. On the other hand, an editor has to deal iwth hunderts of submissions per year. Plagiarism check is usually done automatically. At least I would not have the mental capabilities for remebering those issues and playing such games. But you never know.
Rejecting a paper without review after a year of letting it sit and then returning it without review would be malpractice and a reason to complain about the editor behaviour.

"Rejecting a paper without review" is not malpractice and called "desk rejection". "Rejecting a paper after a year of letting it sit and then returning it without review" is malpractice. Please fix.

@CaptainEmacs yes, I fully agree! I tought the context narrowed down the answer but you are right, a clarification helps.

The people who run good journals are (i) professionals, (ii) people. From the first follows that it's not professional or appropriate to sit on a paper for a year without sending it out to reviewers or making a decision as punishment. That's just not ethical. From the second it follows that the editor is probably not going to bend over backward to ensure the paper gets dealt with today instead of tomorrow. In any case, your characterization of editors as these kinds of vindictive monsters out to get you is incorrect -- that's not the kind of person who would be appointed as an editor.
Either way, there is nothing wrong with writing an email to the editor and asking what the status of a paper is. That happens every day, and it's part of the job of an editor to deal with this in a professional manner. So go ahead.

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Rules for affiliation for student doing unpaid research in their free time?
Lets say an undergraduate student does some research in their free time and wants to write a paper about the findings. What are the rules regarding affiliation when the student tries to publish?

The student is required to include the university as affiliation, because they are enrolled in a program at the university
The student is not allowed to include the university as affiliation, because they are not officially hired/approved to do research under the name of the university
There are no rules, the student can choose
...?

I guess for graduate students / postdocs / professors it is mandatory to include the university as affiliation, as they get paid by the university to do the research they are doing!?

I don't think there are any "official rules".  (I can't even find a clause in my employment contract that officially requires me to list my university on my papers.)
But as long as you are a student, it's a good idea to list your university as an affiliation.  Even if the university isn't paying you, you do benefit indirectly from the intellectual environment and resources that the university provides: professors, fellow students, library, internet, health insurance, nearby coffee shops, and so on.  It costs you nothing to be generous.  Also, for better or worse, readers will take your paper more seriously.

I don't have a clause in my contract either, but I have had a PI insist on adding their group to my affiliation on a paper submitted before I joined the group, because I had spent time responding to reviewers while under their employ and therefore they funded part of the time spent on it. Sometimes, it comes down less to official policy and more on what the boss says.

The student should talk with their department and let a professor advise, acting on behalf of their Institution. This would be to help the Department find interesting work by students as well as helping the student with professional advice.
In any case, they can list the university they are enrolled in without suggesting that it was sponsored by the university. -- unless the University has explicitly set policies otherwise (for some boneheaded reason).

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Handling computer files in simulation-based research with multiple storage drives
Can somebody give me an advice about how to efficiently handle data files, source codes, documents, and manuscripts of research projects based on numerical simulations? My problem is that I use multiple computers while the maily-used one has limited storage space, and that the files and directories tend to scatter over multiple storage devices without being synchronized if I don't take much effort of manually deciding which ones should be stored where and remembering the decisions. If there are some people under similar situation described below as me, I wonder if they could share their detailed know-hows.
I work on simulation-based research using multiple computing environments (a laptop computer, desktop computers at work and home, as well as computing clusters at work) to write program codes, run the jobs, analyze the output data and make figures, and write manuscripts. The OS is exclusively Linux so far. I don't have a stable job, and I need to move from one place to the next every now and then.
Most of my human time is spent on my laptop by writing codes and making plots from the output files of simulations. However, this laptop has a limited amount of storage space (about 500 GB). Sometimes one project generates 100 GB or more output data in total before publishing a manuscript, and I work on multiple projects in parallel. I often look at my old source codes, analysis scripts, and examine output data files of finished or frozen projects. Therefore, it would be the most convenient if I could collect all the files related to all the current and past projects on the laptop, but this is not possible due to the limited storage space.
I tried different schemes in the past but have always been frustrated by the difficulty to keep files consistent over multiple storage devices (internal and external hard disk drives on the multiple computers). By consistent, I mean that those files that are intended to be present on multiple drives should be exactly the same and up-to-date with the latest version, while other files should be present only on a spacious drives.
It's important for me to keep a set of source codes, input data files, raw output files of simulation runs, analysis and plotting scripts, and output files of the analysis for each project in an accessible way, in order to keep myself accountable for my published results as well as to accelerate future research based on the past projects.
In the last few years, I have used git to version control my source codes and keep them synchronized over multiple computers. I also used unison to synchronize data files between my laptop, a USB hard disk drive, and a desktop computer, to some extent. I like both of these tools, and I'm wondering if I can make a more efficient work flow with more extensive use of them. I also use rsync to fetch and push files from one computer to another manually.
I have the following hypothetical directory tree as a collection of all of my projects:
projects/ (about a few TB)
  - projA/ (about a few 100 GB)
    + deploy/
    + dvlp/
      + stable/
        + .git/
        + source/
        + doc/
        + tests/
        + bin/
      + sandbox/
        + debug000
        + ..
        + debug999
    + jobs_v000_machineA/
    + jobs_v000_machineB/
    - jobs_v001_machineA/ (about 100 GB)
        + source/
        + doc/
        + tests/
        + bin/
        - job000/ (about 10 GB)
          - input.dat
          - input_moo.dat
          - out_foo000.dat
          - out_bar000.dat
          - ..
          + graph000/ (about 10 MB)
          + graph001/
        + job001/
        + ..
        + job099/
  - projB/
  ..
  - projQ/

but it's hypothetical at the moment because any of the storage drives doesn't completely mirror this entire tree. On one drive (an older USB Hard disk drive), the actual tree is
projects/
  - projA/
  - ..
  - projN/

while on another drive (an internal disk on a computing machine) it is like
projects/
  - projL/
  - ..
  - projQ/
    - deploy/
    - jobs_v001_machineA/
        + source/
        + doc/
        + tests/
        + bin/
      - job000/
          - input.dat
          - input_moo.dat
          - out_foo000.dat
          - ..
          - out_foo320.dat
      + job001/

Yet on another drive (an internal disk on the laptop), it is like
projects/
  - projL/
  - ..
  - projQ/
    - deploy/
    - dvlp/
      + stable/
      + sandbox/
    - jobs_v001_machineA/
        + source/
        + doc/
        + tests/
        + bin/
      - job000/
          - input.dat
          - input_moo.dat
          - out_foo000.dat
          - ..
          - out_foo320.dat
        + graph000/
        + graph001/
      - job001/

I can buy an external USB hard disk drive of a few TB capacity to store the entire projects/ tree now. But, still I want to have an efficient and reliable way to partially synchronize an appropriate subtree of it with each computers.
I saw the following related questions, but they do not address exactly my concerns.

Archiving papers, simulation and experimental data, etc?

This case does not involve a large data that doesn't fit in one of the computer.

Organizing data and files

This post does not seem to be concerned about files being scattered over multiple computers.

I found the following tool, but it's an overkill for me.

Pegasus, a Workflow Management System for Science Automation

I will be spending more time to learn how to use the tool than writing codes.

I would appreciate your advices.

Have you considered rsync? And a much bigger internal harddrive sounds like a good investment.

BTW, this may be more on-topic on the software-recommendations stack.

I'm voting to close this question as off-topic because it should me migrated to software-rec.se

Also may be appropriate scicomp.stackexchange

@henning Yes, as I wrote, I am using rsync.

@norio Sorry, I missed that (your question is quite detailed). Why does rsync not solve your problem? Is it because you have to think about running it regularly, because you would like to sync the entire tree but only have space for a subtree, or because of something else?

@henning I doubt that there is a single software that automates the workflow. I want to know how other researchers make their day-to-day workflow, what they identified as essential requirements, and how they have addressed. That is to say, I want to get the know-how of how to combine several types of software effectively.

@henning A problem with rsync is that it's a one-way mirroring. If I change a file on two machines and run rsync, I lose one of them, for example. So, I always need to remember to not to work under some path on some machine. As another example, if I change a file on machineA and rsync machineB:file machineA:file by mistake, I lose the change. So, I always need to remember which machine has the latest version of each file before running rsync. This task takes up much mental energy.

Store the files on one powerful machine that has lots of disk space. Set up backups on that machine for disaster recovery. When you're working on another machine, instead of processing files locally, use a remote access tool such as TeamViewer or NoMachine to remote into your main machine and work with the data so the data always stays in one place. For source code, use git and set up a remote repository.

Isn't it a question for the superuser.SE? I'd use LLVM or something (ZFS?) to create a single tree over multiple drives.

Use cloud storage.
There are several options out there. GitHub for source code. Microsoft OneDrive for office files. Amazon Web Services for databases or if you feel like setting up a more customized solution.

Thanks for the suggestions, but this doesn't seem to solve the problem of files scattered all over. I want to know a systematic way that my laptop is sync with all of these cloud storage only for a necessary subtree.

OneDrive allows that.

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German Basic Law and Admissions
My question is mainly a follow-up on this answer.
I understand and know that it is unconstitutional in Germany to restrict access to universities without necessity and that it is hard for a university to prove that it can only accept 132 students next semester and not 133.
On the other hand, the following two things are not challenged by the courts

fail rates of 80-90% in the first semester, often encountered in mathematics. One could argue that is also a restriction on the "freedom of profession" because the courses and exams are too difficult and it is not necessary to fail that many students.
failure to provide the necessary funds. The states could easily enlarge the study programs e.g. for medicine by giving more money to the universities. But I never heard that a court ordered another million EUR for a university so that the university can accept all students that want to come.

So my question is: What exactly is the legal idea of "freedom of profession" and how does it apply to universities?

Maybe a better question for the law stack exchange?

I consider this too broad as the answer can be subject to an entire legal dissertation. I think useful ways of narrowing this down would be focussing on one of your particular examples (but then I concur that [law.se] is better suited) or focussing on what are the practical effects for somebody involved in teaching at a university.

Not an answer, but it might be worthwhile to mention that restrictions regarding the fail rates in exams might conflict with the following part of article 5(3) of the constitution: "Kunst und Wissenschaft, Forschung und Lehre sind frei" (my translation: "The arts, sciences, research and teaching are free"). This is typically interpreted in a way that includes giving a lot of leeway to individual professors in teaching. [...]

[...] So even if one interprets high failure rates to be in conflict with the freedom of profession (article 12(1)), this might constitute of a conflict between two rights granted by the constitution (which is, of course, a very common phenomenon, but probably makes the situation much more complicated from a legal point of view).

In fact, much of this is covered by this recent Law.SE post.

Info for readers not familiar with the idiosyncracies of German constitutional law: the "German Basic Law" is simply the German constitution (which, for historical and political reaons, was called "Basic Law" in 1949 and has kept this name since then). Link to the text (in German): Grundgesetz

"One could argue that is also a restriction on the "freedom of profession" because the courses and exams are too difficult and it is not necessary to fail that many students." The freedom to choose ones own profession does not preclude the requirement of proper qualifications. If you cannot attain those qualifications, you cannot work in that profession. That is completely constitutional.

Arguing that in court will be a very hard sell, because one would need to argue that having such high failures rates on a large scale is indicative of a unnecessary filtering to prevent students from doing their needed professions, while arguing that it is unreasonable to use the complaint procedures in place for the individual exams. Oh, and while at the same time, students could switch to a university of applied science which are said to be somewhat easier to graduate from in maths, which also confer legally valid B.Sc. degrees. And all that on top on the "grundgesetzlich ...

... garantierte Freiheit der Forschung und Lehre" which gives professors a lot of freedom which can essentially only be overruled (in the respective committee) if they behave in an unreasonable way, which needs to be proven. Hard to imagine defending such a claim in court.

@Polygnome But couldn't it be unconstitutional to define unreasonable "proper qualifications"? I mean, if I would require a law student to know every law by heart to graduate, couldn't she maybe argue that this requirement to become a lawyer is unreasonable?

@JFabianMeier Sure, you can  try to argue that. But the bar for lawyers is already quite high. You can make every argument if you input silly parameters like "knowing everything". That is not how reality works, though. The law explicitly says that the right to choose ones profession is subordinate to further regulation, including qualifying exams.

@Polygnome I mean, the courts are already playing silly games in telling universities that they have not proven that it is impossible to take one further student for medicine, opening up the door for rich people who sue several universities until they succeed.

@JFabianMeier: In this case it seems like the people who sue are playing silly games, not the courts. (It is, by definition, the purpose of a court to judge situations where people find themselves unable to agree.)

@JochenGlueck A reasonable court would decide that the maximal number of students to admit can only be an estimate. The burden of proof that lies on the universities is obviously silly.

Courts will be very reluctant to assume a problem solving competency over the experts in the field. In your instance, prescribing to Mathematicians what basic competency is for a B.A. in Mathematics is left to the Mathematicians to define. Finding a way how to provide them is also beyond the courts competency, especially since there is a constitutional right to freedom of teaching. Besides, university mathematicians will claim bad preparation and just blame the high schools for not developing Mathematical skills with resulting bad numbers in the first classes. How could a court decide that since Mathematical skills are not developed sufficiently in the Gymnasium, the minimum competency of professional mathematicians would have to be reduced. This would violate the constitutional rights of all who have to rely on the certification.
Constitutional rights are never absolute because sometimes nature restricts them. Blind people cannot obtain driver's licenses and Germany has never recognized my right to a profession as the nation's teenage girl heartthrob.

There is a misconception here. The law is in place to give everyone who has the aptitude for a certain profession the opportunity to become that profession---and not just those that went to specific schools, have more money, have connections, etc. It is not meant in a way that everyone can become everything, even though they aren't able to do it (for wathever reason).
If it were like that, all professions would have to be so easy to learn that every conceivable person could learn it, which would completely devaluate any profession. I wouldn't like to live in a house planned by an engenieer who only learned the most basic of math so that noone failed the test, build by a mason whith two left hands with a color concept designed by a colorblind interior designer and then when said house comes crashing down on me I would not want to got to the hospital where the doctors did'nt have to learn all the bones in body and all the diseases, because otherwise not everyone would have passed the test.


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Backing out of accepted post-doc position in favor of a permanent faculty position
I got a PhD in mathematics three years ago, and am currently a post-doc. My appointment is due to finish this August, so I am now looking for a new job.
My dream job would be a permanent faculty position in my home country. The problem is that in my home country, the recruitment calendar is considerably later than the international average: if an offer comes, it could be in mid- to late May (most likely) or possibly even June (if I have to wait for people above me in the shortlist to step aside). Also, the competition is very tough; even though my CV (as far as I can judge) seems fairly good, there is a real possibility that I will not get any such position this year.
If I cannot get a permanent position, I would like to do a second post-doc (somewhere in Europe). But the calendar for post-docs, on the other hand, is much earlier: based on my experience so far, it seems that the vast majority of offers that I could get would have a reply deadline somewhere in February.
All of this puts me in a tight spot:

either I could sit idle until the permanent position interviews; and only if I fail all of them, start looking for a post-doc. But the chances of finding, in June, a post-doc to start in September seem fairly slim. If I did this, I am genuinely scared I would end up as a high-school teacher.
or I could apply to post-docs now, and accept one of the offers without telling them anything; then quietly go to the permanent position interviews, and if I pass one of them, renege on the post-doc I had already accepted and take the permanent position instead. But this raises obvious ethical issues.

Of course backing out of a commitment on a job A to take a better offer B is usually frowned upon. However I think I heard people saying that the case when A is a post-doc and B is a permanent position is exceptional, and that in this case such behaviour can be forgiven. Still, I would feel more comfortable if I had more opinions about this. If you were in this situation, what would you do?
Anticipating some objections:

Applying to post-docs now, all while being upfront with them about my parallel permanent position search, seems more or less equivalent to not applying at all. In fact there is already a post-doc position that I failed to get for this very reason (this position actually was in my home country, so I could not hide anything from them).
From what I have heard, requests to postpone the permanent position's starting date by a year so that I could do my post-doc are usually not granted (since they need someone to teach their classes).

(This question: Accepted post-doc and have subsequently received offers for full time faculty position - quandary is similar, but their exact situation seems somewhat different.)

Have you talked to other people from your country who must have gone through the same situation?  Is it extremely unusual for people from your country to do postdocs abroad?

One option is accepting another postdoc and still applying. Often, many permanent posts are happy pushing the start date back a year. You could do one year at the new postdoc and then start the permanent position the following year?

@Noah Snyder: Yes I have talked to some of them, though not a lot. Doing postdocs abroad is certainly common; only exceptionally strong or exceptionally lucky people can get a position right after completing their PhD. I have gotten the impression that the standard procedure is to apply anyway, then back out as needed. But I would like to hear an opinion on this from the post-doc employers' perspective.
@T K: from what I have heard, among the permanent positions I am applying to, most do not accept such requests. Apparently they cannot afford to have an unfilled teaching role for a year.

One of the points to bear in mind is that you'll be forcing the person who would have gotten the post-doc in your stead to plan for not having the position - which might be quite difficult for them.

First of all, at least in my field (CS) there are no fixed or preferred dates for hiring post-docs or faculty members. Positions are open when someone retires (in case of faculty positions) or an institution recieves funding to finance a post-doc position. But it might be different in your home country and in mathematics. But let's take the situation description as given and proceed from that.
For every institution in academia it is clear, that post-doc positions are for further qualification (as long as you don't have many permanent post-doc positions), so it is very natural for post-docs to leave as soon as they are having a chance for a faculty position. In fact it is even a honour, if someone from your group recieves a faculty position.
On the other hand, there are things to do for a post-doc and it is not helpful, if someone leaves a few month after starting. I would assume that this will be a point in your interviews for post-doc positions! As I saif: people are aware of your situation.
So in my opinion, the best way would be to

be honest about your intentions in the post-doc interviews. People want to hire people who want to achieve something and are eager to work hard for a goal - so your ambitions for a faculty position are a strong omtivator to do the job right!
Give them a perspective which is longer then 3 month. Offer for example at least one year of your time, and try to postpone the start of the faculty position.

If the faculty position is not offering such flexibility, you will have to have a difficult conversation about why you want to quit earlier, but in the long run, everybody will understand.

Of course, I am not happy for myself if my postdoc leaves before starting. But, of course, if I am a sensible employer, I will be happy for them (and having confirmed to have a meritocratic interview process)

I would not say it is a major issue to back up from a postdoc position for a permanent position. It is a job market after all. However, as pointed out above, you have to be honest with a prospective mentor that there may be a possibility that you get a more attractive offer that you would accept.
I was somehow in a similar position after I completed a Phd in mathematics in France. In France, offers for permanent positions are also made in May up to June. I started a two years postdoc in Austria in January, but got an offer for my dream position in May. So, I accepted the offer from France and  backed up from the postdoc position in Austria after 6 months. There were no issues at all. They were able to use the remaining funds to hire a new postdoc. In my field, specially if the "postdoc money" comes from a grant, it is easy to reopen a position if a postdoc accepts another offer.  However, if the money comes from the University, it may be more difficult for the mentor, hence the importance of being honest.


text:
Can I list the published papers that I did intern work for even if I'm not an author
I am applying to grad school for a Masters in Environmental Science. (My BS is in Biology). I interned throughout high school and college for NIH. I did the basic lab tech type of work, but there were 2 papers that were published which I helped work on. Obviously, I am not an author on either of those papers, however I think they show the kind of work of which I have been a part. I can't find any format for listing this type of thing, so perhaps I should exclude the reference completely? However, I went to a CV writing workshop and was advised that any papers I contributed to in any way should be listed. I would appreciate any advice on this subject. Thank you.

Do those papers contain written acknowledgements of your help?

However, I went to a CV writing workshop and was advised that any papers I contributed to in any way should be listed.

That's bad advice for an academic CV.  You only list papers on which you are an author.  I haven't ever seen exceptions to this rule - if you list them it will at best be confusing, and at worst seen as fraudulent.
You can certainly list your job as research experience, and in any graduate application, you can and should explain in more detail what you did and how it contributed to published research.  You should also ask your supervisor or PI to write a letter of recommendation which explains your contributions and their importance, and how it shows your preparation for graduate study.

+1 for "ask your supervisor or PI to write a letter of recommendation which explains your contributions". I think this is the best you can get out of your work if you are not an author.

If you are very early in your career and you have very few or no actual authored papers, you could possibly list them so long as it was abundantly clear these were "outputs from projects I contributed to", and that you're not claiming authorship.


text:
Where to add citation when having several paragraphs from the same source(s)?
So, if I have three paragraphs, inspired and even having copied sections from the same source, do I add citation at the end of each paragraph or at the end of the third or where else?

is it a consecutive sequence of three paragraphs that you're quoting ? are you quoting them in the same place ?

yes, the paragraphs are one after the other and the two sources are used for all of them...

actually there is one source... the second is also inspired from the first

Related: http://academia.stackexchange.com/q/8804/2692

When you have such a substantial chunk of information attributable to one source it can be useful to start the text by simply declaring the source. This can be done in the following way (exact formulation can of course be changed; I chose Harvard style referencing as an example):

The following [x] paragraphs follow Name (yyyy) unless stated otherwise.

The formulation can be changed to better blend in with the text by adding details about what the information is about  or why this source is good or should be repeated, or both. The "unless stated otherwise" can be omitted if you have no need to add other references in these paragraphs; I added it for completeness.


text:
Project management techniques applied to research
Are there books/writing on project management as applied to scientific research (for PhD students and higher)?
I am looking for something synonymous to Scott Berkun's "Making Things Happen", which was written for software developers.
Other interesting and useful practices I am familiar with include Agile and Extreme Programming. Maybe I am looking for sources on comparing research process to product development.
Just to be clear, this is not question about software, but about practices.

I've found thinking about my research lab as a startup company to be useful.  The lean startup is an well known example that helped shape how I manage my projects.

I am not sure but I think the Manhatan project was under project managment regulation, and it was a research.

A number of things spring to mind.  The first is communication of goals and time lines, and I'd just point to Gantt and PERT charts.  Each has advantages and disadvantages, but the key is to make sure that everyone knows and understands what needs to be done, when it needs to be done by, and who is responsible for what.
The second is the amorphous management trees that come up in research environments.  A great discussion and treatment of this is in Project Management, by Heerkens -- https://www.amazon.com/Project-Management-Second-Briefcase-Books/dp/0071818480.  It doesn't  speak to research, but projects in general, and a project is a project.  I really like his treatment of the "accidental manager", where you find yourself in a position of responsibility managing people who you have no real control or influence over.

the key is to make sure that everyone knows and understands what needs to be done, when it needs to be done by, and who is responsible for what - this is very important. In my experience, we usually fall to know what and when research tasks need to be done, because that's the nature of research!

It is not project management exactly but more general you can read "Getting Things Done" by David Allen.

A while ago I read a paper [1] about SCORE, an agile method for managing research groups. I have not really tried to apply it at my research group, but we had good experience with using Scrum for the software development aspects of our research projects.
[1] M. Hicks and J. S. Foster, “SCORE: agile research group management,” Commun. ACM, vol. 53, no. 10, pp. 30–31, Oct. 2010. http://dl.acm.org/citation.cfm?id=1831407.1831421


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Organizing data and files
Over the last four years I did a bunch of experiments (biotechnology, protein purification). Some of them are more valuable, some of them were just poorly designed (e.g. temperature not controlled, etc.). Some of them turned out to be important one year after I collected the data.
The data is highly heterogeneous (microscopic images, ELISA, SDS-Page, HPLC of various suppliers, settling velocity, filtration tests etc.; formats are doc, tex, xls, txt, csv, tiff,py, ipynb, png,pdf and proprietary formats like leica`s lif format)
Usually, I started a new folder on our network drive for each task (like afm_particles/2015_01_13_particle_1). Over time I gathered about 50 Gb of heterogenous data.
Usually, I do not alter the data in place, but load it into an Ipython notebook and do the calculation and plotting with python. The calculation files are stored in the same folder as my data. I have a file called INFO.txt in every folder with a brief description of the data within the folder, experimental conditions etc.
Usually, I know where I can find my data, but recently I wanted to change a plot for my thesis and spent about 15 minutes searching for the right sub-folder. In the end I searched for the name of the file and found it. A colleague of mine quit his job a year ago, and if I wanted to reproduce his experiments, I would have to invest several hours in order to find the specific file containing the raw data (hopefully with physical units stated somewhere).
We do write quarterly reports and paper-based lab journals (some more thoroughly than others). However, navigating through my own data is challenging. Not to mention someone else's data.
How do you handle heterogeneous data collected throughout a project (3 - 5 years) ?
Have you found a solution satisfying for both, people who have to document it and people who might continue the project ?
It would be great if I can browse the collected data without going through all the subfolders. For example by some form of index with information for each folder like: title, short description, usefulness of experiments, worked out as expected, totally crap, totally different than expected but interesting.
Are there tools capable of such a thing (Linux, Windows) ?

By storing the raw data and keeping all processing visible in a script, it sounds as though you already have better data management than many!

Tangential comment about "totally different than expected but interesting": My view would be "totally different than expected and therefore interesting."

I have been using a very simple system and am very happy with it:

have one projects directory
have one private directory
have one life directory

Inside these directories, I create one new directory per year, for example projects.2015.
Inside this directory, each new sub project gets its own folder that starts with the date at which I begun this new project, for example 20150902 rendering performance analysis. Everything related to this project will be placed inside this directory, no matter when it is done.
When your projects get too complex, you can always add a text file describing the content of your files in more detail, and place this file inside the projects.2015 folder. However, do not place anything else in this folder! Only folders starting with a date and maybe one file giving more information.
This system has worked for me for about ten years and I see the following advantages:

very easy to implement
does not depend on software that may or may not be available in 5 or 10 years
works on Mac, windows, Linux, etc
can be backed up easily, for example by placing your files on your cloud storage folder
future ready: when I started online backup, for example, did not exist yet and we do not know what will exist in the future; but system will probably be compatible.
very comprehensive: chances are you will remember what year and maybe what month you did a specific project, so it will be very quick to find your stuff.

Finally, if many projects contain a similar structure, stick to an identical organization of your data. Whatever your structure, sticking to a system will make it easier to retrieve your files later. For example, inside your folder 20150817 afm study 1055nm grating and any other measurement folder, you could have the following sub folders

00 notes
01 code
02 raw data
03 processed data
04 plots

Personally, I often use numbers because this orders the list of items in my desired way and because in Unix, Mac, etc, I can just type cd 03[tab] and the system auto completes.
Whatever you end up choosing as a system, my recommendation is that you look for something trivial to implement that does not slow you down. The more trivial the logic, the more likely you will stick to it over the years and benefit from the ordering you have.
Have fun

Thanks, this is a really thorough answer. I can't recommend enough the practice of putting the date in front of file names in the form yyyymmdd (or yyyy-mm-dd), as everything then gets sorted automatically by date without effort and without worrying about the date when files were last modified.

I am going to answer the "I wanted to change a plot for my thesis" part.  I write in LaTeX.
I create a symbolic link to the plot file's directory. When you have to share the LaTeX, such as during submission to a journal, you need to collect all the images together.  I do this using a shell script that copies the figures into the same directory as the LaTeX.  It also helps to have everything in one directory for some version control systems.

That is  a very interesting solution. I guess you are working solely under Linux ?

Yes, but it should work in any common operating system.

I have been thinking about this myself as well. I did a few projects before I started my PhD and learned a lot during those. Now I have several folders which each have specific files in it. I have a folder for References, Data, Experiments, Presentations, Conferences, Manuscripts, Protocols, Information, and a few more. For each experiment I do, I make a new Word-file. This file is saved using the date of the experiment; I often have an Excel file per experiment as well which has the same/similar name and '_setup' in the name. I have one Excel file with each experiment written in it (date and brief description). My 'Data' folder has a subfolder per technique, so I have one for ProteinGels, Chromatography, CD, DLS etc. In there, depending on how many files, I made subfiles per year. All data is saved with the date of the experiment in the file and usually a description of some sort. I have two back-ups of all my stuff that I save at different locations in case my laptop gets stolen. Don't want to do the PhD again ;).
It's still not 100% but I can easily find experiments and the data. I also have a labbook where I write things as I go, and I scan this when it's full and I start a new one. The labbook for me is just daily notes about making solutions etc because it wouldn't work for me to write details of every experiment in there.
I do find it hard to keep an overview of everything I've done and this is something that I can improve on.


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Is plagiarism in the form translating an essay detectable?
Say a student needs to submit an essay as a homework assignment for a course. If they find an essay in another language and translate it into English -- to me, this is still plagiarism. But is there any practical way for the professor to detect it at all?

@AnonymousM, well, I am an instructor, not a student.

What's the question? I think we all know that the answer is "You can't automatically find document pairs of which one is a human-translated version of the other".

Back in the old days before computers, people used to go to the library and copy an essay from a book and turn it in.

As @AnonymousM comments, this seems to be a question about feasibility of a particular cheating strategy...

Maybe change the title to "How to detect plagiarism when the source is in a different language?"

Student has eventually used Google Translator. Then the Professor could use him/herself Google Translator to identify potential text passages.

@Wuestenfux One difficulty there is that translation is not perfectly reversible - you generally won't get the exact same words of the original text, so searching on the reverse-translated material might not always find the source even if it's online.

It's certainly possible to detect some cases of plagiarism even without having access to the source material.
In particular, an instructor who's familiar with the student may recognise clues that this isn't their work - e.g. discrepancies in writing style, or the essay shows knowledge well beyond what the student usually demonstrates.
In some cases, other clues can apply - e.g. student plagiarises from an old essay that doesn't cite any sources or refer to any advances post 1990s, or student writing about a legal issue in the USA is referring primarily to French legal concepts that aren't applicable in the USA. This depends on domain.
None of these are cast-iron proof, but they could certainly provide enough for a reasonable suspicion, at which point the question becomes what to do next. But that seems like a separate question.

"the essay shows knowledge well beyond what the student usually demonstrates." When I was a student and had essays to write, I would do special research just for the essay. So my essays always showed knowledge well beyond what I otherwise demonstrated. I thought that was the whole point of an essay and everyone was expected to do that.

@Stef Certainly students are expected to research for an essay. But not all kinds of knowledge are easily researchable, particularly within the time one might plausibly spend on an essay.
For instance, it's plausible that a student who didn't speak a word of German last week now understands that the lyric "du hast mich... gefragt" means "you have... asked me"; that kind of thing is very quickly researched. But it's rather less likely that they'd spot the word-play in that lyric without reading somebody else's commentary. 1/2

@Stef If their essay shows familiarity with that kind of nuance, and these insights are presented as their own, then it becomes reasonable to consider the possibility of plagiarism. Of course, if they do research and cite the sources appropriately, the question of plagiarism doesn't arise. 2/2


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Using one's middle name in place of their first name in academia
Why do some people use middle name for their first name like attorneys?  And is this ok in the academic venue?

I do not think this practice is more common among attorneys than among many other professions.

@GEdgar - I'm not so sure. From what I've seen (anecdotal, sure), it mainly is men with the same first name as their father (for family reasons). Then, to distinguish the father from son the son's second name is used within the family. One of my brother-in-laws is different - as a child the family used his middle name, and he went to his first name once in college when there was no reason to distinguish (in general).

I didn't know this was more common among attorneys!

To take one prominent example, I think Tim Gowers, the Fields medalist also known as W. T. Gowers, would agree that it's acceptable to use your middle name in academia.

This has nothing to do with academia.

Why do some people use middle name for their first name like attorneys?

Perhaps they don't like their first name, or maybe there's already a well-known person in their field with the same name, so they need to distinguish themselves.

And is this ok in the academic venue?

Yes.

Or perhaps they've been called by their middle name their entire lives.

I am in the same situation and my motivation to use the middle name is for one that it is much rarer than my first name and the other reason would be to make it harder to identify the natural person behind my author name for people that have no justified reason to do so.

Believe it or not, there's some ambiguity in the question because of "middle name" and "use".
Example 1: I've been known as "Andreas" all my life. For the first eight years of my life, that was my middle name. My baptismal certificate says "Raphael Andreas Blass". But when I became a U.S. citizen, the idea that I'm called by my middle name didn't fit with the forms that needed to be filled out and/or with the mind-set of the official filling them out. So I became "Andreas Raphael Blass". Am I therefore using my middle name as a first name?
Example 2: Donald A. Martin, professor emeritus of math and philosophy at UCLA, writes his name just as I wrote it here on his academic publications. But everybody calls him "Tony", even in academic contexts like introducing him  when he lectures at conferences. So which name is he "using"?
As long as I'm writing this answer anyway, let me give a few examples to indicate how common the use of middle names is. Just among my own past Ph.D. students, there are R. Michael Canjar and E. Todd Eisworth (about 7% of the total). Emeritus colleagues in my department include B. Alan Taylor and G. Peter Scott. Other mathematicians whose work is close enough to mine that I've cited it in my papers: C. A. R. Hoare (called "Tony"), F. William Lawvere, W. Hugh Woodin.  And I'm probably forgetting a few here.

Without hardly thinking about it (due to my interests, and my associations with several of his Ph.D. students), R. Daniel Mauldin comes to mind.


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Guidelines for When to Acknowledge Research Assistants
I know that it is general practice to include a footnote on papers acknowledging research assistants who have contributed to the project.  I am wondering, however, if any guidelines or accepted practices exist for how to determine which assistants should be acknowledged where.  In general, since it is low cost to acknowledge assistants, it makes sense to be pretty generous in erring on the side of acknowledging.  But, in the interests of acknowledgments being honest reflections of those who contributed to the project, what about some of these particular cases?

Research assistant (RA) is hired.  Does a small amount of work, but not enough to be useful, and then quits.
RA works hard on project, but is very poor at communicating.  As a result, RA goes incommunicado for a while, comes back at the end of the period having put together a work product that is of zero use to the project.
Open-ended research project begins.  At start of the project, it is not clear which avenues of research / aspects of the project will turn in to productive papers and which will not.  RA does good work on an avenue of research that turns out to be a dead end.  Another avenue of research is more successful and results in a paper.  RA contributed nothing directly to the successful avenue, but did contribute to the larger open-ended project.

The usefulness of failure is easy to underestimate.  Failed research directions are some of the most important contributions to science there are.  In your third case this is probably the sort of project that should begin with a discussion of authorship criteria and expectations. I believe in many cases and fields, the third situation would warrant the RA being offered co-authorship.  Indeed, the answer to this question (especially where authorship is concerned) depends heavily on the field, and you should seek opinions from your colleagues therein.

Thanks @zibadawatimmy for the comment.  In this case the question of co-authorship is unambiguous.  The RAs performed very specific tasks that were explicitly defined for them.  They exercised no significant creative choice in designing the research or its implementation, and worked as part of a team of more than 20 RAs on the whole project.  The fact that they happened to work on a failed project reasonably qualifies them for credit, but certainly doesn't give them special consideration for co-authorship here as compared to those RAs who worked on successful portions of the project.

I think it's best to err in the direction of being generous with attribution.  Firstly because it's hard to be accurate about how important someone was (which both applies to you potentially underestimating your contribution, or the RA overestimating their contribution) which can lead to hard feelings.  And second because it's just a few words and the power to make someone feel good about their contribution outweighs the cost of those words.


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The challenge large language models (LLMs) pose to programming-based homework
I teach courses in which, as homework, students implement data analysis algorithms from scratch, apply them to real and simulated data, and then reflect on the results.
As a student in such courses, I felt that implementing analyses from scratch and conducting in silico experiments provided me with deep insight. However, this year it has become quite easy to solve such tasks using large language models (LLMs). GPT-4 codes quite well. Even its smaller sibling, ChatGPT, can answer correctly if asked undergraduate-level statistics and computer science questions. Copilot also solves many undergraduate-level programming tasks and can be integrated into several IDEs.
I'm wondering what the best way forward is to ensure learning happens. I cannot effectively stop the students from using this technology, and perhaps I shouldn't. This technology will be available to them in their future academic or industry workplaces. However, I'm concerned that if they just use LLMs to solve the homework, they will not gain any real understanding and will not be able to handle novel problems, for which the chatbots would fail.
I'm wondering what the best way forward should be. Currently, I'm deliberating between the following options:

Forbid using LLMs, but do not enforce it. The students' learning is their responsibility.
Modify the homework so that LLMs cannot solve it, moving away from textbook problems.
Complement the homework with written and/or oral exams that should be straightforward to pass if the students understand the work they submitted, regardless of how it was generated.

Thoughts?

I think an answer to this question depends heavily on the answer to another question: Are most of your students capable of learning within a reasonable amount of time to do these tasks with minimal help, then building upon what they have learned to handle novel problems in the future?  In other words, is there a reasonable chance that they will be people whose productivity is enhanced by LLMs rather than people who are made obsolete by LLMs?

Reminds me of the need to be able to do math in your head vs. use a calculator. Need for both exists...

Run the problems through GPT-4. If it can solve them on the first try, change them until it can't. At that point, if the students want to use the tool, they'll still have to use their brain alongside it, which is the best you can hope for.

Does it code well? Or is it good at reproducing snippets of code that are common in Stack Exchange questions? How well does it work with novel code? I remember someone posting a response to a prompt to write a program that determined whether someone was a good scientist where it wrote something like "if white and male return good else return bad."

@Obie2.0 It codes better than someone who doesn’t code well. It’s not a game changer for testing/rating/finding the really really good students, but can blend in well with okay students. (Your mileage may vary depending on the specific domain.)

For data science, Chat GPT is far from the only problem here.  There is a profusion of blogs that contain python code for various analysis tasks.  Very often students will just search online for a suitable recipe and cut-and-paste without really understanding what they are doing (easily exposed by asking a few technical questions about what they have done).  To make matters worse, many blogs are written by inexperienced practitioners or other students and full of bad advice.  Chat GPT is just automating that process.  Unfortunately the students educational environment is simply too helpful.

@Dawn exactly, but how to motivate students to learn to do math in their heads when they can use a calculator?  The analogy I use (when teaching programming) is that ChatGPT is a bit like going to the gym and watching someone else pump iron.  You will learn how the equipment is used, but you won't make your muscles (e.g. problem solving skills) any bigger.  Unfortunately some students just want to get good marks on the coursework (as that is how education has shaped them).  Sadly ChatGPT is a barrier to developing problem solving skills as it replaces practice.

@Obie2.0 Chat GPT will do well on tasks for which there is a lot of material in its training corpus.  Sadly the sort of educational examples/problems that are good for teaching programming will be well represented in the corpus as there are a lot of student solutions to programming tasks on GitHub.  Sadly "novel" tasks that ChatGPT will struggle with are likely to be too obscure or awkward to make good training experiences for students.

I taught undergrad and graduate classes on statistics and had my students learn R and Python (for many of them from scratch). I think learning to use tools like GPT-4/ChatGPT is a valuable skill.
It does not do well enough for them to just use it without debugging, at least beyond very simple things, so I guess it's a nice way of learning how to use a tool like this effectively. I teach Stats for Cognitive Scientists and Linguists, so it's not super high-level stuff. Simple data mining and data wrangling and regression in frequentist and Bayesian frameworks.
And I don't know about the data you use but when I was confronted with some of the linguists' data and questions, I've had to think a bit on how to address them. The way one would ask GPT to formulate a solution would fail to provide a good outcome in many cases given the underlying questions.
I'm sure there are many questions that can be asked where ChatGPT can be a start but cannot provide the ultimate solution.
Same goes for term papers and homework essays. I've yet to see a research overview produced by GPT that is good (or even one that provides actually existing references).
So yes, I guess it's most likely to be "moving away from textbook problems". In many disciplines the textbook examples are lacking anyway and often might not prepare the students for the "real world" enough. Finding trickier examples but allowing for increased use of these tools might be a service to them.

ChatGPT seems to have become better at basic arithmetic (like multiplication), but it still fails a relative simple task such as "What are the prime factors of 30387?" (tried 2023-04-15T124015Z+0). Response: "The prime factors of 30387 are: 3 x 29 x 349". That is completely wrong. It doesn't even introspect its own answer (3 x 29 x 349 = 30363). It is "only" off by about 0.08%, but that doesn't cut it for prime numbers. The correct answer is 3, 7, and 1447.

Or in other words, it shouldn't be difficult to find problems where ChatGPT is not just a little bit wrong, but utterly and completely wrong.

@PeterMortensen, how about with the Wolfram plugin? It will help with many currently incorrect/unreliable calculations, I would imagine.

@PeterMortensen It's funny. I did high school AP calculus in a time when the big, graphic calculators were used but they still wanted us to be able to do stuff by hand. So the fact that we could teach classes on "how to solve equations in solver() without making it look like you used solver()" was a common inside joke among us students. We all still enjoyed math and still went on to do well. The kids might just be alright if they care about the subject. Those that don't were going to forget the stuff soon anyway.

As I write this, ChatGPT told me "So the prime factors of 30387 are 3 and 10129." It also said that "Dividing 10129 by 7 gives a non-integer result." however my calculator does not agree with that.

@Nick Gammon It is just a predictive text generator, albeit a big one that can look deceptively like it's actually using reasoning. I don't think I've done a literature "search" on GPT yet that would have been usable. It just recreates believable sounding Frankenstein references based on actually existing ones.

@PeterMortensen I agree that it seems to have become a little less terrible at multiplication, but it's still pretty bad at that! Asked for the product of 234 and 708 just now, it gave me 165,312 (should be 165,672).

@NickGammon: If I ask it a question that one could reasonably expect a human to answer without using any tools, e.g. "Assuming I have a computer with an internet connection, what's the easiest way for me to factor 30387 into primes?", it suggests using a web tool, which is quite a reasonable approach for a student seeking to avoid mental work. At first try, it (ChatGPT3.5) gave me a 404 link, but upon telling it that, it corrected itself to a working link. I think students will quickly learn how to ask it questions that work.

Incidentally, it seems that GPT-4 is very, very close to being able to factor 30387 on its own. It initially gives the factorization 3 * 10129, claiming that 10129 is prime. I then told it that using Python, I found that pow(2,10128,10129) is 64. It deduced correctly from this that 10129 is not prime, and went on to produce correct factors for 10129 and a complete solution to the original question.

@story_without_a_title I agree. Since it doesn't actually do the calculations it is relying on finding text that matches the questions. So, if many people asserted 2 + 2 = 5, then it would echo that as being true. However the way it presents its answers usually looks very plausible.

@Polytropos Probably future iterations (if they haven't already) could do some maths already, like Google does, if you type in some random arithmetic for it to solve.

I've asked it to write code for me for some rather obscure apps (in Lua). It generated plausible-sounding code with function calls that might have existed but don't passing to them arguments which would plausibly solve the problem. It's like it is adapting existing code for another app, and guessing that these functions would exist. It's kind-of fascinating to watch. And if you challenge it, it apologises and makes up different code, also wrong, also plausible.

I tried again just now and got "Therefore, we conclude that the prime factors of 30387 are: 30387 = 3 x 23 x 439". It is interesting that we don't get the same answer every time. If it consulting the same database (frozen a couple of years ago) logically it should return the same answer every time. And, of course, it is wrong again.

@Polytropos Not math-related as such but every time I ask it to summarize some major new pubs on a topic, it makes them up. When told that these are made up, it changes them but sometimes they're still made up. Last time it took four reprimands to get real references but two out of three ended up missing the topic of the initial prompt.

I recently asked the free version of ChatGPT to give me Python code for the Pool Adjacent Violators (PAV) algorithm, which has applications in stats/data science. It returned unsalvageable crap. Exposing students to this kind of malfunction ("Here is your assignment. Solve it using an LLM. Does the result work? How would you test whether it does?") would IMO be of immense pedagogical benefit.

Homework, or work under non-controlled conditions in general, is okay for informal, formative assessment, but poor for high stakes summative assessment.
So you should start with "Why am I testing this person".  If you are doing so to help your students identify their strengths and weakness and target areas for work, or to help you plan your teaching then this is formative assessment.  If you are testing them to assign a "score", "grade" or similar that describes their success or failure in a course or section of a course then it is summative assessment.  If the consequences of success or failure could have significant impact on the students' future career prospects and earnings, it is "high stakes".
With high stakes assessment, the temptation to cheat has always been high, whether this is copying a friend's work, or finding essays in an essay bank, or asking an LLM.  The advances in AIs only increases the range of options; it doesn't fundamentally change the nature of high stakes assessment.
The solution is, and always has been to have controlled assessment. First: communicate to the students.  "This task will help you think about loops and working efficiently.  An AI can solve this task, but I'm interested in what you can do with it.  I'll be feeding back, but this won't be part of your final grade."
Or "This task will for 20% of the grade for this course.  You will have 3 hours of lab time, and the work must be completed individually. Use of AI is not permitted, to ensure that the assessment is fair."
Or even "this task is more complex than others you have met so far.  You are expected to use the full range of resources, including internet Q&A sites and GPT-4 to achieve this task.  You won't be graded on the details of the code, but on the professionalism with which the product is produced and presented."
Using homework as a means of giving a grade for a course has always been problematic. If the grade is worth anything then students will cheat. The art of assessment is creating a task in a controlled environment that correlates well to the actual skills that the course is teaching. That has never been easy, but AI doesn't make it any harder.

Coming back to LLM (or conversational AI generally), I believe they have a place, a role in learning and teaching.
What can't (or rather shouldn't) happen though is for them to replace the learning/teaching. Their role is learning scaffolding.
By learning scaffolding, whether constructivism or connectivism, I mean they should be engaged as part of the learning or teaching process. When calculators came along, they were integrated; we used to use those Four Figure Table. When computers (PC as they were) came, they facilitated educational technology. With internet, boom, technology enhanced learning.
PS: when I was teaching subnetting in networking, I discouraged students from relying on calculators. They get to convert any class A or C address and large 'numbers' in short space of time, typical in less than a min (2min at most). And they did it. It was more of improving their thought process and reasoning capability.

Complement the homework with written and/or oral exams that should be straightforward to pass if the students understand the work they submitted, regardless of how it was generated.

LLM have their role if focus is on the cognitive reasoning.
Earlier in the year, I drafted a policy thought on conversational AI (ChatGPT.
I refer to this, as in my view, your use of LLM/conversational AI should facilitate rather than a replacement tool.
My view about conversation AI like ChatGPT is that it should not be banned outrightly. It should be encouraged as part of learning processes: as learning scaffolding.
[Extract from my policy thought on ChatGPT]

Students must disclose their ChatGPT search terms (keywords)
Students must include their ChatGPT result verbatim as an appendix
Students must write their own assignment/assessment submission
Students must show their creativity, their critical thinking and cognitive skills in their writing
It is a crime or punishable offence to use tools to paraphrase ChatGPT results.
Students must show their own originality.
For computing-based assignments/assessments, students must fully comment on their work:
introduction/summary, each line of code, and each function/class

Edit: by the way, the ability to ask LLM/conversational AI, domain knowledge guided queries, might become sought after skills in coming years.

To add to the internet example: that is very true. that's like saying that students cannot do research anymore because they can look at online databases instead of filing cabinets at libraries. The technology has moved on and we need to adapt our skillset to still get to the same way of using critical thinking and ability to synthesize and create original work based on previous work.

Re: your policy, in your experience so far, is it the case that students feel that ChatGPT has "powercrept" non-ChatGPT students? As in, if you don't use ChatGPT, you will most probably do worse compared to those using ChatGPT. Was wondering about this.

In the long run, presumably the object should be to teach students the skills they'll need in a world where GPT exists - which would include include the ability to use GPT for the things it's good at, while still maintaining enough skepticism about the technology and enough understanding of the code to avoid embracing its mistakes uncritically.
One interesting strategy I've seen, via Reddit: https://old.reddit.com/r/ChatGPT/comments/117gtom/my_friend_is_in_university_and_taking_a_history/

My friend is in university and taking a history class. The professor
is using ChatGPT to write essays on the history topics and as the
assignments, the students have to mark up its essays and point out
where ChatGPT is wrong and correct it.

As a programming-oriented example, I just asked ChatGPT the question that plagues me any time I order at a café:

write a Perl regular expression that matches all common variations of spelling for "Geoffrey", e.g. "Jeffry", "Jeffery" etc.

Response:

To match all common variations of the name "Geoffrey" using Perl
regular expression, you can use the following pattern:
/^(G[eoa]offr(e|ey|y)|J[eo]ffr(e|ey|y))$/i
This pattern will match the following variations:
Geoffrey
Geoffry
Geoffery
Geofrey
Geofry
Jeffery
Jeffry
Jeffray
Jeffraye
Joffrey
Joffry
Joffery

It then provided a nice explanation of how this pattern is supposed to work. But just a few seconds' scrutiny will show that several of those variations (7/12 by my count) are actually not matched by its pattern. It doesn't even pick up "Jeffery", which was one of the cases in the prompt.
If you play around with GPT, you start to get a sense for the things that can trip it up. For instance, I find that it's often vulnerable to context reversals, where most of its training material deals with a question asked in one direction but the prompt runs in the opposite direction. Like so:

Please convert the following old Python 3 code to run in Python 2:
print("hello world")

GPT:

In Python 2, the print statement requires parentheses around the
arguments. Here is the Python 2 version of the code:
print("hello world")
This code will output "hello world" to the console when executed in
Python 2.

This is of course incorrect - in Python 2 print was a statement not a function, so the correct change would have been to remove the parentheses. But GPT has seen thousands upon thousands of examples of updating Python 2 to Python 3, and very few in the other direction, so it patterns its answer accordingly. The "old code" is thrown in to mislead it further.
(Disclaimer: GPT has a random element and this trick doesn't always work. On a few trials with similar prompts, I found it got the conversion right about 60% of the time. But even if it's right 90% of the time, I'd still need to vet its output 100% of the time.)
(As suggested by wizzwizz4 in comments, something with file= or end= might be a better example in Python.)
A problem format similar to the history-essay approach might be:

Part A: Write a program to do [thing]
Part B: Prompt GPT to write a program to do [thing], then compare with your own code from Part A. Discuss the major differences between the two versions and comment on which version is better.

Once you have a feel for GPT's weaknesses, you'll be in a better position to set questions for which it will occasionally stumble, or to provide examples where it has stumbled and ask students to explain what it did wrong. Once they understand that it's fallible, and have developed the habit of assessing its output with due suspicion, you've done your job.
The meta-problem here is that recycling old assessment material with minor variations is much, much less effort than coming up with new question styles appropriate to the new world, and lecturers who are suddenly required to update vast swathes of assessment material should be appropriately compensated and supported in that. But that'd be a separate question, perhaps best directed towards one's union.

To be fair, that Python 3 code would work flawlessly in Python 2. I'd use something with file= or end= in practice. (But this is a great demonstration anyway.)

What about effectively haven’t in-class coding exams on university-controlled computers? You could even allow such computers to connect to a range of Internet resources, just not LLMs.
You could then tell students that they are free to do their homework however they wish, but if they use LLMs for all of it they are likely to be very poorly prepared for the exam.
I guess this is variation on your third option.

+1 I used to do exactly that for reassessment exams on a programming course about 20 years ago (they were just for credit, so they didn't have to be commensurate with the main coursework).  They worked really well, and also have the advantage of limiting the time students spend on the task (which can be a problem for students with poor time management skills that don't spread their time well across multiple assessments).  I am considering going back to that, but the size of the class makes the logistics tricky.

As a teacher as well as a student, I think the abilities of large language models (LLM) such as GPT-4, should shift the focus of what we are teaching, and what we should be learning.
Yes, the invention of the pocket calculator diminished people's ability to find square roots, and logarithms in their head or by pencil and paper. And because of that, we could progress to more depth in other topics. But yes, there are pros and cons. Now there is a lot of time and effort to save for the students on the programming aspect when they use LLMs, which however also implies that they can spend it on other aspects of the course.
If programming is the goal and essence of the course, like in undergraduate programming courses, one could argue to forbid the use of LLMs. Which could be enforced by exams or homework in computer laboratories on site where there is supervision and technology to enforce this. Or smaller offline oral examinations about homework, or supervising peer reviewed student assignments. Having the perspective of exams/assessments without the possibility to use LLMs might stimulate the students to train and practice without them. On the other hand, if after trying exercises themselves, they are stuck, LLMs can be great tutors, available any time, which means that TAs can maybe be used in other ways.
However, for many courses, such as data analysis, the essence could be more about understanding the algorithms, methods, and interpretation of the results. For this type of course I think the use of LLMs can be a blessing, because you can increase the complexity and depth of the contents of the course.  If you target insight by programming, ask questions about the particular insights you expect to be gained by the implementation experience. Already assuming that the students use LLMs,  you can ask questions about the computational complexity of their code, and alternative different implementations. You can focus more on in-depth experiences with different data sets. But the problem is that GPT-4 is also already getting quite good in the reasoning skills that can be expected from students even in advanced courses.
One way to deal with this is to give more realistic and open-ended questions and data sets where there are multiple possible correct answers. Such that the students have to choose and analyze themselves. The challenge could become to formulate and create questions that are educational and challenging even when the students will be using LLMs.

LLMs are a tool for experts.  They should be taught, but only on advanced programming course when students can already program competently and have gained problem solving skills and code analysis skills (so they can spot where the LLM is giving them a bad solution).  LLMs don't understand the code they produce, if the user doesn't understand it either, we have a recipe for low quality code.

Why are you testing "hello world"?
It is common to give assignments that boil down to a little more than "hello world".  Consider why you give such assignments - the students ability to do "hello world" is usually not the goal.
If you treat it as a learning opportunity, where you offer to fix problems the student has with writing "hello world", instead of an assessment that determines if they get credit for the course, the GPT problem sort of goes away.
Your goal is to have the student honestly try the homework, then provide feedback on how to do it better, and repeat until they can actually do the homework type problem reliably.  Handing a student a problem and saying "solve it" doesn't really do this; it will divide your students into "the ones who can solve the problem" and "the ones who cannot", but it won't substantially move students in the ones who cannot bucket into the ones who can bucket.
Homework as tutoring
Require that they turn in the "hello world" problems.  These will be corrected by your TAs, but the assignment is to attempt it, not to succeed at it.
Students whose assignment doesn't succeed can try again and get marked again.
Assignments build on each other.  You are expected to have mastered assignment #1 before you try assignment #2.
Actually handing in an assignment, that honestly attempts to solve the problem, at the level of competence expected of someone entering the class, is worth "full marks"; if it fails abysmally, the student may be required to do some tutoring and try again.
This will require some prerequisites on students.
Assessment as Assessment
Your assessments -- the exams will have problems -- similar to the assignments, and often simpler, but different.  Simply memorizing will not work.  ChatGPT will not be available during the exam.
The exam will have a choice of more than one problem to solve.
Students are encouraged to use GPT or whatever to help them learn
If a student is having problems with a question, they are encouraged to try to use ChatGPT to prototype.  They will, however, have to be able to solve the problem without ChatGPT.
You can even provide sample solutions.
Multiple assessments
Don't have 1 or 2 high-stakes assessments of the student's progress.  Students will falsely think they know something after they copy-paste it from somewhere else.  An actual early assessment may wake them up.

I used to teach mathematics about the time that computer algebra systems were being built for smartphones. I used to encourage students to use smartphones to do the mathematics because the department had a policy of using TI-Nspires with a built-in CAS anyway. So, the approach was two-fold and simple:

Design a series of questions that show a problem done and instead of asking the student to solve the problem, ask the student to explain the rationale in the steps of the problem solving itself. Why did we move from A to B? You can also structure your language to contain hypotheticals and counterfactuals. If X were Y in step A, would we still arrive at step B? If NOT X, why wouldn't we transform the equation in such and such a manner?

Ask natural language questions about the concepts involved and make them linguistically challenging. Programming language theory is replete with concepts about syntax and semantics and specific programming languages, given the complexity of the BNF and the idioms that are used, can be torturous to master. Take JS equality rules for instance. Go after questions that not only allow for the use of technology, but require it. Imagine assigning work that asks natural language questions about JS equality, not only by asking a student to fill out such a table, but then to either craft natural language or to answer questions that use indexicals that refer to the table. In other words, create Captcha-like questions. Even if a student finds a way to translate a graph or picture into words to feed to an LLM, make them work for it.


text:
Why Do Researchers Paraphrase Instead of Quoting, considering the pervasive usage of language models?
In open-source software development communities, if someone has already implemented a module well enough, we re-use it over and over again, in different projects. If there is any room for improvement, people update the original module and all the project that use the module, get the updates.
On the other hand, in research communities, we rarely quote the exact definition of a theory or method that was written by the original author. Instead, we paraphrase them over and over again, which sometime involves noise in the paraphrasing process. The worse issue happens when the authors decide to change the terminology to define the same theory or method, which makes it very confusing for the reader.
I understand paraphrasing is preferred over quoting in most cases because it shows that you understand the outside material you are using and it gives you more agency over your paper by allowing you to explain the expert opinions, research studies, or other evidence to your reader as it relates to your topic and thesis. Paraphrasing also helps you process the material and understand it on a deeper level. It signals to your reader that you have processed the information and understood the material. Paraphrasing also reads more smoothly and keeps your own voice front and center.
But why do these benefits justify the drawbacks of paraphrasing? For example, if we paraphrase to signal to our readers that we have processed the information and understood the material, why can't we just show this in the way we use the theories or methods in our analysis?
The main reason I'm asking this question is that I believe after the wide usage of large language models like ChatGPT, all of these benefits of paraphrasing do not make any more sense and I see greater value in quoting.

"The worse issue happens when the authors decide to change the terminology to define the same theory or method, which makes it very confusing for the reader." The alternative to this is that everyone would be forced to use the same terminology and notation that was used by whoever first introduced a certain concept. In other words, we would all need to use the terminology and notation of Euler, Gauss, Dedekind, etc. The mathematical terminology that you've inherited is precisely the result of a long process of evolution, not a long process of blindly copying the first inventor's notation.

The OP is making an analogy between software reuse and direct quotations in a written work.
The thing is, the way software is reused (in modern development) is really more like paraphrasing than direct quoting. The actual code implementation should be encapsulated and hidden from the application programmer, with a clean interface that the application programmer references without duplicating the code wholesale. A function call in this sense is like a citation. It's not like you copy the reused code in its entirety.
Providing paraphrasing, citations, and references is efficient for a writer, reader, publisher, and programmer. There is less content bloat to deliver. If an interested party wants the exact expression/implementation from the original source, they can go look it up. If for some reason there's an edit, revision, or correction in the source, there's no promise or need to propagate it to all the places it was quoted.
For more reflection on this point, see Hunt and Thomas, The Pragmatic Programmer, Tip #15: Don't Repeat Yourself.
Secondarily, the OP's attempt to connect this issue to LLMs like ChatGPT is hard to interpret. The OP posits a rather strange reason for writers to paraphrase anything:

... if we paraphrase to signal to our readers that we have processed
the information and understood the material...

Perhaps in a school environment some instructor once said this as justification for an assignment? But it's not generally true (and it's also not true that the presence of ChatGPT means that no human has to understand anything anymore), so what follows after this conditional is inoperative.

Writing style is a cultural thing.  There are not reasons for it.  In my experience, science and engineering fields do not use quotes, but I think some humanities fields do.
I am sure someone will think up some reason why some fields use quotes and others do not, but it's actually arbitrary.


text:
How to find professional references?
I am from a small country where we do not have many eminent professors, but I did work hard and published over 30 papers which were cited more than 400 times. I am applying for research jobs internationally, and a key factor is references.
It does matter who has written the recommendation letter. In addition to my unknown colleagues, I tried to find some eminent professors as professional references. I contacted several ones who have even cited my papers, but they simply replied "they cannot be my reference as they do not know me", meaning that why they bother to recommend someone they do not know.
This gave me a bad impression that no matter how hard or good you work, career success depends where you are and how many people know/care about you.

What is your current position?  Graduate student? Postdoc? Faculty?

I contacted several ones who have even cited my papers, but they simply replied "they cannot be my reference as they do not know me", meaning that why they bother to recommend someone they do not know.

This is to be expected. The purpose of letters of reference is to provide a personal perspective on a candidate from an eminent figure who knows the candidate well and is able to compare her/him with peers. The simple fact that they cited you means close to nothing. I have probably cited hundreds of researchers, maybe thousands, some of which I don't even remember the name. There is also the question what these references are actually supposed to write in their letters if they don't know you personally - "Mia wrote this pretty good paper in 2012, but I have never met her or talked to her outside of when she asked me for this reference." isn't a great letter of reference.

This gave me a bad impression that no matter how hard or good you work, career success depends where you are and how many people know/care about you.

It is a bit of a depressing way of putting it, but at the end you are not incorrect. "Knowing people", or more correctly, "making people know you" is indeed very important for a researcher who wants to progress her/his career.

How to find professional references?

This depends a lot on your level. As a student, you typically want your professors to act as references. As a more advanced researcher, which you seem to be, it is very important that you:

Find and occupy a niche (that is, if people think about subsubfield A in area B, yours should be the first or one of the first names that pop into people's mind). This works only if you repeatedly annoy people about a subject in papers, keynotes, lectures, and tutorials.
Go to conferences, write joint papers, and work on joint proposals related to said niche
Visit other universities, or invite researchers from other universities working in your or a related niche
In this way build a substantial network of collaborators and co-authors
Follow-up on on your network, make sure that people that you once knew are still aware of your recent work
Maintain reasonable standing with the important players in your larger area

If you end up with a reasonable network, which clearly takes time and dedication, you typically want to select the most eminent members of it suitable for a given application to act as references.

[This should be a comment but too lengthy to put in the comment box...]

I am from a small country where we do not have many eminent
  professors, but I did work hard and published over 30 papers which
  were cited more than 400 times.

Write this in your cover letter, explain why you cannot find a famous reference. It is really an achievement that you published over 30 papers which got cited more than 400 times if you have not worked with famous professors. Make sure the reference you got from the "not so famous" people are good.
Good reference is important, but that's not the only factor people consider when they read application. Try apply first before giving up and feel frustrated. Good luck.


text:
What's the ethics of (paid) proof-reading college admission essays?
A friend of mine is looking for undergraduate or graduate students in the US to proof-read college admissions essays from high school students back in my home country. There will be pay, but the proof-reader can also do it pro bono. Given that the students are not native English writers, "proof-reading" means more than spell-checking, and more like writing coach.
I have always felt torn about this. If I help a student, doesn't it make it unfair to others? This is especially serious because in college admissions essays, the story and the voice of the essay matter a great deal. In contrast, paid proof-reading of graduate dissertation is much more justified, because the make-or-break factor is the scientific substance, not the prose.
On the other hand, perhaps this a trivial matter compared to all the other unfairness that the admission process entails e.g. test prep, going to a good high school, or even being born to a better parents.
Thus, I want to ask how admission officials think about this? And does the argument change in any way if pay enters the equation?

Having a qualified reader provide more than superficial corrections to an admissions essay is a valuable asset, and one which is almost certainly required for top tier institutions. Many American students are literally surrounded by qualified readers with diverse perspectives, while international students may have fewer contacts with the requisite mastery of the language. If the only competent advice a student can obtain is from a paid reader, they are still only narrowing the gap with their American peers. I think there are only two ethical arguments to consider here.
First, is the paid service worth anything? If the criticism and corrections are junk and the service does not increase any student's chances at all and the service provider should reasonably be aware of this, then it's clearly unethical.
Second, does the contribution of the reader go substantially beyond what an American student would receive? Specifically, do the contributions of the paid readers potentially cross the line to misrepresent the abilities of the applicant? If the submitted essays are not representative of the, as you put it, story and voice but also of the English competence of the student, it could be a disservice both to universities and to inadequately prepared students.
If you are clear of these two issues, I see no ethical complaints. Of course, a generous person might donate their time, but that doesn't make a student who needs to earn a living unethical.

Could you describe "what an American student would receive"? I suppose what's considered ethical in American college prep could serve as an useful guideline here. (Of course, one may ask whether the services that wealthy American students receive are themselves ethical or fair, but that gets philosophical very quickly.)

@Heisenberg Just as an example, when applying to colleges, I had a couple of my high school teachers proof-read my essays (a college application-style essay was actually one of our assignments senior year, and most people obviously used theirs), as well as several of my friends. Proof-reading was for spelling and grammar, as well as some higher-level style and organization suggestions. My guess is that the line is before when you start actually (re-)writing sentences for them.

After  just going through this with my daughter.  Paid services are available. Lots of unpaid options as well (such as the teachers and fellow students per @RogerFan).I certainly spent my share of time reviewing and discussing the various essays my daughter wrote. As a reasonably well-published scientist who did a post-doc overseas (so helped many students write better) and had a mother who was an English teacher, I think I did a pretty good job.  Was that unethical? No, I really don't think so.  Others aren't as lucky to have help in the family. Paid help may be the only way to go.

@JonCuster The issue is where to draw the line with "helping". Proof-read is okay, write whole essay is not. What about suggesting a more appropriate word? Sentence? Paragraph? Now I'm uncomfortable deciding.

@Heisenberg - I think that any help equivalent to what one might reasonably expect in a high-school English class would be in scope. Spelling, grammar, sentence structure, discussion of theme, ...  That is a level of help/instruction what non-US applicants might not easily obtain. Somebody else writing significant portions from scratch would be right out though. Yeah, there is a lot of grey area.

@Heisenberg I think Jon Custer has it right. There is gray area, but the help that American high school students routinely receive is more comprehensive than what one might associate with the word proof-reading. I think assistance at every "level" of the composition (from spelling to style) is acceptable but it should probably transition from fixing things at the lower levels to asking questions at the higher ones.

@Heisenberg One way to think about it is like this: if this were an academic publication, would it be unreasonable for me to ask for coauthorship for my contributions? If the answer is “no,” then you’ve gone too far in “helping.” You can suggest words and paragraph orders, but anything that resembles a substantial original contribution on your part isn’t okay.


text:
I found a trove of Computer Science Masters thesis unprotected on a respected uni's website
I was looking for some technical documentation in relation to development of a specific piece of software, using a Google search such as "ACMESoft" "ACME_Rhino" filetype:pdf.
I found a PDF hosted on a European University's website which looked to be of interest; detailing some generic processes in relation to my search query.
I haven't read much of the PDF though, since I then went to see what other resources were available, so I browsed to a URL such as https://fileserver.someuni.ac.at/files/cs/pdf/ instead of https://fileserver.someuni.ac.at/files/cs/pdf/0815.pdf - and a full Apache webserver directory listing was presented to me.
It occurred to me that these PDFs were all people's thesis. I clicked a couple to check, and some were lengthy documents - Masters etc.
I didn't go to university, so I don't know whether these documents are supposed to be public-domain or not; but it would appear to me they shouldn't be.
Is there anything I should do in this situation?

At least in the US, most theses are public. It would be extremely odd otherwise and I would question their value. (Note: There are exceptions with industry-related type of stuff, which can usually be hidden for a few years)

Note that there's a world of difference between public (which these are) and public domain (the phrase you used). The fact that they're publicly visible has no bearing on their copyright status.

If you feel it's incorrect for them to share the links in public, you could contact the webmaster and question their process. They possibly should disable the file listing at the directory.
Nevertheless, it's possible that for some universities access to theses is provided free of charge for other researchers to access, and you probably found the location where all of them are stored.
I don't think access should be disabled to the files individually, but indeed it is my opinion that the file directory listing should be private. They probably have a website with descriptions somewhere else, that link to the files.

On the other hand, the file directory listing is the poor man's version of the front page for the files available for download section, so it might also be entirely intentional that it is accessible the way it is.