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Professor claiming first authorship on former student's project
A Ph.D. student at institute A recently graduated and left the field. They had been working on a project in the last ~2 years of their programme, as part of a wider research group, but did not publish it. It did, however, constitute a significant chapter in their thesis, and represents a lot of work.
This student's supervisor, Professor X, picked up the project after the student left. They have done a lot of work over the past 4 months to bring it up to publication standard, and as a result have decided to change the first authorship to themselves, and plan to submit soon. In this field, first authorship is desirable.
I (a former student of Professor X at this institute, now working as a postdoc at another institution, and a member of the research group) am a bit uncomfortable about this, for the following reasons:

Whilst the Professor has clearly done a lot of work, it's arguable whether this constitutes the majority of the contribution. The student did much of the less visible but often difficult work, such as data compilation, exploratory analysis, and literature review. They also produced the first draft of the paper. This contributed as much to the current content of the paper as the subsequent analysis by the professor.
The paper is an important and exciting contribution, but it is not a key paper from the research group. Professor X does not need another first author paper. Arguably, it would reflect better on Professor X, and the research group as a whole, if the work was (correctly in my opinion) attributed to the former student.
Part of what makes me quite frustrated about the situation is that if Professor X had expended the same level of effort on the project whilst the student was still at the institution it may have been published in a more timely manner. Much of the additional analysis done since the student left was suggested by said student, but they did not receive the support they needed to achieve it at the time.

I do not believe that making the student first author would violate academic integrity due to the significant contribution they made to the research. The corresponding author would, however, be Professor X.
I'm not sure how to handle this. At one extreme I may remove myself from the paper in solidarity with the student. Alternatively, I could just let it go, and not jeopardise my relationship with the professor and the wider research group.
Another way of posing this question: at what point would it be appropriate for the professor to claim first authorship, and has that threshold been met in this case?
Edit: To be clear, as pointed out by @Ben, none of the reasons listed above are themselves any justification for changing authorship order. But in the marginal case where either author could reasonably be considered 'first-author', I think they provide important context.

Was the actual paper already drafted while the student was still with the group?

@MisterMiyagi yes, a draft existed which the student was working towards submission. The paper that exists now does, however, include extra analysis

Does this post answer your question: https://academia.stackexchange.com/questions/193551/supervisor-taking-first-authorship-after-student-has-left-university?rq=1

@Job_September_2020 It is relevant, but I believe the details are significantly different, in particular my own relation to the Professor and student

Who initiated the project and suggested the problem to be studied?

So you are co-author? (Better write this explicitly so that it is clear.) Have the former student expressed that they are unhappy with this situation? If they are fine with this I don't see any point for making a fuss.

How closely tied to the student’s progress was the professor in the first place? They may actually have done the lions share of the work.

Is an "* Authors contributed equally" footnote on the first two authors not an option?

Is it possible to publish the first work of student A with his name as the first author as the drafted version, and then cite to that work and publish new analysis as the second paper with the order name of itself?

To answer this question, we need to discuss what does the first authorship mean first. However, the meaning differs across research areas and places. For example, in pure mathematics authors are commonly listed alphabetically, hence the first authorship means nothing except the surname of the 1st author started with A, B, C or D.
In other fields, e.g. physics, medical research, biology, the authors are listed in order of contribution. However, it is now more clearly appreciated, that "contribution" is not a one-dimensional measure. It is often not easy to compare who did the "most important" work: person collecting the data, person analysing them, person writing the paper, or person who did nothing particular about this work but whose grant is paying for the whole lab. There is no universally accepted rule. Everyone values the contribution they best understand. Everyone has their own opinion. It's healthier to list contributions in the paper, rather than encode the ill-defined importance in the order of authors.
Finally, the first author is often someone who is the "face" of the work, who is most responsible for the results in this paper and who can be contacted first about their developments. With this meaning in mind, if one of the co-authors left academic field and is no longer participating in further development, it is justifiable to move them down from the first authorship.
Having said all that, it is always a bit greedy for an established Prof to claim the resources or appreciation away from their student to themselves. I can see the reasons behind it, and it is justifiable in this case (depending on the norms of the specific research area), but this is still a bit of a shame that Prof could not be more generous and gracious in this case.

Agreed on explicitly stating contributions, we use the CRediT system, but in this field first authorship still counts for something. I think your final point hits the nail on the head

This answer's conclusion is that the professor may be a bit greedy, but how does that answer OP's question?

Having been a data analyst on many papers, I will say that I've never been first author if all I did was data compilation and analysis.  On many papers that I worked on, the people who did the most actual work (in terms of number of hours) were field workers who did all the interviews or focus groups -- they usually only got acknowledgements but sometimes were in a list of authors.
First authorship is, in the fields I know about, usually given to the person who came up with the idea and supervised the writing of the paper.
That said, if a professor already has a lot of publications and has tenure, it's nice for them to put a student first, at least if the contributions are roughly equal.

All of your concerns are irrelevant to authorship
Each of the dot points you list as your causes for concern are irrelevant to authorship and reflect a complete lack of understanding of the purposes of authorship attribution.  Authorship is a recognition of substantive contribution to a written work.  It is not a gift for trying hard or putting in hours (I'm sure an undergraduate could have taken even longer to do the same work).  It is also not a thing that is handed out on the basis of "need" or on the basis of what a person might counterfactually have done if they had had more support at a previous time.  In cases where the order of authorship even matters, the only questions that are relevant relate to the substantive contributions made to the paper.

I agree, but in this case author-order is valued in the field, and the substantive contributions are, for the purposes of this argument, equal between the two individuals. I have edited the question to clarify

John contributed onions for the soup, and Jill contributed stock, salt and pepper. Whose contribution is more significant? Who should come first in the list of authors? There is not right answer to this. Contributions can not always be compared in a fully objective and transparent way. The order of authors often reflects nothing but the balance of power in the team.


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Applying to a PhD with a weak masters thesis
I am working on my masters thesis which is the first time that I have ever really taken part in research activities. Throughout the experience I have learned a lot and I think that I would like to pursue a PhD in the field that I am currently working in right now. However, I feel that when all is said and done with my masters thesis, the end product will not be that great. There are a number of factors that I think contribute to this - the first being that it's my first time doing original research and second I am the first student of my supervisor, so we are experiencing the student-supervisor relationship both for the first time. But given that, I think it is primarily a function of my introduction to research. A similar analogy would be a first year undergraduate student that doesn't do well, but finds their groove in the last couple years of the program.
My concern, however, is that if I produce a low quality thesis that I might actually be disadvantaged compared to a recently graduated undergraduate student. For example, my undergrad/grad GPA's were (3.5/3.8) and assume that the undergraduate also had a GPA of 3.5-3.8, but I can foresee that a committee might view my weak thesis as an inability to do research in contrast to the undergraduate does not have any published research either, but doesn't show signs of being a "weak researcher" by nature of not having done any.
So the question really becomes whether or not somebodies MSc experience is a good predictor of how they would fair out as a PhD student, and whether somebody applying to a program with an MSc can actually be disadvantaged.
Edit: Perhaps mentioning my perspective on the MSc experience in my SOP might be of benefit? Unless, all of this might be moot if entrance committees might not even read your MSc thesis.

What do you mean by low quality? Is it terms of research findings or rather the format of it? Most master's theses are rather short of new findings, and thus the focus will be on the format, and ability to structure your own research. It's fine if you haven't come up with something brand new, but it should still look decent on paper.

@Seal I would probably say in regards to findings. Objectively speaking, I don't think that the idea is all that great. In regards to format, do you mean formatting in regards to if it is structured, well written, etc?

Yes, that was what I meant regarding the format. If you have a thesis with a good format, but not necessarily great findings, you have still shown that you can produce some work. When it comes to master's theses, it can be hard to produce high quality results. It shouldn't be expected of you, but it is a merit.

Where are you and where are you applying (country). Things vary.

@Buffy I would be applying from (University of Alberta) Canada to University of California, Davis.

First, you may be judging your own work more harshly than is warranted. It may not be as weak as you think. It is fairly common for problem to appear easier after it is solved than before you began.
Second, no one is likely to read your master's thesis as part of a doctoral application. They will take note of it, and any grade you got, and the general field. At most a few people will read the abstract. They are too busy to read it, frankly.
Third, an application for doctoral study is normally judged on a wide range of criteria with different candidates scoring higher or lower on the various elements. But the overall picture is what is important. "Does this candidate have the necessary background?" "Does this candidate have a good chance of success now and hereafter?" And since most programs are competitive, "How does this candidate compare with others in the pool?".
But, if you are accepted then you still have two hurdles. The first is passing comprehensive qualifying exams. Some advanced coursework may still be needed to get you to that point. The other is writing an acceptable thesis under the direction of an advisor, along with whatever research enables that.
In the US a lot of doctoral students enter the program with no research experience at all. You already have an advantage there, no matter how your recent thesis turned out. And, even at the doctoral level, not every dissertation has the same impact. It represents early work, not the person's best career work.
Make sure your CV is solid with your background accomplishments. Don't negatively characterize your thesis. Make sure your SoP explains your goals, short and long term, along with how your background supports that.
And, though you name a specific school, don't cast a narrow net for a doctoral program. Give yourself more than one chance.

in which way are "most programs competitive"? What kind of programs are these? Where is the competition? What is the pool? :)

@Ben, by competitive I meant that the pool (all the qualified applicants) will be larger than the number of slots available. Few programs can accept everyone who applies.

Indeed, makes sense. In my field it is more or less common that once you do a master thesis in the certain working group, you'll highly get the chance to add a phd there. Hence, there is not really a competition.

Your Master's thesis is mostly irrelevant as the only thing that matters is they you did your master and depending on the university, your overall grade (around the world, most countries require you have a master to be able to do a PHD).
What will matter is your research for the PHD (and the PHD thesis if that is your graduation requirement) and any other pre-requisite the university with the PHD demands.
Keep in mind that you dont have a weak master thesis. You merely have a thesis. It's binary, you have it or you dont, and having it approved and published means it has enough quality to have been deemed appropriately.
A thesis is not graded like homework, it's a work of knowledge and/or an effort(for title requirements different than a thesis) evaluated by the scientific/academic community as a valid contribution to the field, and backed up by a country's government as legally real. Therefore if it is accepted, it's good enough to award a professional title. So remember that you dont have a good,weak, strong or bad thesis, you have a thesis and it being 'good' is not an assessment you make.
Also the experience on a master is no indication for a PHD in general because it can vary wildly, but at the very least it already gave you a test  for writing stuff down. Remember that it doesnt matter if you have the cure for all types of cancer, if you dont write it down under a proper paradigm , a big useless theoretical frame, and many references in APA/CHICAGO style, it will be themed useless by academia.

It can be worth noting that your thesis could be a merit if you want to apply for a PhD within that specific area, but that's mostly it.

@deags I like how you think about the quality of a thesis, that being it's not really my decision to decide it's merits but my peers. Interesting take and definitely a piece of wisdom i'll be sharing if it comes up.

@D.Gray What can I say but that a thesis is not graded like homework, it's a work of knowledge evaluated by the scientific community as a valid contribution to the field, and therefore if it is accepted, it's good enough to award a professional title. That makes having a thesis/title a binary thing. You have it or you don't... let me put this in the answer.

@Seal I would say that having graduated via thesis might be considered a merit, but graduating is the requirement.

@deags Sure, what I meant was that the topic of your thesis may matter if you apply to a position in the very same topic.

@Seal Do you mean the topic of a past thesis or for the current/next one?  Can you give an example?

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In Preparation For The Job Market - On Day 1 of the PhD
There's a higher likelihood everyday that I am considering pursuing a PhD in my field, and ultimately, in applying for positions as a professor down the road. What I am trying to do is think ahead as to what some extra pieces of my application could include that are not research related. I understand that every field is different, but there are most likely numerous qualities and characteristics on an academic application that are not bound by discipline. So the question I would like to figure out is what type of professional development activities I can work on over the course of my PhD that would contribute to a well-rounded academic resume.
Obviously, publications are key components in an application and over time those will (hopefully) manifest as research continues. What I am trying to do now is target other areas that will allow me to round out an application in the future. As an example, I am currently taking a Teaching & Learning Program with the Graduate Studies department at my university and the program draws on the "Science of Teaching and Learning". In contrast to research papers, this type of thing may be minor, but I feel that developing these sort of auxiliary components to the overall application over time are all important.
So pursuing development to become a better educator (and this is because I actually want to, not because I feel compelled to) is one thing that I can do in the present to help prepare me for the future. I am wondering what some other things can be done over time (especially over the course of a PhD), that can contribute to a strong application aside from research activities. Below is a list of some things that I have thought of that could be developed over the course of a PhD that could A) help a future application or at least B) make my life easier down the road (haha).

Developing a sample of introductory course notes for in class & online learning (boo COVID-19)
Pursuing professional development activities in teaching and learning
Developing introductions for certain technologies that have some relevance in my field, but can be overlooked at the undergraduate level (Programming Language or Geographic Information Systems)
Writing tips and advice for undergraduate students

I debated asking this question, because it sort of feels like a shopping question, although after some thought it felt like more of a professional development question.

"Pursuing professional development activities in teaching and learning" This is what will actually help you teach better later in your career.

I can't tell what your question is, maybe you can edit the title as CJR is suggesting?

I feel like this question has been asked before, but perhaps it was also unpopular and got closed. I would say that you should follow people in your Subfield on Twitter. This is a popular topic of conversation there. You could also read some books on the academic job market. I like “The Professor is In” but it is more helpful for some fields than others.

Actually the most important thing for you to focus on right now is getting a good research experience and a good relationship with an advisor. Get started on that. Learn to go deep into some subfield of your overall field. Gain expertise in that.
Along the way, if you want to be an academic, a bit of teaching experience will help, but will be more valuable for some positions than others. But without the "gain expertise" part it won't matter much.
And I caution against trying to choose a field strategically based on what is going on this month. You will graduate, in several years, into a completely different job situation and it is impossible to predict how it will go long in advance. As you get closer to graduation, in four years or so in the US, you can start to broaden your skill set depending on the perceived needs at that time.
But some things are valuable in any case. Learning to do research. Learning to write. Learning to collaborate. Building a circle of potential collaborators.
But most of the details you suggest are just distractions at this time. Day 1 is the day you look seriously for the best advisor willing to work with you.

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What are the chances of degree revoke if an unrelated plagiarized paper was published during the course of Phd studies
The Phd Thesis and course work all are un-plagiarized; degree completed through all the normal procedures of the university.
However, a plagiarized research paper which is un-related to the doctoral thesis and coursework was published during the PhD tenure.
What are the chances of degree revoke (the degree has long been granted).
Regards.

Very slim chance, though it might depend on circumstances. Can you retract/correct the paper? And what some fear is plagiarism is really just sloppy scholarship or other errors of a less serious nature.

the plagiarized paper is heavily plagiarized (I always regret that time of doing it).... can you please explain 'though it might depend on circumstances'.

it must be added here that the plagiarized paper has no match for the scope and field of study of doctoral dissertation.

I would like if anyone has ever seen such a case.

If ever such a case happened, please share the details.

It can happen.  But it would depend a lot on the policies at that institution.  As reported in the New York Times, consider the case of Senator John Walsh (D-MT), whose master's degree from the US Army War College was revoked when it was discovered that he had plagiarized a paper he'd submitted in partial satisfaction of his degree requirements seven years earlier.  They even ground his name off the bronze plaque listing the school's graduates.

Thanks for a reply. It must be mentioned here that the plagiarized paper has not been referred to as a part of partial satisfaction of degree requirement. It was a stand alone incident.


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IEEE TVCG citation date on google scholar
In the following example, google scholar says (bibtex and everything related) that the paper is from 2014:
https://scholar.google.com/citations?view_op=view_citation&hl=en&user=kLGQWIsAAAAJ&citation_for_view=kLGQWIsAAAAJ:u5HHmVD_uO8C
same thing for ORCID.org:
https://orcid.org/0000-0002-1045-5880
When you go to
https://ieeexplore.ieee.org/document/6905844
it confirms: "Date of Publication: 19 September 2014."
Clicking on the question mark refers to an explanation:
"The Date of Publication for Journals...was simplified to reflect a single publication date..."
https://ieeexplore.ieee.org/Xplorehelp/ieee-xplore-training/working-with-documents#publication-dates
which sounds promising: no ambiguity, but a single date to refer to.
However, if you click "cite this" on the page, the bibtex says year 2015, which is what counts and everyone cites and refers to.
What's up? This discrepancy seems to be in every TVCG paper that I've checked so far.

As you already read in your 3rd link, "IEEE Xplore represents the very first instance of public dissemination". This date relates to online publication of the work and is often as soon as possible for the journal (i.e., 2014 in your example). But later, the paper gets published in an scheduled volume at a certain page (i.e., 2015 in your example), and after this time you can mention the page number and volume at your citations.

In what issue the paper is going to be published in is usually known right away. Also, you didn't explain the discrepancy between the different sources.

@ZoharLevi "In what issue the paper is going to be published in is usually known right away" - I have experience with two IEEE journals and this definitely is not the case. They "fake publish" it online first (i.e. they just put the accepted version up, not even the actual copyedited one) and fix that as the official publication date. Then at some point, which can be months or even a year later, the paper gets assigned to a print issue. The BibTeX data then refers to the bibliographic reference for the journal, which is the corresponding print issue. The online publication year is irrelevant.

Perhaps what's confusing is that the statement says (emphasis mine):

The Date of Publication for Journals and Standards on IEEE Xplore represents the very first instance of public dissemination. IEEE Xplore previously made distinctions between the online publication date, print publication date, etc. This was simplified to reflect a single publication date of record as defined above.

If you interpret this as "we used to make this distinction, but will not do so going forward" and don't expect the change to apply retroactively, then it makes sense you can find papers that were first distributed online in 2014, and first published in print in 2015. (I don't know when they made the change, but in the absence of evidence to the contrary, I will assume it happened after the paper in question was published.)
Whether you write 2014 or 2015 in citations to the work might depend on conventions of your field, but I would tend to suggest going with the bibTeX entry from IEEE here. It at least used to be the case that it was the print version was assumed to be the version of record, and represented the "official" time of publication. The Google Scholar entry is clearly based on the online publication date, and well, Google Scholar is just a database, not an authoritative source of publication details.

That may be the case. But reason say: 1) the going forward should have a year, and consistency should be kept backwards; 2) there should be one year only; 3) both google and ieee are big enough to make an effort to be in synch; 4) the suggested bibtex doesn't align with "going forward."

@ZoharLevi 1) Consistency is likely much less of a priority than not breaking previously published citations. 2) There is only one year for the version of record, but there are often different years for different versions. 3) Google is big enough, sure, but also don't appear to care much about Google Scholar. I've seen it being very wrong in some citations, they don't fix bugs etc. I think GS just isn't a money maker for them, so nowhere near a priority. 4) Not sure. I didn't check IEEE's bibtex entries for recent years.


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Do you write about known code bugs in your publications?
I am a PhD candidate working with a large computer code (my code plus others, well over 100,000 lines). I am starting to realize that the code will always have bugs in it, no matter what I do. I'm in engineering, not computer science, but it seems this is just the norm for software development. Despite the bugs, I've been able to do real research with this code and demonstrate viable results.
Nonetheless, I have a mental list of all the bugs I haven't had time to fix, and I've been under the impression that I need to write about them in upcoming publications for the sake of being transparent. Yet when I draft some words to describe / explain them, it seems really silly and distracting from the main point of the paper... these bugs don't impact the main conclusion of the work, so why tell the reader something that doesn't even matter?
What do you all think about this? Do you describe every known bug in your publications? Or can some of these things be omitted for the sake of clarity?
Additional info:
The research is to study a new method for design optimization of an aircraft. The bugs are flaws in the engineering test-case set-up. In other words, the code is not as accurate as it should be if this aircraft design were to be built -- but the results do without a doubt demonstrate the optimization method. i.e. the code was successful at optimizing the design, and the test-case is accurate enough that it represents a real engineering problem. There are bigger approximations regarding the aircraft design that are intentionally used to simplify the problem. These have more impact than the bugs, and are clearly discussed in the paper, so no one will ever take this as a real design and try to build the aircraft.

Do the bugs actually affect the results (even if they don’t affect the conclusion)?

Even assuming it doesn't affect your results/conclusion (I would like to hear more about how you know this for certain), it seems a little strange trying to justify buggy code in a paper instead of just fixing it. If you need help, you could request your advisor for support -- there are probably hundreds of very capable undergraduates in your university who would jump at the opportunity.

Also, I just have to say this as a (former) software developer: while bugs are exist in any software, they are not the norm. Teams of developers work very hard to fix them; and although even good software has bugs, those are often complex edge-cases that are difficult to isolate. This is very different from your situation where tech debt seems to be passed down to the next graduate student with no responsibility for fixing them.

Are you and others maintaining the code?  If so, you should have a mechanism to report, track and fix bugs, which I would say does not need to be in the paper unless it is affecting results in the literature/current research.  You also probably want a mechanism to independently verify your results in some cases, as cross-checks on your results.  This is good to mention in the paper.

Is the bug a limitation of the code or a limitation of the method? In other words, is the bug caused by your research's intellectual contribution having a flaw/limitation? Or is the bug caused by bad code style, version/environment conflicts etc.?

@Taw There's 3 levels: one, you have an algorithm or concept, which may have limitations. Two, you have an implementation of the algorithm which may be incomplete, but you did enough to show it works. Three, you have specific implementation details, like your code has specific dependencies or poor error checking.

@MisterMiyagi The bugs do affect the result slightly. As I said in the updated post, the bugs are flaws in the test-case. So some of the plots presented for the test-case are slightly off. (I've assessed how much error these bugs would cause, and it is very small, it is a difference nonetheless) But the point of the research is not the details of the optimized design. It's about the optimization method itself.

@Taw The algorithm was implemented, and works. The test-case on which it was tested is sub-par for real engineering design. (Although it already has many approximations in it intentionally, which I do discuss in the paper, so no one is going to use this as a real design anyway)

"The bugs are flaws in the engineering test-case set-up." This doesn’t sound like a bug. Rather, it suggests an idealized or simplified model (which should absolutely be explained in a paper, but not as a bug).

"Nonetheless, I have a mental list of all the bugs I haven't had time to fix, and I've been under the impression that I need to write about them" << Yes! Write about it today!! Don't keep mental lists. A PhD lasts a long time, long enough to forget 50% of all your mental lists.

If the bugs are directly related to the results of your publication then you need to fix them and re-run the simulations. If the bugs are known and in the code, keep track of them in the documentation.  There's no point in writing in your paper that you code has bugs. If you write: I solve problem Y, but for the simpler problem X the code doesn't work, there isn't any credibility for your work... If you are certain the bugs you mention don't affect the simulations you make, don't mention them.

I don't think the things you describe as bugs, that is, "flaws in the engineering test-case set-up", fit what people ordinarily mean when they say "bugs". So, I think many of the answers you've gotten do not actually fit with the question you meant to ask, because they are about bugs whereas I think the question you actually have in mind is closer to: https://en.wikipedia.org/wiki/All_models_are_wrong

Are you planning to release the code with the paper? Do you think the focus of the paper is on the code, or the mathematical method?

It seems like it's potentially plausible that you publish one paper about the numerical method, where you fix any "bugs" (code that is simply incorrect), describe any simplifications or future extensions to the model. Then, partner with a CS undergrad and reimplement the 100k lines of code in a much more maintainable ~10k lines of code, and woah! you've got a second paper where you discuss an open source library/tool for your method. That paper should go over future developments, but more importantly it should link to the project issue tracker, where you can detail all bugs.

The most successful paper I ever published was a software tool. We released 9 years ago, preprinted 8 years ago and published 7 years ago. It has over 1000 citations and 100,000s of downloads.
Since publication we have closed over 400 issues in the GitHub issue tracker. These won't all have been bugs - many people misunderstand how to run the software, but there have been 900 commits. We currently have 11 issues open.
I don't remember how many issues there were open when we published, but I doubt it was zero. And given that issues come in at about 2 a week, if it was empty when we submitted the publication, it won't have been empty when it was accepted.
We wouldn't have published if we had known that there were major bugs that made the software give seriously erroneous results, but we knew there were things that were sub-optimal, data types it wouldn't work well on, install dependencies that were fragile, etc. Given that we've had about 2 issues a week since we released the software until now, and that hasn't really sped up or slowed down, there would never be a time when we could release it 100% bug-free. We published when we were confident that we had something that produced results that were provably better than what came before and could be validated by orthogonal means.
As the paper was more about the abstract algorithm than the implementation, we didn't talk much about the known implementation problems in the paper, although we did talk about the limits of data where it performed well and where it didn't. Limits that we've improved since publication.

I am starting to realize that the code will always have bugs in it, no matter what I do...it seems this is just the norm for software development.

This is troubling. It's true that few things are ever perfect; it's usually possible for code to be improved. Perhaps it could be faster, or more robust, or make fewer assumptions, or be more accurate. But buggy normally refers to actual errors: either the code doesn't run at all, or it doesn't reliably do what you think it is doing. If buggy is truly the correct word for what you mean, I would not say that this is normal.

my code plus others, well over 100,000 lines

I think this is the real issue. It sounds like generations of students have been using a semi-shared codebase with poor practices, and now there is a mountain of technical debt. I empathize that you don't want to be the one to spend months fixing it all up. But I cannot in good conscience advise you to publish with buggy code. Rather, I hope your advisor will recognize the problem, distribute the work, and set up better practices going forward.

Nonetheless, I have a mental list of all the bugs I haven't had time to fix

Sounds like you need an issue tracker.

these bugs don't impact the main conclusion of the work

How do you know this is true? This is the fundamental problem with bugs.

If you fully understand the problem and know how to fix it, then maybe you're right that it doesn't impact the conclusions. But in this case, why not just fix the bug?
If you don't fully understand the problem or solution, then you can't possibly understand all the consequences, and so it's possible that it does impact the conclusions.

I've been under the impression that I need to write about them in upcoming publications for the sake of being transparent.

It depends.

You do not need to go through all the gory details of your implementation. Ideally, you would make the code publicly available, and the issue tracker can list any known issues.
If these bugs could impact your results, then you do need to disclose them as you say. But in this case, I would expect reviewers to tell you to fix your bugs and then resubmit.
The exception might be if one failure mode is legitimately hard to fix. In this case, you can say something extremely brief like "for transparency, we note that our basket algorithm has a known bug with its underwater weaving algorithm. This seems to be related to an issue with one of our dependencies [X]. However, our analysis shows that this affects the presented cost-depth curves by less than 1%." But this is unusual and should be reserved for really intractable bugs, not just bugs that you don't feel like fixing.

"If buggy is truly the correct word for what you mean, I would not say that this is normal"
I have yet to see a single non-trivial software that is free of bugs. That includes even security relevant things where millions are spent on validation like flight software. Claiming that that's not normal seems very weird to me.

Masters in Computer Science here. If its bigger than Hello World, it has bugs. Some Hello Worlds have bugs too.

@T.E.D.: Case in point, in your comment you are missing the comma in between Hello and World! (This is not meant to be snarky, rather, it is meant to show how hard it is to have software, or really, anything, that conforms 100% to its specification … and there's always the possibility of bugs in the specification.)

@JörgWMittag - "and there's always the possibility of bugs in the specification.". <-- Exactly. Hence the famous Knuth quote: "Beware of bugs in the above code; I have only proved it correct, not tried it."

I covered this ground in my answer. Most code is imperfect; if that’s what OP means by bugs, then fine. But if the bugs are such that the code does not “reliably do what you think it is doing”, then this is not normal and the bugs need to be fixed.

"reliably do what you think it is doing". A lot of bugs take the form of, "this usually does what I expect it to do, but there's an edge case where it doesn't." Such bugs are very common. Whether that counts as reliable or not I guess is subjective

Yes, this is the judgment call. In research, the corner cases can be interesting, so I'd hesitate to say that bugs are always okay so long as the code "usually" does the right thing. Also, most students have no real background in software, so when they tell me that their code is buggy, I tend to believe them and assume that for every bug they know about, there are 10 more under the surface. So while it's true that "real" software could be "buggy" and that's okay, that is very much not what I'm talking about here.

"If buggy is truly the correct word for what you mean": as far as I can tell, only you used the word buggy, not the OP. The OP just mentioned that their code has bugs, which is kind of like saying that the sea has fish. Of course any large piece of code will have some bugs, but going from "some bugs" to "buggy" is a bit of a jump.

«How do you know this is true?» It's sometimes possible to assess an observed bug's impact without (completely) knowing its fundamental reason, or to know (or at least presume) its reason without knowing how to fix it, or to know how to fix it without knowing how to sufficiently efficiently (and thus feasibly) do so. (Whether that's the case for the OP, I obviously don't know.)

Usually, research software's existence is aiming to solve some scientific question (I'm assuming you are not writing a paper just about a program, which some journals do accept).
Given that, there are two cases:

The bugs don't affect the scientific results.
In that case, there is no need to discuss them in a paper,
because people who read your paper don't need to know about bugs
that don't affect the scientific conclusions.
The bugs do (or you suspect might) affect the scientific results.
Then they need fixing, the same way you shouldn't publish
the results of an experiment you seriously suspect are wrong
because you ran your experiment wrong.

"The bugs don't affect the scientific results" -- But if you assume that, you'd better be able to back it up. If you genuinely understand a bug well enough to know all the effects, you probably also understand it well enough to fix it.

@Ray Not necessarily. The bugs might be in how command line arguments are parsed. Or they might be in the build routines. Or in setting that allow the software to run on a datatype that isn't used in the published results.

I have lots of bugs I understand but can't be bothered to fix in research software I work on, some examples:

Terminal output is badly messed up when software is used in parallel mode -- workaround: Use the "write final answer to JSON" option.
When using the 'kissat' option, I never free memory as search progresses, so for larger problems we will exaust the memory and the software crashes. workaround: kissat doesn't work for large problems, but it's super fast so we provide the option.

I'd call both of these bugs, and I hope to fix them one day.

@ChrisJefferson: "I never free memory as search progresses" <-- totally unrelated to OP. Why? What could've motivated you to neglect memory management? Like is it intricate in some way? Genuinely curious.

I'm using an external program which I converted to a library. The program didn't clean up after itself (because there is no need to when a program finishes), so my conversion, currently, doesn't either. I can "restart" the library, by calling it's init functions, but these just abandon all the old memory. Fixing this will involve adding tracking to collect all the memory I'm losing.

Most programmers would agree that all nontrivial software has bugs. Writing bug-free programs is the kind of absolute perfection humans aren't capable of. So it's not necessary or helpful to say merely "the software has bugs"; this can be taken for granted. Nor is it probably helpful to list every bug in your paper, because it's unlikely that every bug is germane to the results you're presenting. If one or more bugs are germane to your results, then by all means you should mention them, perhaps in the context of other limitations of your study. If you want to be especially thorough and transparent about bugs without burdening your paper with a full bug list, use a web-based issue tracker (or just write up the list of known bugs as a web page) and link to it from your paper, with a few words about the most important bugs.

The only developer I know of with a reputation for "no bugs" is Donald Knuth himself.  And even TeX had bugs.  But by version 3, he decided it was in pretty good shape.  So the next bug fix made version 3.1.  The second bug fix made it 3.14.  TeX is now on 3.141592653.  The fact that we can call this piece out as the exception really does prove the rule.

@CortAmmon And TeX is a pretty small program - TeX82 in its third stage had about 14k  pascal statements. And even that little program, written by one of the cleverest humans around had 440 bugs published. And that was a rewrite of an existing system where lots and lots of bugs had already been found. And despite all of that it still had a bug in every three statements. The amount of errors in most modern software will be orders of magnitudes higher.

@Voo+ 440 / 14k ~1 / 31.8 -- slightly less than one bug per screenful. Now, what's the bug rate for SO comments ?

Are other people going to use the code after you?
If it's purely a personal thing for your degree, then don't worry about it. Chances are, no-one (including you) will ever look at it again. This might be a harsh truth now, but in 5 years time when you're in industry doing some hundred-million-dollar project with 200 other people, that project will be far more interesting.
If it's something which people are going to be carrying on developing after you leave though, none of this should be merely "in your head".
If other people will be working on it, or are already working on it, you need an issue tracker. Stat.
Maybe those bugs don't affect you right now - but they might be fatal for the person following you. The more info you can give them, the better that handover will be.
They might not even be your bugs - I note you said that multiple people had worked on it - so you're not setting yourself up to be judged here. And if you've got multiple people still working on it, an issue tracker can be a good way to see who's working on what, as a way to manage division of labour. An issue tracker can also capture the extra features you haven't had time to do yourself, but someone else might find useful in future.
The final reason to get into the habit of using an issue tracker is that every competent company in industry, and every open-source project, uses issue trackers. It's simply best practise, and the sooner you get used to filtering everything through formally-tracked issues, the easier your future career is going to be.

Indeed, I think the most worrying aspect of the scenario is that the list of bugs is a "mental list". Ideally both the code and the issue list should be in GitHub, and the thesis should include a reference to the commit state of the repository from which results were produced, so anyone can download that version, reproduce the results, and check for themselves whether any of the known issues affect the outcome.

@MichaelKay Exactly. I don't know whether this gets taught at uni these days in software 101, but I could well believe that this is something they could easily miss.

For research code, one good practice is to not only prepare many tests to check individual submodules, but to run integrative sanity checks on known cases.
These can be either well-understood models, parity or other checksum tests (e.g. energy-conservation if you are in physics, accounting balance if you are in accounting, function/loop invariant if you are in computer science, etc.)
Where this is not possible, you need to have an idea of the potential errors, a standard practice in numerical analysis. Some well-used algorithms did, for a long time, not have good error estimates (e.g. 4-th order Runge Kutta), but were used extensively due to good experience values.
In any case, you have to put substantial effort to have a clear error model about when your stuff can fail and how to check this.
It is your responsibility that the results you publish represent your best-faith belief that they are correct.
One example is that, if you code may occasionally crash, but when it runs, it always provides correct results, that may be buggy code in the sense that it cannot deal with pathological cases, and is not suitable as user-friendly code for 3rd parties' use, but is acceptable as research code.
But you must be able to trust that the results represent what you claim they do. This is on you. Of course, errors are always possible, but you need to exert the best-faith effort to make sure that they have no role in the results you see.
A rule of thumb that I particularly like is: in the special case that your results are just so perfect, be as distrustful about them as a conspiracy theorist is about the government and the big corporations. Run sanity checks back and forth to make sure things really fit together at all levels.
The nastiest-to-debug cases are randomized algorithms, e.g. Evolutionary Algorithms. Here, systematically debugging with seeds, can help you find problems.
Other nasties are algorithms that depend on race conditions in parallel threads. Another vexating example is one where I once spent a whole week trying to find why my dynamics started out the same, but then diverged after 10 or so integration steps, depending on the optimization level of the compiler. Turned out my system was chaotic, so the dynamics indeed was sensitive to the minuscule numerical rearrangements that were induced by the higher optimization levels.

Your second paragraph makes me think you are talking about some subfield here ...

Captain, pretend you are a competent scientist who doesn't know what "parity or other checksum tests (e.g. energy-conservation if you are in physics, accounting balance if you are in accounting, function/loop invariant if you are in computer science, etc.)" or "4-th order Runge Kutta" or "Evolutionary Algorithms" or "race conditions in parallel threads" or "dynamics started out the same, but then diverged after 10 or so integration steps, depending on the optimization level of the compiler" or "minuscule numerical rearrangements that were induced by the higher optimization levels." is and

... re-read your answer and ask yourself whether it is understandable.

Ahh, the joys of a legacy code base!
Others have answered about the publication aspect. I will address how you save your lab mates from the same problem in the future by writing tests.
IMHO, an Issue Tracker is necessary (as mentioned by @Graham and @cag51 above), but not sufficient. It doesn't tell you before you release if you've re-introduced an old bug or created a new one.
Some people say that Test-driven Development takes the surprise out of programming, but a few tests will start to give you the confidence to fix those nagging bugs. Don't go overboard, but do a little at a time when it will benefit you.
I started using it in a highly disruptive environment just so that I could run a small portion of the code I was working on in isolation.
Perl uses the Test Anything Protocol and I love its todo function that says "Don't complain when this test is broken, but tell me when it starts working". Other languages will use xUnit or something like it.
Nothing beats starting the test suite in the morning, making a coffee and sitting down to a green line saying All tests successful.
Please, commit to fixing ONE bug. Document the proceedure of writing a simple test and how to run the test suite. Make it easy so there's no excuse not to run it. This one act will save you 6 months later and you will understand the your test suite's power for good.
Then you will start telling anyone who will listen how to run your test suite and write their own tests. Eventually the bugs in your code base will diminish as you only have to fix something once and the next generation will sing of their deliverance from development hell by The Magnificent Katie9206.
Living with known bugs should not be the norm for software development. The Society for Research Software Engineering promotes software skills in research and may be able to put you in touch with people who can advise.

It seems to me highly unlikely they have built a system with 100K lines of code without some kind of test framework. But who knows, you might be right.

I've seen CompSci academics I respect release 7k loc without a single test. I expect most people test by hand what they've added/changed, but never consider the action-at-a-distance bugs that go unnoticed. Large organizations have learned to automate tests. Individual labs write code out of necessity and can remain oblivious to lessons learned elsewhere. I really hope I am wrong. Any horror stories out there?

What are you trying to accomplish?
None of the other answers are relevant to engineering: First and foremost, you must have some goal or set of goals (requirements) you are trying to achieve. What you describe as bugs might be anything from non-starters to non-issues, depending what your goals are.
If, for example, your requirement is to optimize that specific design, software bugs are totally irrelevant: if you have a demonstrably optimized design in hand, you're done. It doesn't matter whether the tool is good because the tool is a means, not an end.
If your requirement is to demonstrate the method, bugs are only relevant to the extent that they affect that demonstration. For example, if you have horrible code that randomly formats your drive, that's pretty unusable as a practical tool, but might be totally fine as a demonstration of a method. Less ridiculously, you might have a bug where not following certain input conventions causes the tool to choke, and you manually sanitize your inputs: it's a non-issue, not relevant to your requirement.
If your requirement is to create an actual commercial-equivalent tool, then security holes you would not have even otherwise considered are likely show-stoppers.
Etc.
So, figure out what you are trying to accomplish first, then you can figure out which  items on your mental list affect your result.

TL;DR
There's no need to burden the main text of your paper with details that don't affect the results, but the known bugs should be documented (in appendices, code sharing platform, and / or the code itself).
Share the code
First of all, you are sharing the code publicly, right? If not, you should. Evaluating or building upon a piece of research that relies on 100k+ lines of code without having any access to that code is basically impossible.
Don't keep the bugs to yourself
The bugs that you know of should not live only in your head. They should be documented in at least one (preferably more) of three places:

In the codebase itself. Hopefully each of the reusable pieces of your code (functions, classes, etc.) have attached docstrings. You should point out any known limitations there. For instance, Using float64 here is unreliable and should be avoided. It's best to be conservative here, so maybe even better is, e.g., This is only tested with float32.

In bug/issue trackers. Given that you're sharing the code, you are presumably using a platform such as GitHub. These platforms usually have embedded issue or bug trackers: take advantage of them! Post any known issues there.

In the paper's appendix / supplementary material. I think your intuition is right that you don't want to spend too much space in the paper describing bugs that are ultimately tangential to the point of the paper. However, most venues allow for appendices / supplementary materials, and those would be great places to describe the known bugs and / or link to the issue tracker (see point 2 above).

It's OK not to fix everything
Any project needs to end at some point and if the project is big enough, there will be known issues that you won't have time to fix. That's OK, as long as they don't affect your results. Do make sure to document the known issues, though.
In the paper you can include a sentence along the lines of

The focus of this paper is on the optimization method, and we use a toy model to demonstrate its effectiveness. Note that this model has a number of shortcomings, as outlined in section XXX, which make it unsuitable for real-world use. We believe however that it is a good test case for the optimization procedure.

Then in "section XXX" you describe both the approximations you mentioned in your question, and link to the appendix section describing the other known issues.


text:
Can PhD students take a semester or two to study abroad, similar in nature to a sabbatical?
I know that it is common for professors to take sabbaticals and conduct research at other institutions, but I was wondering if this is something that is available to PhD students as well?
I ask because there is a professor at another university that I would like to collaborate with at some point in Australia (I'm coming from Canada) who works in my field of interest. I think that given the interconnection between research areas may have a direct benefit to my own dissertation or other research projects, but at the same time it could be easily just become a distraction from the dissertation.
I'd be curious to know if this is something that could happen while working on my dissertation (assuming proper discipline can be maintained to balance priorities) or if it is something that requires patience for later on in ones career?
For context, the field would be in mathematical biology / applied mathematics / bioeconomics / wildlife resources (from a math / modelling perspective)

As a grad student I spent a lot of time at an industry lab (although not international). And our lab hosted visiting grad students from overseas. But in both cases we had close collaborations in place.

Ah, I should actually include the field information. In hindsight, this is probably very different across fields.

How are you planning to fund your expenses? If you can get funding from e.g. NSERC then it may be possible.

Sometimes, Ph.D. programs have formalized exchange years. If that is not the case, then if you go abroad to collaborate with the reasonable expectation to come back with a paper in progress to include in your thesis, and funding is secured (most likely provided by the foreign collaborator), then this will probably be fine. To get someone interested enough in you to invite you will be the major step. If it were just for an amorphous “I’d like to study there,” I’d expect your faculty to not be pleased.

Related if not duplicate: https://academia.stackexchange.com/q/83204/20058

I did this (or at least, did half of my six months abroad before covid struck :) ). You're not asking about the UK, so I leave this as an FYI for others, but STFC funded students (and possibly all UKRI funded students) have the option of going on a long-term attachment to visit collaborators or facilities like CERN or LIGO. Similarly, STFC will fund internships in government/industry. See here: https://stfc.ukri.org/funding/studentships/studentship-terms-conditions-guidance/overseas-fieldwork/

I heard about this guy Brian May (https://en.wikipedia.org/wiki/Brian_May).  He took a multiple decade sabbatical - during which he rocked the world a number of times.

This is common in Australian universities.

You might be interested in NSERC funding for foreign study: https://www.nserc-crsng.gc.ca/students-etudiants/pg-cs/cgsforeignstudy-bescetudeetranger_eng.asp

This is really commonplace in my field (computer science). I spent every summer of my PhD interning somewhere.
I think that this is ultimately up to you and your advisor to figure out, as well as for your university/the host university to agree to (universities have rules regarding how much time you need to be on campus, and about double enrollments). If it's one semester then it should probably be fine. I don't know about the standards in other fields, but spending a whole year away is less commonplace (I would probably not agree to it myself as an advisor).

Yep, this is very common. I did 5 myself (a bit extreme...) and knew several students that would work as visiting researchers at other universities.

I spent a couple of summer months and three winter break weeks at a US DoEnergy National Laboratory making connections and doing interesting stuff more related to what I do for work now than my dissertation. They just kind of let me do what I wanted.


text:
Addressing Background Deficiencies In Statement of Purpose: An Initial Solution
Background: Did an economics major after switching out of physics and I did a math minor in university with somewhat inconsistent math grades (A+, A-, D, C+, ... ). The reason behind my grade spread is, quite simply, a lack of consistent, deliberate, and persistent effort as an immature student. I am now very close to defending my MSc in resource and environmental economics with a 3.83 gpa in coursework, graduate awards, academic teaching & learning courses, conference proceedings, and hopefully a submitted paper for review by the time of my application , so there's been much growth for whatever that's worth.
Current Situation: I am trying to switch into mathematical biology to study similar natural resource problems I would in resource economics, but from a different perspective I find myself more drawn to. I have reached out to a potential advisor, they have my transcripts and CV, we are set for a video call to talk about me coming on as a PhD student.
Statement of Purpose: There are some math courses that I do not have as a result of not being a math major, and there are some courses I am planning to retake this academic year to boost the grades of the courses I did poorly in. In my statement of purpose, I am wondering how I can address the background gaps.
Initial Solution: After my chat with the potential advisor, I was thinking of writing out a concrete action plan (including MIT OpenCourseware, References to texts I would study from, and subject matter I would cover) to address the background gaps which would include a combination of taking courses and self-studying courses that are not being offered at my local institutions. This plan could then be submitted (appended?) to the Statement of Purpose.
The Final Problem: Given some deficiencies in background, how can one, or how should one, adequately address those deficiencies in a Statement of Purpose to communicate that by the time they enter the program, they would have a sufficient background to be successful in a program?
Many answers around Statement of Purpose tags have suggested that the statement be forward looking, and so I intend to not spend an inordinate amount of time talking about my history, and instead in the spirit of those answers, focus on the path forward.

One issue is that many programs will probably not allow you to append extra material to a statement of purpose, or if you do, they will not read it.  Otherwise such supplementary material might be used by some applicants to circumvent the length limits.

That’s a strong point. I think that perhaps my initial idea might have some utility outside of the Statement of Purpose, but I’ll have to think on it a bit more...

You should focus on your strengths,and focus on the road ahead. The main focus of the PhD would be research, and writing out a detailed, concrete plan for doing coursework may not help much. Graduate committees are looking for research potential, I guess you can mention that you have a concrete plan on doing the necessary courses in your SOP, and you can submit the detailed plan as a separate document under the additional documents section as part of your application package. Someone who is interested can take a look there.

If your potential advisor wants to fund you as a PhD student, then it is very likely you will be given admission as their student regardless of whatever background deficiencies you may have. This is especially true now that your potential advisor is aware of those deficiencies. In terms of writing your statement of purpose, it is probably sufficient to say that you worked with Prof. x to create a plan to remedy these deficiencies.

You can't "explain away" your deficiencies. They exist. And the SoP is not the place for it in any case. I also doubt that a "plan" for addressing those deficiencies is helpful in the SoP, which should be about your plans to succeed in the graduate program and beyond.
In the US, at least, there are courses open to doctoral students that will address at least some of your deficiencies and if your overall record is good, and you have good letters of recommendation, then a missing course or two probably won't be the single reason for rejection. Committees tend to look at the "whole picture" and try to make a prediction about the success of prospective students. If you can make that happen you can be accepted.
Treat the SoP for what it is intended. Commitment, hard work, dedication, interest, goals, plans. Make it a strong statement.
If you reach the point of an interview, you might be asked about deficiencies. That is the place to address them. And, if you still haven't been accepted anywhere, you can start to implement the "action plan" you suggest.

I have taken the majority of your advice regarding SoP's. Glad to say I got accepted for the only PhD I wanted to be accepted into and went with the strategy of "forward looking" (as you mention here) and in other posts. Advice well received.


text:
The Use of Academic Work in a Professional Publication
I've come across an academic work, a summary of which would make good guidance in a professional publication.  What is the protocol to be followed in respect of the copyright of the authors and of the academic publishing company?

You wrote you wanted a summary of an existing work and depending on what you want I see two directions this could go.
Option A you essentially want some quotes or subsections from the existing work. In this case you need to mostly talk to the publisher of the academic work who likely has most of the copyright.
Option B you essentially want a newly written text with the same content as the academic work but summarized and written for your target audience. In this case you should talk to the authors and ask them to write something new for you.
Sidenote, as you intend to publish this in a professional publication you would need to pay for this in either case.

I don't know if this is exactly a protocol as such, but the only time I published a book my publishers told me they would take care of all that. They have shedloads of staff who know exactly what the copyright rules and laws are, across jurisdictions, and know how to deal with other publishers to ensure everything is copasetic.
So I'd say, get your publisher to figure it out.
(Full disclosure: my publisher is one of the biggest in the world of educational publishing, so it's possible they are better resourced than others.)


text:
Coauthors rewrote and published "abandoned" project without my knowledge
I worked on some results jointly with other coauthors. We jointly wrote a first draft of the article, and we started discussing where to submit. Time passed, and there was not much impulse to finish the article. We moved on to other projects and forgot about it. Our shared draft was never touched again. I assumed we lost interest.
Until, a year or so later, I found my coauthors wrote another article on the same result and published it. To be clear, it is a different text – I do not think any of the text is mine – but it follows the same structure and main idea that the document we discussed and drafted together.
What to do now? I imagine this is a common situation: multiple people work on an idea, but a subset takes the idea to a different place and finishes an article. I do not know if I would feel comfortable being added as a coauthor: I have not written this. At the same time, it does feel unfair to have put so much effort and time into a project without my name. Asking them to add an acknowledgement feels a bit unsatisfying to me (although I feel it is the least they could have done). Is there any better option? Apart from contacting them, is there any obvious step I am missing?

What is your academic position? What is theirs? Are they your classmates? Do you have the same advisor? Have you been non-university friends? Is there any common authority higher ranked than you and them? If so, you may be able to refer to that authority.

"I do not know if I would feel comfortable being added as a coauthor: I have not written this." You should be co-author if you have contributed in a substantial way to this work. This does not necessarily mean writing. I can't say from reading your question how substantial your contribution was, you are the one to know. If an important part of the paper is your idea or elaboration, you should be co-author and they should coordinate this with you, otherwise not. (It is not enough that you took part in initial discussions -what of the work is really yours?)

Thanks. Reading most guidelines for authorship, I feel I could perfectly be an author: I contributed a core idea, I contributed substantial results to a common draft of the paper, I advised against a research path and for the one that finally worked, I also revised and line-by-line edited the first versions of the manuscript. I do not think any of my text is on the final version, but it still feels very close to my text.

"We moved on to other projects" and to other institutions? are you sure you could be reached by them? did you meet them at conferences/workshops/etc.?

Whether you should have been invited to be a co-author, acknowledged, or whether they were perfectly in the right to move on to do a related but independent follow-up project on their own really depends on the specifics of the "ideas". Nobody on the Internet will be able to give you advice on this (I will still try, but it will be a bit abstract).
The problem with ideas is that they are often incredibly fluffy initially, but also very persistent. Some ideas I brainstormed widely with colleagues and friends ended up shaping my research agenda for years, and I doubt any of these people would honestly expect to be a co-author on each of my papers in the next five years because some of the underlying themes are similar to something we discussed abstractly at some point. I would say in my community there is a fairly widespread acknowledgement that ideas are less important than execution (i.e., the concrete experiments, data collection, analysis, and write-up). If you are significantly involved in one of those you should expect to be at least offered a chance at co-authorship, but not for being part of an ideation phase. Part of this is also pure pragmatism - if discussing research prevents all involved from ever doing related research on their own or with other collaborations, people would be very hesitant to ever discuss their ideas with others. It would also mean that there is basically no way to gracefully move on from a collaboration that isn't working, without putting a full stop on this entire line of inquiry (and as you can see browsing the history here it's not exactly unheard-of that collaborations at some point break down for one or all parties).
In that sense I would, without knowing the details, suggest to be gracious about this - sure, it would be nice, maybe even expected by common courtesy, to have been invited to this independent-but-similar research, but from the outset it doesn't sound like they did something obviously wrong so I would let it go. Of course, you can draw your conclusions from this event and decide that maybe this is not a good group to do future work with.

It's hard to account for academic norms. Your expectations don't seem to have been met, they're not unreasonable, but there is more grey in the scenario than is perhaps obvious. Some in this thread have framed it as, "ideas are cheap", which is partly the case. Sometimes ideas are worth keeping to yourself, but if you're not going to be able to see them through, sometimes it's better to know that someone else had explored them.
Unfortunately I've had not dissimilar things happen a few times over 20 years. Some were a genuine misunderstanding, some my expectations were unreasonable and with some the co-authors were simply ruthless in their ambition and happily excluded me (and others).
The most critical thing here, if you're intending in progressing with an academic career, is to think about your reputation and relationships, and how this will impact your career. The sooner you can move on from this and forgive them, the better. Very few people are going to credit you for pursuing the situation (not actually understand it fully), and it sounds like you may be someone with whom ideas come easy- this perhaps demonstrates that it's not merely the ideas that matter, but seeing them through.
Having an authorship dispute hanging over your head is unlikely to be good for you. With time you might find an "in" to discuss it with these individuals but equally you may find that their approach means you choose not to actively collaborate going forwards.

I disagree with this simplification of "ideas are cheap, move on". If I take a paper and I rewrite it on my own words without citing the source – perhaps expanding a bit – that is plagiarism; my coauthors took a joint draft article and rewrote it on their own words to publish it without me – that is not taking only my idea but our article. A different question is what is best to do; you seem to argue that there are no incentives to pursuing coauthorship. I agree there; I think I will contact my coauthors and ask my contribution to be acknowledged.

You should have been acknowledged. it happens that you may think what you contributed was crucial, but the authors think differently. In this case it can be a problem, or an opportunity. Reaching out and asking how they see things might lead to possibilities for future collaborations, acknowledgements, work or something else. I know of a case at Aalto University where an artist's work has been taken and presented by a researcher without due accreditation. The artist is understandably furious. His attempts to get restitution from the institute have led to legal proceedings.  This is bad for all concerned.  In your case you have to see what is best for yourself and consider others, will they leArn lessons? Or just learn to be more Machiavellian and sly? Hopefully things can be done to build integrity and not undermine it. Scientists who are willing to steal, lie by omission and plagiarise - how trustable will they be with future publications? Do you want to be jointly publishing with them when their work appears on retraction watch?

Ideas are cheap, seeing them through is what counts.
Lesson to learn? Next time you initiate/agree to a preliminary collaboration that you think has value, then see it through to a conclusion or let it go. If you cared enough about this topic, you should have pushed to take this through to publication.
Follow up lesson to learn? You identified several people that you should not collaborate with further. If they valued your initial input, they would have invited you to contribute to the derivative work. They don't, so they didn't.
You did not functionally do anything to warrant authorship (because you were not given an opportunity), and acknowledgements while "nice", are completely worthless. Focus your energy on fostering good collaborations and publishing your own works. As you go through your academic career you will collaborate with many people (to various degrees of effort) which you will find out are not worth your time. Dwelling on them is just more sunk time and energy that gains you nothing.

It, unfortunately, happens all the time. Definitely reach out and defend your work as best you can by sticking up for yourself. But if it doesn't mean a publication later on, then just see it as a lesson and keep going

What do you think would be the lesson to extract here? I have collaborated multiple times with others and it is frankly the first time I see something like this. It feels disappointing.

Yeah, I had the same happen last summer to me. I also coauthored many manuscripts and publications. However, this was a first that happened to me. When it did, I reached out to the PI and asked for a resolution. Basically, it ended up with me letting it go because I respected the professor, and I didn't want to see them lose their job. So the lesson is that it is a small thing that doesn't amount in the long run. Let it go. That's the lesson. Unless the research is groundbreaking and ends up in Nature, don't worry about one slipping by :)

I think I disagree. At the very least, I will ask to be added to the Acknowledgements section. After checking all of the authorship guidelines, it feels very obvious to me that this is the minimum they should agree to.


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Grad application: how much do gen-ed summer classes taken elsewhere weigh?
I have taken several classes as a part-time student in several community colleges as an affordable way to complete my gen-ed requirements. I was a naive freshman back then and I thought that since the grades would not transfer to my degree institution but only credits, my grades do not matter much. I ended up doing the bare minimum for the classes and getting a BC for an intro-chem class, and a B for calc 3. These grades are not reflected on my main transcript, which is right now 3.96 overall.
I am a physics and applied math major in my junior year, and I tend to do satisfactory in my major-related classes (mostly A’s). I want to apply to grad school after graduation. I recently realized that I need to submit all of my transcripts from every institution I have been to, so I am really bothered if my lack-of-focus on these summer courses will do me wrong.

You wrote: "I need to submit all of my transcripts from every institution I go to."
Can you decipher the exact meaning of "institution I go to"?
Does it include also a college where you were taking classes on a part-time basis, while still being a full-time student at the college wherefrom you received your main transcript? I used to think that "to go to" would apply only to your "main" college where you were full-time. Please correct me if I am wrong.

By that, I indeed mean that I'm supposed to submit transcripts from "all institutions in which I have taken classes". I had the same impression you had before, until recently I realized that a lot of grad schools need applicants to submit "transcripts of every institution you have attended". However, I did not find much discussion about this on the internet so I could have interpreted it wrong. That said, if you are trying to remind me of a grammatical error, then I admit that there were heavy ambiguity in my previous post. Applogies for not proofreading my post.

I didn't mean that there was a grammatical error; but I do see quite an ambiguity in that rule. Did you take classes part-time at that other institution? -- yes. Did you attend that institution in the full sense of word? -- that, methinks, is a matter of exegesis.  I used to think that to attend means to be a full-time atudent. I certainly may be incorrect in my interpretation, and you may consider seeking also other people's opinion.

@Michael_1812 "Taking classes at" is the essence of attending. I would not advise trying to wiggle out on a technicality.

It's entirely up to the admissions committee how to weigh things.
Some people reviewing your application will focus most on courses in your major that are most applicable to your degree. Others may focus on the GPA and hardly pay attention to the transcript. You'd be okay in those cases.
Bad grades early on followed by good grades is better than the opposite; it suggests learning and growth.
"I did the bare minimum because I thought I could get away with it" is a bad look, and I think it'll hurt you if someone has the same impression that you admit is reality. Grad school is a lot more self-driven than any schooling that comes before. You won't get a grade on the tasks you complete, and the dissertation will be far in the future.
You can't correct your past grades, though, you'll have to just apply with what you have. If you are accepted somewhere, though, do consider how you'll need to adjust your motivation going forward.


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Would an F and repeating some C courses affect my Grad admissions
I am a 3rd year student in Communications Engineering undergrad. Previously I was a physics major for my freshman and half of my sophomore years (1 year and a half ). Unfortunately for my university start, I had a rough one, failed a Biology II course and a got a total of 6 C grade courses during my first two years due to mostly not being able to study efficiently for uni which affected my mental health. I realized Physics wasn't my career and decided to go into Communications and Information Engineering in the second term of my sophomore (2nd year). As of right now I am scoring straight As in all my core courses, and have already repeated 3 courses of those 6 and got a A- to A (A as the highest grade) in all of them. As I have calculated I would achieve a 3.76 cGPA upon my graduation provided I continue being a straight-A student in Engineering. I went on as well to get multiple internships each summer vacation. And thinking of minoring in Physics just to study Quantum Computing applications in digital communication systems in grad school. I am planning on joining extracurricular activities while I am at Uni to hopefully publish an article review connected to my field of interest in addition to my GRE Physics score. Would those repetitions look bad on my transcript for the Grad school application as I made a wrong choice at the start of my degree, provided all the efforts to outbalance this start in my current major? as well as providing explanations in my personal statement?
Ps. I have to continue repeating the 3 other C-grade courses to achieve my target cGPA, to get honors of the second degree (cGPA: 3.75 ~ 3.85)


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When Is It Time To Get the Department Involved With MSc Progress?
I am a masters student approaching my expected graduation date (September), the problem however is that I have not a single result to speak to. However, this is not due to of a lack of serious effort and time invested on my part. The problem that I thought of was slated to be worth publishing in a top journal and the idea and execution that I had was considered good enough to be accepted as an abstract to two top conferences.
My issue is that my problem is simply too hard to solve (computationally speaking). At the beginning of the research, I simply would not have known or understood this and so I kept working on it and working on it and as of last month the stark reality hit me that my original idea had to be abandoned. I even found a possible path forward to solve my problem which is used extensively in the field of engineering and operations research, but I was told not to use it by my advisor.
I tried to simplify the idea which then requires me to completely change the method that I use to analyze the problem (changing the method sporadically also means my research question has to be altered). However, in order for me to use this new method I have to learn how to program with a new set of libraries, and solving my problem is non-trivial because I have nobody I can speak to locally about implementing the problem in the programming language I use (Python). Long story short, I am unable to implement this new method because I cannot figure it out and have nobody I can talk to locally about it. What feels even weirder is that I am trying to use a method for the sake of salvaging a problem, and this feels very unnatural to me as an inexperienced researcher.
I am three months away and I have no results. My masters program appears like it is going to take 2.5-3 years to complete, and on top of that, I will ultimately need to present something to my committee that I am not really proud of. Looking forward, I am somewhat worried that PhD programs might not be interested if they see a masters student that took a much longer time to complete a masters program with no publications to speak of. Furthermore, I am concerned that funding for my project could run out from my supervisor, and then I would either need to get a job to continue on or try and find a new project with a different professor in the department.
No matter the case, I am sure that I will graduate at some point, but I am very concerned about how the end result will reflect on me if I wanted to apply to PhD programs afterwards.
Are these common occurrences in the paths of masters students or is it time I get somebody outside of my supervisor involved? It’s a difficult decision because I feel that I lack the experience and judgement to make such an important call. Furthermore, how could I make the best of this situation when I am applying for PhD programs in the future? I

What field is this? Pure math or something applied? What country?

The country is Canada and the field is resource economics - the ‘complexity problem’ and the modelling approach is based in Operations Research / Math Programming / Optimization.

Guess I can't help much other than to suggest that you work it out with your advisor. It may be that enough progress has been made to get enough insight into a difficult problem that the thesis and the degree are not in danger. Many problems are beyond scheduling.


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How To Professionally Address A Mental Breakdown
I’ve been fairly strong throughout Covid, able to push myself to work every day (6-7 days x week) because my thesis project has been somewhat compromised by Covid that this is my unfortunate reality. However, we have just entered our country’s third lockdown recently and just yesterday - out of the blue - I broke for the first time in my life.
I won’t go into the details, and I’ve made efforts to reach out to a therapist.
I have signed a contract with another faculty member to work with them because my supervisor can no longer support me, and right now I am unpaid because of an agreement we made so that I could be paid “up front”, but I can no longer do this work given my extremely broken state of being. So in essence, I owe them hours, and I am currently on no income for the foreseeable future.
I am unsure how to communicate this in a professional manner, though, because I don’t want to hold the project back out of respect, and I also signed a contract. So I’m hoping to get some advice on the best way to approach this. Unfortunately, taking an official/medical leave of absence is not possible for me.

Take care of yourself first, see the counselor, others can take care of themselves for the time being. This sort of thing is more common than you’d guess. A brief note to your supervisor will suffice. The mental mud will clear, be patient and don’t forget about that counselor

What exactly do you want to communicate? That your work will be delayed? That you want to undo the contract (and presumably repay your advance)? Something else?

What is the nature of your "agreement"? What prevents you from taking medical leave?

It is extremely strange that you say that medical leave is not possible --- can you explain why?

Ask your therapist before you do anything.  They may know something we do not.
If you have a union, they may know about local rules you need to follow.  Ask them.
Tell the faculty you have been ill.  They do not need to know the details of your illness.  Nearly all faculty know that illness, including mental illness, is a common occurrence and not under your control.

And do these things in this order. 2.5 might be to go to your university's central graduate school (if your school is organized like that) to find out what their policies are and ask them for advice.


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Is ISIT conference on Linguistics and applied linguistics fake?
Is this conference fake? Or is it just not popular? https://isit.org.in/event/index.php?id=2385102
I am a PhD student. I just found this conference and applied for me to do something useful while traveling in Summer. But it seems really weird to me. The name of the contact person cannot be found in any academia. The domain of the website is in India, the country code of the phone number too

Looks pretty sus, but we don't answer questions about individual journals/papers/conferences, or else we'd be over-run with that kind of question.

The About page is... unusual.  "The ISIT-forum constitutes of professional wizards and overseas technical leaders who have left no stones unturned to reinforce the field of science, engineering and technology." "Beside this ISIT is collaborated with leading publisher of world to expose the innovation of its wizards. Last but not the least [ISIT] is one among the leading institutes of the globe to inculcate technical revolution among scholars, brilliants, geniuses and wizards of different country."

ISIT is a legitimate conference in information theory. ISIT on Linguistics on the other hand…


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Awaiting an LoR request response
So, I have emailed my professor to ask if he felt comfortable with writing me an LoR during the weekend. I got an automated response stating that he won’t be back till this  Monday, it’s currently Wednesday and Tuesday was a state holiday. I know he just got back, has young children, and his research going on, but the wait is making me anxious. What I am asking is whether or not this is a no, maybe he’s avoiding me? I only doubt his agreement to writing me one because I got a B- in his class, and only have talked to him a few times during office hours. However I will be in another’s lecture of his next semester, and he’s approved me to be a lab course intern next semester as well. What are your opinions? Could it be that I don’t have a strong enough relationship for him to write me an LoR? Thanks in advance!

He may have several (tens or hundreds) emails in his in-box to deal with. Patience is a virtue.

Go see him. Don't depend on email for anything requiring a personal response.

You only got a B- in his class and only talked to him a couple of times during office hours.  You sent mail over the weekend just before a two-day holiday and got an automated response that he was out of town.  It's
 the first day back and you're already worried that he hasn't responded.
In both cases, your expectations are unreasonable.  You didn't do very well in his class and he hardly knows you.  What exactly do you expect him to say that could possibly help your chances of admission anywhere?  He can't just make stuff up, he can only report what he actually knows about you, which is that your performance wasn't that good.  You need to request references from instructors who gave you A's, not B-'s.  And pick people who actually got to know you because you showed up at their office more than just a couple of times and for more than just help on the homework.
When you do ask, allow them time to respond.  Give them at least a week (longer if it's a holiday!) to say yes or no, and then at least a month (more is better!) to write the letter.  (Be sure to tell them the deadline and how the letter has to be submitted.)
You should also provide a set of materials to help your reference write a good letter. It should include a list of schools where you’re applying, copies of your transcript, resume or CV, statement of purpose, and a short reminder of who you are, e.g., the class you took from them, where you used to sit, why they might remember your work or some discussions you had, perhaps a photograph. It’s okay (and helpful) to suggest strengths you hope your reference can speak to, e.g., your creativity, ability for abstract thought, contributions to a group.
Depending on the privacy laws in your country, you may also need to state explicitly, "You have my permission to discuss my academic performance."  For example, here in the US, instructors are prohibited by FERPA from discussing a student's performance without explicit permission.

I'm curious about your last point. I've never given a professor explicit permission to write me a LoR. Have your universities told you that, as a professor, you needed it?

@AzorAhai  FERPA is pretty clear, so I already knew this.  But yes, it's come up in faculty meetings here at UMich.  The fact someone asks for an LOR isn't sufficient even though common sense would tell you that they have to expect you'll talk about their academic record.  You need explicit permission.

Huh. Guess none of the professors that ever wrote me one didn't know that. I was wondering because I knew you used to be faculty at UWB and I was in the same system and wasn't asked. Ty!

@AzorAhai When I was at UWB, I knew to ask and always did but I was probably one of the few.  All instructors here in the US should know about FERPA and should be aware of the need for confidentiality and that you can't disclose grades to third parties without permission.  But if no one points out that FERPA also applies to recommendations, it probably doesn't occur to them.

@AzorAhai, Googling turns up lots of universities advising faculty to get permission to discuss grades before writing an LOR.  Here's an example from Penn State.


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How to send a reminder to a US prof who has accepted my internship offer but didnt reply to my follow-up mail
I am a PhD student and I wrote a mail to an US prof for an internship offer, which will be completely funded by my parent institute (fellowship available) and hence the host US prof does not need to spend on anything. He replied in a matter of few hours that he has agreed to host me. I replied to him thanking him and also asking for an official letter from him that he has accepted my offer. He has not replied after that, it has been two days. The deadline is in a week. Do you think I should drop him a reminder or is it too soon?

Two days is no time at all. But if your first request for a letter didn't emphasize the time constraint then you could send a note apologizing for that omission and giving the deadline.
And if the letter was supposed to be addressed to the institution and not to you personally, it is possible that it has happened or will happen without your knowledge.
But if you were initially clear about the time requirements then a note after two days is just noise. The prof may be busy, even busy composing the note you need.

Thanks a lot for your suggestion. Maybe I will put him a reminder 2 days before the deadline, since I am also running short of time. I can just drop an email saying "a gentle reminder" and mention the deadline.


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Is it possible to get a faculty position from a skype interview alone?
I am applying for faculty positions in a country outside the country I am currently a postdoc in (USA). My visa restrictions make it difficult traveling outside of the US for interviews.
Is it possible to get a faculty position from a skype interview alone?

Possible? Yes, of course. Assuming your other materials are solid and you are high on the list of applicants. Not the common thing and not the best route, but if your restrictions are understood by the hiring committee it might be possible to get an offer if you come over well in such an interview.
But you may not have many alternatives, so it may be your best bet.


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Is it common to delay the start date of a postdoc?
I wanted to apply for a particular postdoc in the UK that has a start date in September. It is a great opportunity but I would need to delay the start date as I am currently in the US and it would not be possible for me to move before then.
How common is it to delay the start of postdocs? Is it worth applying even with the knowledge that I wouldn't be able to start by the advertised date?

Have you tried asking them?

I haven't applied for the position at all. I'm wondering if in general, are these things set in stone or is there flexibility?

The main issue is if you have teaching duties when you arrive. If not then there is a very good chance that they are flexible. At the university where I'm at quite a few people start a couple months after the official starting date. Actually even though I was given an official starting date I was asked when I'm planning to start.

Well, how much? A week? Three months?

It is very common to negotiate the start date. All of the postdoctoral researchers I hired or was involved in recruiting came at different times than advertised. Most funding bodies allow you to defer funding for a few months. For instance, EPSRC allows up to 3 months delay for start of projects. Also, most funders allow a so-called no-cost extension, meaning you can extend the end of the project without losing the money.
Just apply and in your cover letter say that you would be available in October, for instance.
Source: I am faculty (engineering) at a UK university and have been involved in recruiting 5 postdoctoral researchers.

I would go further; start dates are almost always delayed.

I would definitely ask them if that could work. Sometimes the timelines on funding are restricted, but often it's not set in stone and there is some flexibility. Be honest from the outset about what your existing commitments are though - I would definitely suggest getting in touch before doing the formal application.
My current postdoc position (also in the UK) wanted someone to start straight away, but I was in another post and was really keen to see that project through, so they were willing to delay for 7 months for me. I think they actually respected that I was loyal to my existing role, even though it was ending soon. For postdoc positions, I think getting the right candidate is often more important than getting a candidate ASAP (if that's possible within the scope of the funding).

Not especially common, no. But you have to take it up with them to see if it is possible. No one else can answer. It depends on field, generally, and on the particular needs of the institution. In some fields it wouldn't be especially disruptive to have you come in late, provided no teaching is involved. But in some lab sciences (for example) there might be a firm need for a person in place by a certain date.
The agency funding the postdoc might also have firm dates in mind, after which funding ends.
But it would be a mistake to apply without exploring it first with them. If you get pretty far along before you find out you are actually not eligible, then you've wasted everyone's time.