text:
Is there a way to save my career from absolute disaster?
I am in my fifth year and I am about to submit my Applied Mechanics PhD thesis in. However, I always felt absolutely incompetent in the field. I tried improving my weak areas by self study, taking up challenging projects, but always ended up with either abandoning the idea or settling with a less rigorous work.
When I used to talk to my advisor, he used to say that I am doing fine and I shouldn't worry about impact factors and rigour of a work at PhD level as I will eventually learn more and get more opportunities as I advance in my career.
However, I feel that I wasted my PhD. I am searching for postdoc positions, but I feel incompetent for the openings. I feel that even if I do get response back, I will get rejected due to my poor knowledge.
I also feel that I should have worked harder and not have wasted time in my past. I also tried making connections during my conferences, but that did not anywhere.
I screwed up big time. I am 30 years old. No work experience. And a mediocre PhD. I messed up really badly.
Is there a way to redeem my career?

Try searching this forum for imposter syndrome, you aren't the only one to think negatively about your work.

Please check out the questions for the emotional responses tag. Your question is not an exact duplicate, but the kind of issue that you are encountering is frequent. You're not alone, and your advisor is most likely right. He has more experience than you to make this assessment.

"I shouldn't worry about impact factors and rigour of a work at PhD level"  That is terrible advice.  PhD students should spend a lot of time on rigor.  Rigor is a key part of a PhD.  Impact factors are important right now, but could go out of fashion.

"A mediocre PhD" is still better than what 99% of people have achieved.

I understand that you feel strongly, but your title is quite stagily. Would it be possible to tone down the drama? And if you go into the industry I think a mediocre PhD in Mechanics is enough to make a good living.

Do you know what people call the person who finishes with the worst score in medical school?
Doctor.

I have to smile. "Oh my god! I have only a mediocre PhD! I'm a dysmal failure!" Step back. Smile.

I shouldn't worry about impact factors and rigour of a work at PhD level — He's absolutely correct about impact factors, even after the PhD level.  But he's absolutely wrong about rigor, even before the PhD level.

Welcome to life. But I take a bit of an issue with everyone's certainty regarding the situation. To paraphrase catch-22, just because you have imposter syndrome doesn't mean you're not unqualified. Can your adviser not get you a job?

@AnonymousPhysicist Not everybody is aiming for a PhD in the first place. Calling non-PhDs underachievers sounds (unintentionally, I'm sure) incredibly arrogant.

@KonradRudolph note that he did not call non-PhD’s underachievers. All he says is that if you have a PhD then you have achieved more than 99% of people. This is not logically equivalent to: if you don’t have a PhD then you are in the bottom 99% of achievers.

To follow up to what @AnonymousPhysicist said, simply the fact that you have a higher-level degree puts you at an advantage over most of society.  You may also find that you learn a lot by teaching some.

I think that you have achieved a lot of academic success. There is hardly anything you could do to improve your technical skills I think. From what you say, I feel that you might be depressed. Maybe a therapist would help you to gain confidence that you need to be truly successfull :-)

Hello. I'm on my second post-doc and still feel that way some times. My motto is 'If I was able to get them to hire me, then I can't be that bad.'

I think your situation is not unique. If you look around academia.SE (as user2768 suggested, search for "impostor syndrome"), you will find that many PhD students have the same feeling about their work or their career path. See How should I deal with discouragement as a graduate student? for example. I hope this gives you some comfort -- sometimes it helps to know that other people are in similar situations -- and you will find some advice in the answers which works out for you.
The advice of your advisor seems reasonable in the sense that often one looks for the perfect solution from scratch, which is a path that rarely brings the desired results. I found that research is done in an iterative way, where you steadily improve and refine the methods and thereby the results. Of course, academic rigor must not be neglected. The impact factor is in my opinion indeed overrated, but this may depend on your field.
Don't let that stop you from applying and attending job/PostDoc position interviews. If you do nothing, you may end up in a spiral downwards since naturally there will be no progress in your career. And even if you fail at an interview, at least you gain experience what is required to succeed.

If you read his first autobiography, you'll find even Richard Feynman suffered from a form of Imposter Syndrome, after his gig working on the Manhattan Project ended.  We could all hope to one day be just as much of an imposter as Feynman.

The "imposter syndrome" is mentioned so frequently that I just felt the necessity to do a websearch to see what the opposite of the imposter syndrome is. I'm not saying that this is the case here, but I think that unconditional encouragement might not have the best long-term effects. I mean, there are people out there who screw things up...

@Marco13 The opposite also comes up on this site quite a bit: the Dunning-Kruger effect.

@Kimball That's not what I meant. Imagine someone trying to write a proper PhD thesis. And imagine that he does not get himself together, does waste time, and is incompetent (all relative terms in view of the high demands, of course). And imagine he reflects, recognizes his flaws and shortcomings, and tells others about that. How would he have to phrase this, exactly, so that they do not assume the "imposter syndrome"? I mean, maybe I'm old-fashioned or whatnot, but there must be people out there who fail miserably at their PhD, and they might just call things as they are...

@Marco13: Nobody sees themselves exactly as they are. Everybody either has at least a small amount of impostor syndrome, or a small amount of Dunning-Kruger effect. The fact that the OP managed to get his PhD at all means that he should not give up and work in a fast-food restaurant the rest of his life (which seems to be the scenario he is imagining). Even if he doesn't quite have the talent to become a tenured professor at a university, he undoubtedly has more than enough talent for an interesting, rewarding, and lucrative job doing applied mechanics or something similar in industry.

Focus on what you can do going forward rather than on mistakes of the past.
Let me tell you about my own situation, which is quite a bit worse than yours and which I am happy with.
I was a top math student in a large school (yeargroup ~1000). I found maths easy and was looking forwards to study it at university. Unfortunately I did very little work at university. Not nothing but little enough to fall behind and drop out of the course. After this I found work as a home carer which I enjoyed and worked hard at but which offered very poor wages and working conditions. I sat in this job for four years before I managed to put enough effort into finding a better job and moved into hospital admin.
After landing a temporary admin role it has been up and up for years now. The turnaround in fortune has been slow but tangible. I started to feel good about myself and my prospects around six months after starting to put some effort into my future. I excelled and found a better role. I saved money, own a home, started a degree, found a wife and have a child on the way. I will graduate in June, aged 30.
Job prospects look pretty good thanks to some AI research I've done.
How does this relate to you?
Well, you haven't screwed up. In the grand scheme of things you've done a lot of things right and you will soon have a PhD in mechanics to prove it!
Lets examine the idea that you "wasted" the last five years. Even if this is true (and the academics here are suggesting that it is not) then it kind of doesn't matter because:

you've identified some of the problems and how to do better
you have 35+ years in which to do better. So what if you didn't produce an earth shattering result within your PhD. If you start producing high quality work then people will notice.

Where can you go from here?
Set aside some time (a day?) to think about what you want to do in life and how you might get there. You could spend some of this time talking to friends and family. Draw up a list of options and decide which appeals to you the most. Vigorously pursue this option. Your actual career might go off on a complete tangent to the plan but having a plan is a good way to get things started.
Something you might want to consider is doing a non-academic job for a year or two. Hopefully some of the academics on the site will be able to comment on whether or not this will be good for your academic career but from a non-academic perspective it would make you much more well rounded and hopefully expand your horizons.

I like this answer because it demonstrates that life is a fluid thing, plans change, you work with what you have, and your inclinations and abilities will move you forward in certain not-quite-coincidental directions, sometimes unforeseen.

The main problem is not with your career, but with your psyche
I was in a position somewhat similar to yours: In a late year in my Ph.D., stuck on some dead-ends, exasperated and thinking I've essentially failed. Wasn't even sure I was going to finish.
I'm not going to tell you that things will pick up and it'll all be Ok, etc. Maybe it will, maybe it won't, or at least not as much as you would like. The point I want to make is that I remember people telling me: "Oh, but you're not doing so bad, you're smarter and more accomplished than 99% of people, and lots of grad students have trouble with their PhD's etc." - this is all true but it wasn't helping me.
It wasn't helping, because I had pinned my sense of self-worth in life on having a certain experience or certain achievements with the PhD. When this did not occur, I felt like a failure - like my life, my identity as a person, was sort of falling apart. There was no me without stellar academic success - and I wasn't really prepared to think about my life otherwise. So whenever I thought about my situation I was subconsciously terrified, mortified.
The disaster - with our without quotes - you are facing is not in your career. I mean, sure, your PhD could have gone better, but it's certainly not a disaster. You need to be able to:

Emotionally recognize and accept what you've done and what has happened to you
Be able to set goals for yourself given your situation, rather than semi-consciously obsessing over not being in the situation you would rather have been.
Have these goals be potentially attainable in terms of the world, and likely satisfying for you personally.
Have other aspects of your life on which you can "bank" emotionally, regardless of whether your career/academic pursuits fare better or worse.

Consider reading my answer regarding a situation worse than yours, with more concrete advice:
How to deal with anxiety and depression after being kicked out of PhD program?

Now for a few concrete words about where to go from here.
Well, what is that you want(ed) to do, as a researcher? You talk about it as though it's some sort of game where you need to improve your points in weak areas, overcome challenges for some sort of metaphysical score of accomplishment... why did you enter a PhD program? What did you want to discover, or get to the bottom of, as a researcher?

If the answer is "nothing in particular", then it might have been a mistake to enter the PhD program to begin with, and the outcome you ended up with is reasonable, though lackluster, given your motivation.
If the answer is "I wanted to explore XYZ" - then who cares about the PhD's quality? Are you still interested in XYZ? If so, think of how to utilize what you've learned and accomplished, and what you know about other people's work, for different avenues into XYZ, in academia or in industry. If not, then think about how to get inspired again about something else. (Yes, I know that is a vague point.)

Great answer. In reality, many people have their first taste of not being a “star” as a PhD student and don’t have the emotional tools for it.

See it the other way around: In the meantime you know at least that you do not know everything about the field. This is an interesting insight and more than many people know (also: "I know that I know nothing").
Your feeling is normal (see also above posts about "impostor syndrome"). A good example is the new group of fresh PhD students every year that think that they know everything and can conquer the world but the deeper they get into academia the more they realise how little they know. This is illustrated (in an ironic way) for example in the well known PhD comics here.

The nice thing about academia is, you most often have a fallback: You can go to industry. They love high trained, clever people. They don't care about papers, impact factors, rigorous PhD theses and so on.
I don't think you should leave academia. But relax, if everything goes wrong, you still have the chance to make a successful career outside of academia.

If you're in applied mechanics, then going into industry should be your aspiration, and staying in academia should be your fallback. (Those that can, do, those that can't, teach). OK, personal bias from one who did a PhD in Computing and then went into industry, but I think if you go into industry you will never suffer from the kind of self-doubt about whether there's any point in what you are doing, you will never be measured on how many citations you can achieve, and you will know you are having an impact without artificial metrics to prove it. You will also earn more.

You mentioned two key words in your question, "..work experience..."  Just block out the noise, finish up your degree, go out there and work; this will provide the experience and build your confidence and competence.  Be honest with your prospective employer where you stand in terms of experience but express your willingness to learn.  You'll likely start off lower in salary than your peers but take the offer and get your foot in the door.  Moreover, never take the path of least resistance in terms of work challenges, it'll bite you later on when you finally get tasked with a huge project.  I like that you do self study.  Good luck with your career journey, congratulations on your prospective degree and may God bless you.

Is there a way to redeem your career? Of course there is, but it depends on what you really want out of your career.
The most important questions to answer are, "What do I enjoy doing?" and "What am I good at". You seem to have questions about that latter (at least relative to other PhD candidates in the same field), but it is not clear to most responders here that your feelings are completely based in reality. Many people are unsure of themselves. It can be a very good thing if humility and a rational comprehension of your weaknesses drives you to learn more and work harder. It can be very harmful to your success if a lack of confidence keeps you from attempting hard or unsure things.
What do faculty at research universities actually do? Teach, guide students at various levels, write proposals, creatively think of problems to solve and how to solve them, exercise technical expertise, write and publish papers, travel and speak, serve on department committees, self-promote, and compete against one another. How many of those things do you enjoy doing, or are you good at, or preferably, both? If "not many" is the answer, then whether you are better than 99.9999% or just 99.9% of people in the world at Applied Mechanics is irrelevant: find something else to do. You are smart and quantitative and can compete quite effectively in many many valued roles in society.
What elements of your own work or work you've seen around you actually excite you; do you find inherently interesting just because of who you are and what they are? Find a career that overlaps as much as possible and then pick a post-doc or industrial job or peace corps stint or MBA or whatever that gets you closer to doing those things.
It is actually not all that rare for a smart person to go quite far in their education before they found out that they are not all that interested in their field (or in an academic approach to it). Starting out, the challenges of just going through the process can be interesting enough and obscure an underlying apathy.
If, after reflection, you really do want an academic career in a particlar field, then do whatever honorable things it takes to succeed there. If you are not all that happy with your thesis, then work hard to get a post-doc with the most famous person you can in an area most of interest to you. Who will help you get several really sexy publications?
If you really don't want an academic career, get out asap (after getting the degree...) Write code, design or build machines, solve problems, teach at a different level. Get an industrial job that both overlaps with your applied mechanics background and will expose you as much as possible to other roles in R&D, product development, business, management,and human society in general.
"Do what you love" beats "Follow down the path I'm on because I'll feel like a failure if I don't" by a thousand miles.

Cheer up, seek out a counselor working on your fears. "It is not fun, if it's not fun"  We all have ups & down's.
I was properly educated in Industrial Technology, I eventually had a carrier of 27+ years in Information Technology as a Systems & Network Engineer Level 3.  Both required advanced problem solving and attention to details!
I'm the guy that walks into a machine room. Finds the ORANGE light, and turn's it GREEN.  The device doesn't matter. Green was my job! Or is was the guy that could machine, forge, identify, weld, bond, measure, compose, print. Any project using metal, wood, ink, emulsion, and complete a project manipulating those media with appropriate tools, to 1/1000 of an inch tolerance. Cutting a 2x6 with only a hand saw, perfectly plum & square (I can still do that).
Seek out happy folks, not in the basement of a Victorian building.
I'm 2x your age, have the life equivalent of 5+ Ph.D.'s.
I am able to keep a conversation with a PD-EE (power distribution, electrical engineer) to a Major MD/Ph.D. researcher in Virology.
From Educators clueless about a specific Learning Disability to teaching a son "the best coping skills" an MD/Ph.D. specializing in Learning Disabilities has ever seen!
Another phrase "don't worry be happy" that is even a song, folks of my age were tortured with on the radio, in college.  I know stuff, you do too!


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JMM interviews?
How many interviews does a typical good liberal arts school do at the Joint Mathematical Meetings every year?
Background: I'm on the math job market for the first time this year.  For the most part I applied to postdocs, but I also applied to a handful of very good liberal arts places this year, any of which I would prefer to a postdoc.  I assume that with my rather short publication list and teaching history I'm a long shot, but I do have a couple interviews lined up.  Really I'm just trying to gauge whether I have a nonzero chance at an offer (assuming not, but it will be good practice for a few years down the road).

Not that I have any specific experience, but if you get invited to an interview, I am sure you have a shot. They wouldn't bother spending this kind of time on your application if it would be otherwise.

The Employment Center runs for three and a half days with time increments down to 15/20 minutes. So you could physically expect three interviews an hour for about 30 hours for a physical (and totally absurd) upper bound of roughly 100. But from what I have seen going through this Employment Center three times is 30-40 is more realistic and I have seen schools do as few as 15-20. Often they also do phone/Skype interviews with interesting candidates who did not attend the Joint Meetings. This gives you a rough estimate of how long the list you find yourself on is.
You mentioned that you have a short publication list and teaching history. If they are willing to talk to you they have already decided that you are interesting enough to spend their time with you. So be ready to tell them quickly why you are worth more of their time and why you are interested in their school (have an answer for this one). Also do not forget the thank you e-mails afterwards.

I know of one department that does get close to 100.  Most of the department comes but they only have one or two interviewing any particular candidate.  I agree this is an exception.

My university used to do around 20. With Zoom interviews we do a similar number; sometimes even lower.

I am on the market this year and attended the JMMs in Baltimore.  After not tailoring my application whatsoever to liberal art schools (which I don't advise) since my primary goal is a research post-doc position, I had 5 different interviews (TT AP) and 1 post doc interview (which, I understand there were not many post-doc interviews anyway . . . and none at the employment center).
Anyway, most places, (from what I know), have about 40 interviewees they talk to.  The goal is to whittle this down to 2-3 candidates that they can invite to on-site interviews (and possibly more if they have more than 1 opening, i.e. they may invite 6 people for on-site interviews).
At the JMMs, these are speed-date interviews.  They want to know if they like you as a person and can foresee you spending your entire life at University X.

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How to study without having money
What would be the best option for an EU citizen to study for a bachelor's degree (in any developed country: UK, EU, USA, AU, NZ, Japan, China etc.), without having the money to pay for fees, bills, accomodation etc.? Is there a study program, university or institution that provides funding through scholarships, financial aid or other means, and/or some kind of job position so that I can earn the money needed while studying?
After contacting many institutions I still haven't got a positive answer regarding such possibility. I have also read about need blind admissions, but these are very few USA colleges, with extremely low admission rates and other requirements.
Is it the only option available to just try enroll in one of these universities that have low fees, and then do your best to get a job in the city where the university is, and if you can't find any drop out and go back?

Update
@xLeitix said that:

"[...] funded study places basically only exist for the PhD level."

While learning about the early life of Jane Goodall, I have read from this paragraph that:

[Louis] Leakey arranged funding, and in 1962 he sent Goodall, who had no
  degree, to the University of Cambridge. She went to Newnham College,
  Cambridge, and obtained a PhD in ethology. She became the eighth
  person to be allowed to study for a PhD there without first having
  obtained a BA or BSc. Her thesis was completed in 1965 [...]

Therefore she was able to get a PhD in 3 years, at Cambridge, without prior degree and on top of that without having to worry about money. Is this possible only if you manage to get in contact with someone at the level of Louis Leakey? How can I skip all other degrees and just go for a PhD like Dr. Goodall, so that I can get funding like @xLeitix said?

It seems to me, most students have a job while studying. Would that be a problem for you?

I also think many unis have some money which they give as a present to good students but it's not impossible to list all of those stipends here. Have you checked whether the unis you are interested in have some stipends like this?

This is dangerously similar to a "shopping question". Should we specify that we do not want "shopping answers"?

@user111388 That would be a dream, not a problem. The problem is getting such job.

@user111388 Thanks for the reminder. I have contacted many of these universities but never heard of these stipends. I will look more into those.

@FedericoPoloni how can I remove the "shoppingness" from it?

@John I would suggest specifying that you do not want a list of specific programs, but just a description of how scholarships and funding systems for economically challenged students works in different countries.

@John: I belive getting a job is not so a problem. For example, museums often need students to watch the rooms (or the entrance). Or many work in bars, clubs, restaurants...

@John: From the article, it seems that only very few people have the "honor" to go for a phd without previous degrees. So you must be extraordinarily grear (in a few countries, political connections would also matter)

In your circumstances, your best bet is likely to study in your home country. Many EU countries have stipends or student loan schemes targeted at qualifying local students that could otherwise not afford higher education. If you are willing to work next to studying, the world is basically your oyster anyway - many/most institutions in Europe allow students to work next to their studies even if the program is nominally full-time (of course the university does not look for a job for you, that would have to be your responsibility).
In your circumstances, moving to a different country to study will be a lot more challenging. Student loan schemes and stipends are often not available to foreign students, and funded study places basically only exist for the PhD level.

Are you sure about the last paragraphs? Shouldn't EU students be treated like nationals in other EU country (except for maybe some restriced studies like medicine)?

@user111388 Typically the eligibility for such schemes isn't based on citizenship, but often a student is required to have been a resident in the local country for some period of time. Two years, say. On the other hand it may be possible to get loans/stipends from one's own country for studies abroad, of course, with some restrictions.

@Anyon: Okay, interesting! I always thought this was irrelevant in the EU.

@user111388 Pretty sure, yes. Basically no country even in the EU will be happy to explicitly pay for the education of nationals from a different EU country, and all the ones I am familiar with (Austria, Germany, Sweden) have found hacks to restrict access to more or less their own citizens. What Anyon mentions is one of these hacks.

@xLeitix: Interesting! I googled yesterday and found this official looking site which seemed like it was in most cases not as you describe: https://europa.eu/youreurope/citizens/education/university/admission-entry-conditions/index_en.htm Since you talk about Austria, as far as I know, many Germans and South Tyrolians study there - do they all have to pay the university fees?

@user111388 There is a difference between not paying fees and getting a stipend / loan. There are usually no fees (or at least not higher than for locals), but you also won't get money to study (i.e., pay for accommodation, books, food). In Austria, university fees are definitely the cheap part of studying. The expensive part is that you have to live while doing so.

It's possible. At the Bachelor's level, your best option is to be really good and thereby qualify for scholarships. Example at the university level.

At MIT, we admit the most talented students in the world through need-blind admissions. Once admitted, we meet your full financial need for all four years of your undergraduate career.

So once admitted, you're on easy street. Of course, to actually get admitted into MIT is very hard.
Various governments and/or organizations might also offer scholarships, although not all of them go to 100% of the costs. Example in the EU, example in New Zealand.
In all examples you can expect that although the money is there, actually getting it will be very competitive.

im not sure if this is applicable for bachelor

In Finland you can get the same support as natives if you are in the country for some other purpose than studying, such as working. For more information, see Kela's (Kansaneläkelaitos) website: https://www.kela.fi/web/en/financial-aid-for-students-eligibility
In Denmark it is possible to get the same support as native students get; see https://www.su.dk/english/su-as-a-foreign-citizen/. The most relevant method is likely https://www.su.dk/english/su-as-a-foreign-citizen/equal-status-according-to-eu-law/you-work-in-denmark/you-are-a-worker-or-a-self-employed-person-under-eu-law/, which stipulates:

As a general rule, we expect that you as a minimum work 10 – 12 hours a week. Since the decisive point according to case law of the Court of Justice of the European Union is that the employment has lasted for a certain period of time, we also expect that you, as a starting point, have employment to this extent every week for a continuous period of 10 weeks.

I would suggest checking out other Nordic countries, too.

Getting into the best school you can afford, I think, is a worthwhile investment. If you strongly believe that the schools in your home country do not offer what you need, then just keep trying.
Here are my recommendations: Some universities are more interested in international students than others, so, try to look up these types of institutions. Consider doing your degree online. This would save you from the burden of having to relocate. If you need to earn money in school, consider learning an in-demand skill that allows you to earn money while working remotely. If you do this, make sure you are capable of earning money before you relocate.
Do not try to relocate to another country, drop out and go back. That is simply not an option. It is a complete waste of your time, money, and mental/emotional resources. It may even damage your reputation. If you feel that you would be at significant risk of dropping out of a foreign university, you need to reevaluate your plan.

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text:
Appropriate description of image processing algorithm in engineering
I want to write a paper in the field of mechanical engineering. For the evaluation of my experiments, I would like to use image processing. When image processing software is used in my field of research, it is for the most part heavily based on or at least closely related to digital image correlation. However, in my particular case, I believe it is more appropriate not to rely on digital image correlation and solve the problem on my own using "classical" image processing. Using my hardly existent programming skills, I developed some python code and want to incorporate it into said paper. I should probably mention, that the code shall not be the focus of the (mostly experimental) paper and more of an "accessory".
Now, I have encountered a problem: I could not find many research papers in my field that got into detail with their self-written code, especially regarding image processing. Hence, unfortunately, there are few examples to learn from. Furthermore, I do not think that many of the readers of the paper will be familiar with image processing to a greater extent in the first place. However, in my opinion, just stating I developed some software and showing the results is not sufficient.
How much details on my code should I give? Is it already sufficient to show a flowchart and to briefly describe the software? Should I state which python libraries and modules I used? Are there some guidelines that a non-computer-scientist should use when describing software to non-computer-scientists? Does one of you have good examples? Unfortunately, as my programming "skills" are mostly self-taught and rather poor, I am afraid to just upload my code into a repository to "speak for itself" would neither be helpful for comprehension nor good for my reputation.
What do you think? I would very much appreciate all your ideas and opinions.
Edit:
After all, I ended up just very briefly describing the procedure, primarily focussing on the contribution in my field. Neither the reviewers nor the editor had any comments on the level of detail and seemed to be satisfied.

You should provide the source code, as well as the version numbers of all software and libraries you use. The source code is an accurate description of the processing you did, and the only way reviewers are able to evaluate if there are any errors. If you are unable to produce source code of sufficient quality, find someone who can improve it for you. But there is no need to be scared: researchers will not judge you so much on the "quality" of the code, as long as it is correct.

Here's a suggestion: don't make the paper about the code. You used an image processing package for Python (I'm guessing OpenCV?) and that's it. The purpose of the code was to access the functions that are in the Python package. For instance, many papers that use SIFT-based techniques for image processing will simply say just that (e.g. "the images were rectified using a SIFT-based algorithm").

@GuillaumeL I had to do a bit more than just using OpenCV (although I relied on OpenCV to a large extent). I had to implement some of my own functions with certain criteria that need explanation. But I think you are right about just briefly brushing over or even not mentioning the functions that were used from OpenCV without further adaptations.

I'm in the same situation (had to replace the algo used in a opencv based package because it was not robust to the type of images I use). My advisor suggested I briefly explain the changes, maybe add the code in the paper's supplement files, and think about publishing another paper that explains my changes in depth and benchmark my code vs the original.

Your paper should focus on your contribution to the field, not to the field of image processing (if I understand your question).  Your paper will likely have some form of "approach" or "setup" section or something to the effect.  If this is a novel algorithm, you may choose to describe it there.  A common thing to use is a LaTeX "Algorithm" environment, and provide pseudocode.  If the algorithm is very complex or detailed, consider breaking it into pieces.  If it is truly novel, consider publishing it!
An alternative would be to have some way to publicly access the code, say github or your institution's personal websites, with an explanation there.  Again, a pseudocode algorithm is probably sufficient.
If your paper is accepted but you are asked to revise the description of the code, do so according to the comments.  Otherwise, unless there is a lot missing from the main paper's description of its contribution, your paper will not be rejected or accepted based on this detail.

Thank you very much for your input! My paper cannot contribute to the field of image processing, the solution I came up with is more convenient than digital image correlation in this particular case, but definitely not advanced enough to achieve a scientific delta in image processing. However, at least in my field, I have not yet found anything similar to my approach.
I am torn between a pseudo-code and a flowchart. A flowchart could be easier to understand for a less experienced reader, but pseudo-code may provide more details.
Should I consider both describing and uploading the code?

I think that the better question to ask is this: consider your personal strengths.  Can you, personally, describe your algorithm more clearly using pseudocode or a flowchart?  Choose whichever you feel will be more clear given your skillset, and stick with that.  I don't think it will otherwise matter.  I'm certain that making the code freely available for download will also help people to learn from and expand upon your results, but it won't make or break the paper


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Suggestions for learning new techniques during postdoc training
I am about to finish my PhD. My PhD work mainly have computational biology focus(60%) and rest of them are experimental biology. Due to lack of experimental biology  goal I am thinking of learning new techniques during my postdoc training. Although I performed the experimental work smoothly during my PhD training. Is it possible to learn new techniques and experiment during postdoctoral training? or the postdoc PI normally expects that the PhD graduate should know every experimental detail?

I expect my staff to keep learning new things through their entire career.


text:
Seeking Tools for Efficient Illustration Design in Physics Book Project
One of my friends intends to write a book related to the field of physics. I have been assigned to design illustrations similar to the images below for his book. The total number of these illustrations is about 150. I am familiar with tools like PowerPoint, Illustrator, and Photoshop. However, given the volume of work, I am looking for recommendations on tools that publishers use to increase efficiency and save time. If you know of any suitable tools for my work or have any experience in this area, please guide me...
First image
Second image
Third image

Experienced users are efficient with the tool of their choice. So what tool are you proficient with? Graphic design is a profession after all.

I am not a publisher and myself have a similar question, actually. But maybe my experience can be useful to you. I ended up not inventing a wheel and just have been doing all my drawings in AutoCAD. I.e., you don't have to use AutoCAD for technical drawings only. Schematics for papers or books can be done in it the same way we do technical drawings there. Actually, I have moved to LibreCAD since recently, because I prefer open source. And I am just curious why you are talking about PowerPoint, Illustrator and Photoshop. How do these tools can, possibly, come to mind?

Thank you for your suggestion, but that wasn't the answer to my question. @Jon Custer

Your example pictures are all optics drawings. For that context, as well as pulley diagrams etc., I've heard good things about a tool called EdrawMax, which has a lot of suitable templates to speed up the design work.

Thank you, my friend.


text:
My supervisor wants me to switch to another software/programming language that I am not proficient in. What to do?
How can I address my professor’s insistence on switching to SAS when I’m already proficient in R and Python?
I’m facing a dilemma where my professor keeps urging me to switch to SAS for my work, despite my extensive experience with R and intermediate skills in Python. I find SAS to be outdated compared to the flexibility and power of R and Python, and honestly, I really dislike using it. I’ve tried to learn it, but I just can’t find the motivation. It is also disappointing to do things in both R and SAS and sometimes seeing different results. How can I approach this situation without damaging my relationship with my professor or compromising the quality of my work? This is somehow also affecting my health and my motivation for the job.

Why do they want you to switch? "I see you are not making any progress, switch to my pet tool" and "everyone else in the lab uses it, we need your work to be compatible" are different situations.

"It is also disappointing to do things in both R and SAS and sometimes seeing different results" (assuming SAS is indeed the stats software) is kind of a red flag, results should not vary. Results should be independent of the tool/language.
From another perspective, imagine you are the Prof and faced with the following situation: 1) The student (assuming here) uses R/Python and you cannot follow the results. 2) The student uses SAS and you understand the results.
The prof's conclusion in such a situation might be (unfortunately) simple: Student doing #2 works, #1 does not, so do #2.

Frankly, learning yet-another-software-package should not be a particularly large burden, and you get to put experience with that package on your resume which should be a bonus. If you are getting different answers from different packages, that is a whole other problem.

How different are these different results? Is the error like 10^-12 or 10^-2 ?

You will continue to be asked to learn new programming languages and skills for the rest of your career, and you will get used to it.

While I can easily believe that having to learn outdated software would affect your motivation, I seriously doubt that it affects your health. Probably best to avoid claiming that it does.

Are you seriously fighting your professor over him/her making you learn something?

@TerryLoring Maybe p = 0.0499999 and p = 0.0500001.

If you are doing a fairly standard type of analysis, the different results are probably not due to an error in either package, but slightly different methods/calculations. There can be more than one way to calculate regression parameters, standard errors, confidence intervals, etc. Read the documentation in detail, and in R you might need to look at the source code, which can be hard to understand.

@cag51 Mental health is health

True, but claiming "this is bad for my (mental) health!" every time you have to do something you don't want to do is not a good strategy. Not saying that's necessarily what this OP is doing, but what OP wrote could certainly be interpreted that way.

My understanding is some research fields (medical research I believe) considered SAS as the "gold standard" and the core libraries are mature, heavily curated and validated for correctness. If you're in one of these fields expect resistance to R and Python as many will consider the libraries far less well tested - since open source libraries are more likely to "move fast and break stuff"

What is your role?

First step is to ask why the request to change. You've summarized the rationale for your preference, but you haven't indicated what your professor's rationale is. If you're working collaboratively with others, there may be a strong reason to change to match them. Or there may be a corpus of tools in one language in your field. Both of those are strong reasons.
Or your professor may be worried they may not be able to follow and evaluate what you do, or be misinformed about what the capabilities of different languages/environments are. Those are less valid reasons, in that they can be addressed in dialogue, but you need to engage with them.
Finally, there is an important difference based on your role (and funding). If you are working independently on your own research topic, and your professor is an advisor and evaluator only, then ultimately you choose to take or reject their advice. And deal with the consequences, if your choice is suboptimal (for any number of reasons). Versus if you are part of -- and funded by -- your professor's research lab and research grants, in which case you can bring your arguments to the table (and understand and engage with your professor's arguments), but ultimately you will need to adapt to whatever your supervisor, in this case your boss, decides.

It is also disappointing to do things in both R and SAS and sometimes seeing different results.

This is great! It means that you found a bug in your code that would have otherwise gone unnoticed.
If you look around this site, you'll find a lot of "I found a fundamental error in my thesis after submitting it and it freaks me out" kind of questions. Congratulations, you just prevented that from happening.
Use your R skills to your advantage: Learn SAS and do your analysis with it. If anything looks fishy, validate your results with R. Since you have a lot of experience with R, this should be easily doable. If the results differ, find out why and experience the joy of research: Finding out why things are the way they are.
If, on the other hand, learning new tools and digging deep into unsolved problems gives you anxiety rather than joy, that is also an important result: It means that staying in (this branch of) academia might not be the right choice for you, and it's another data point that you can use to decide upon your future career.

Not wanting to dig deep into implementation details of R vs SAS is not a sign of a bad academic. For many academics the software they use is just a tool.

And wanting to understand your tools is a sign of a good academic.

I do agree that getting more perspective from your advisor about why they want you to use SAS is a good idea. For example, it could be that it's something they're more familiar with, and so can directly comment and assist you with your analyses, in a way that would be difficult if they're in a language that they don't understand. Although it may be tedious for you to pick up a new language, there's real value in being able to get direct, specific feedback from your advisor on your analysis!
Something else to  consider is that SAS is still the language of choice for many biostatistics jobs, especially in fields related to clinical science and public health. Although analysis can of course be done in any language, I think the reason SAS remains prevalent in those fields is for regulatory reasons (i.e. they have specific processes in place that were certified in some way, and they use SAS code and so must be maintained using SAS code). If those types of jobs are at all interesting to you, spending some time picking up SAS as a graduate student could be beneficial. Every time I'm on the job market I think "damn, I should have learned SAS" because there are inevitably a bunch of jobs that require years of SAS experience.
Finally, regarding your point about differences in results between SAS and R: make sure to pay attention to the type of sums of squares you're comparing between SAS and R. This may be outdated, but it was a huge issue when I was in grad school in biology in 2010 and the field was transitioning from SAS to R. My memory was that the ANOVA function in SAS provides Type-III sums of squares results by default, whereas the ANOVA function in R provides Type-I sums of squares. So, depending on whether you have interaction terms in your models you will likely see differences in the results unless you specify the same sums of squares. Apologies if this isn't the issue that you're seeing, but this was such a vexing issue for my cohort so I thought I'd mention just in case it was useful.

As most answers have stated, you certainly must have an honest, frank, and open discussion with your advisor to determine why you are being asked (if not required) to use one computational tool when you prefer to use another. The opposing side for your discussion is also important to prepare. You have yet to demonstrate objectively why your preferred choice in a computer tool must be (rather than should be) used. Statements such as "it is outdated" infer subjective "I feel this way (about the user interface)" sentiments. Can you prove that one package uses outdated computational methods versus the other? The note that your results from the two different packages do not agree with each other can mean that you, as the user, are doing two different methods using each one. Can you reconcile your inconsistencies and still find that one tool gives a presumably correct answer while the other gives a presumably incorrect answer for exactly the same computational processing?
Of course, you are not called in this forum to present any of the above supportive discussion, not even in outline form. Debates on the objective reasons why one software platform or application is "better" than another software platform or application can and do fill general forums on computer software as well as support forums for the specific tools themselves. Such discussions belong there, absolutely not here.
So, before you visit your advisor to ask why, you should prepare an objectively researched marketing package to support your case.

Visit the support forums for your two opposing packages. Find what each forum says in comparison to the other. I suspect (without proof, but with confidence) that some hints will be given about the pros and cons of one package versus the other. What do you discover that supports your case? What do you discover that supports your advisor's case? If cost is no limit, is one package generally stated to be preferred over another for the work that you must do?

Document objectively the cases where your time to complete task X in software package A (your preferred) is consistently and significantly shorter than your time to complete task X in software package B (your advisor's preferred). Clear for yourself and for your advisor how that difference is a function of how the two packages allow the work to be done rather than as a function of your clumsiness or lack of experience in using package B versus package A.

My favorite (as one who demands publication-ready quality in outputs from science/engineering software) ... Demonstrate outputs from package A and package B where the results agree yet the quality in the output from A (your choice) is noticeably higher than from B for the same nominal effort in input time/effort invested. Yes, quality metrics can vary from person to person and from discipline to discipline. Yet I would argue that any step in quality that accentuates content over glitter and as well does not loose key meta-information with such things as fonts being too small or esoteric labeling will always stand out no matter who is the final judge.

The opinion that one programming language is just "better" than another is in most cases wrong (excepting maybe esoteric languages). However, it is likely that as a SAS beginner, you are unaware of concise, efficient and readable ways to do the things you need to get done in SAS. Maybe there are SAS idioms or functions that could elegantly solve your specific problems, but there is a bit of a learning curve to finding them. I think this is one of the main reasons why using a new programming language can seem uncomfortable at first.
I think maybe large language models could help speed up the process by teaching you the constructions that are relevant to your use cases.
If you have some code in R/Python that is reasonably small and modular, and if confidentiality considerations don't stand in the way, you could try to see if a large language model can translate it well to SAS. Some of these models are fairly good at small-ish programming tasks (at the time of writing, I'd say Claude-3.5-Sonnet is the best one at that kind of thing), and I would expect that good translations of your code will turn out to be more elegant and idiomatic than what you might produce as a beginner in a new language. If so, having such translations of code that you (hopefully) already thoroughly understand can help you learn that language and do some part of the work of migrating your codebase, to the extent that you are required to.
My guess is that after seeing and understanding that code, the likelihood is high that you will come to the conclusion that either tool has some advantages and disadvantages over the other, and that you can live with migrating.
That said, I agree fully with the previous posters that you should seek to understand why your professor is trying to make you migrate. If you are getting non-equivalent results for the same data with functions that you believe should be equivalent in R/Python and SAS, understanding the reasons for that should also be a high priority.

Using LLMs to translate a code to another language doesn't solve the problem. You still have to know the fundamentals (syntax and semantics) of the target language.

Yes. But neither is it clear that OP doesn't know the fundamentals of SAS (it seems to me he probably does), nor does getting stuff translated automatically mean one cannot then learn from the translation.

This is a good answer, but we academics are so grumpy about LLMs that we do not recognize their legitimate uses, evidently.

The opinion that one programming language is just "better" than another is in most cases wrong ... < Opinions go both ways. Your opinion about opinions can equally be deconstructed as imbalanced because it lacks objective metrics. Granted, the OP may also be mistaking subjective sentiments as being valid, objective reasons to force a decision to go a preferred way. This mistake is however not the sentiment to address as you have done. Essentially, everything but your last paragraph could be taken as an admonishment (polite as it may be framed) to suck it up and learn the other language too.

This isn't really just an everyday question about language choice & simple syntax translation. In the statistical realm, the choice of language itself is nowhere near as important as the availability of a wider ecosystem of third-party extension packages. In the statistical domain, the vast majority of current research and development occurs in open source communities, around open languages like R and Python. 20 years ago, a proprietary environment like SAS likely had the advantage in terms of power, polish, and quality assurance and certification, but the ground has really shifted since then.

Disclaimer: The following can vary by country and by law! Consider with caution:
There is a fundamental difference in:

Professor is your boss, you are his employee and get a salary:
Your boss has the right of deciding how things are to be done. He has managerial/decisional prerogative. I would assume that this is the case in every country (but I don't know in your case for certain).
However, you have signed a contract and in this contract has to be defined what your tasks and obligations are. If you are employed as an R developer, he cannot urge/command you to use SAS now without negotiating a new contract with you or at least try to support you in learning it (giving you time, resources, paying for a course or whatever).

Professor is your supervisor, you are a student writing a thesis or paper:
Your professor most likely has in mind that for the specific research topic "using X because I am good with it" is not appropriate in the context of the research field or topic. This is heavily dependent on the focus of the thesis. In some cases which approach you take does not matter. In other cases it does matter a lot.

Recommendation
Ask him, if the urge to switch is because of

an authority decision where you have no choice
is it considered a recommendation, where you can chose
is it considered important because, you have to proof or give reason why your way is the best/better instead of SAS (within the thesis/paper)
is it considered necessary for the research topic
what are the consequences if you would or would not switch.

If you can talk freely/openly mention your concerns about health and enthusiasm.
Keep in mind: Nobody can force you (this is not slavery). Quitting is your ultimate right to handle the situation. Keep this joker to you for the next conversation though and hope you see clearer after asking your prof.
Even if you are angry, don't say something like "Then I will quit!" just give your thanks and end the conversation. Then go home (not at once, after you finished your hours!), cool off and sleep a night over it. Think carefully about the pros&cons and also get advise from someone you trust (family usually, there are university counselors also you can visit). Only consider quitting if it is okay for your financial situation. But maybe keep looking for a different job to have options in the near future.
Going to work but feeling not happy there is a sure way to burn out. Don't stay in such a situation forever!

Often a professor is simultaneously a boss and supervisor for a graduate student when the student is paid from the professor's grants.

@BryanKrause: I thought about that as well... the paying aspect would make it more of the case of having a contract. The recommendation would stand anyway, would you agree?

+1. Many scholars are simply unaware that in many countries a supervisor is not the boss of the student, and that the student can literally finish the PhD without the supervisor's consent!


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How to handle not being credited for research software development in papers?
I worked for a publicly funded body. Whilst there I designed and developed a number of epidemiological surveillance applications.
I have left the organisation but since then I have noticed that some of my ex-coworkers have started to publish papers based on the data collected by the systems that I designed without crediting myself in the publication (in at least two cases directly referring to my system in the paper).
Since the publications wouldn't be possible without the systems I developed would I have any remit to ask for credit and/or authorship? How is this viewed any different from a co-worker using someone's lab results without credit for their own publication?

Sorry if I sound mean here. Were you the only person who designed and developed the system? To the best of your knowledge, has the system been modified since you left? If yes, to what extent? I am playing devil here in case they refute your claim for the credit.

Two of the systems I was the sole designer and developer. One legacy system was partially developed but not publicly launched when I arrived I had to re-develop and launch it then maintained for the next two years (I probably estimate my contribution to that system as around 60-70% of the source code, although there were 4 previous developers). As far as I can tell the two systems I developed solo have had minimal additional work - a change of colours on the CSS template. The other legacy system has incorporated a feature I started to develop but had not completed when I left (VNTR profiles)

I would also say that all the systems were version controlled - two on github and the legacy system on an internal SVN installation. I do know that they have a new developer in although he hasn't been credited either in the research paper.

Did they acknowledge the publicly funded body in their papers? If yes, I guess you can ask them to add your name with that organization for the contribution.

The authors work for the organisation, two were co-workers (although at the time I was employed they worked in different sections - same department). This is part of the reason why I haven't approached my old head of department since they are also one of the authors. Thanks for the suggestion though scaaahu - guess this is starting to look like one of those "cover all your bases" before you leave a job answers :)

Technically they are not publishing papers about the systems, but about the data collected by the systems. Potentially, since these systems are in Github, anyone could use them, get some data and publish the results. I doubt whether the real contribution is the software or the data but in principle they are two different things to be published separately. E.g. people developing a DB system may publish some papers about the DB system, while people using the DB system will probably just mention they use it (in case it is relevant, and often it isn't).

True - I suppose your argument is that the system is the same as a lab technique. I would agree with you here however since there is no paper on the system (which not only collects the data but processes it) and they have pretty much only used data from the system in their analysis, I would say that not crediting the developers is akin to not crediting PhD Students / Research assistants that gather raw data from lab based analysis.

Thought I'd better clarify - Trylks is correct that since the systems are on Github (published there by myself) anyone could use them. However they both contain GNU General Public Licenses giving credit to myself.

Just add a positive spin, why not just write this up and publish it? Then you can send the reference back to the coworker and hint that from this point and on, they are welcome to cite this method paper when mentioning the system.

Yup that could work although in most cases writing up papers on the various systems would usually require me to use some of the data as examples - even if it was mostly aggregated. Not sure if that would go down too well (especially with the one system that they have already used in 2-3 publications), although I suppose the only way to find out is to draft a version and run it by them. From what i've seen of such papers they follow the format "Since xxxx our system has collated data on x cases and found that.." this is precisely the paper that has been written for at least one system.

Have you tried talking with the authors about it? It is difficult to say whether or not you have contributed significantly enough to the particular work in those papers to have earned coauthorship ... but it sounds to me like you have ... that it's not just off-the-shelf software you developed. I know that if I were using such software developed by a colleague for a paper, I would talk with them and give them the opportunity to help out with the paper and solidify a coauthorship. At the very least, even minor contributions to dedicated software would earn an Acknowledgement in my book.

I can say that it's common for the people working at an organization in the technical fields to not get credit for their contributions. I'm not sure if this exactly applies, but often such staff is looked at as essentially a technical support or IT worker, even if the programming and methodology is original.
If in fact your system is novel or original, then there is nothing excluding you from writing your own research paper and submitting it for publication.
BTW - I'm assuming that the researchers published in peer reviewed academic journals and not white papers?

Yup all the publications have been in peer-reviewed journals (impact factor 5+), the organisation in question doesn't really have a culture of generating white papers (which is a shame tbh since alot of knowledge on these systems is lost as developers move on). The closest recent open access paper that I could find that would probably cover the same sort of ground is http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=20534

@Badroit ironically I do have co-authorship on one paper unrelated to the systems in question with one of the authors, published recently. I think its more of a polictical issue with the people who have been put in charge of these systems. Actually getting them to contribute to a joint paper on the systems could be problematic since they have already published descriptions of systems "they" (I have developed) have developed. But I guess nothing ventured nothing gained - possibly I'll look like a general paper on the system that hasn't been published yet.

A better example might be http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=20237 - although the systems in question are much larger and have been running for a much longer period of time.

Procedures for acknowledging the work of others varies by discipline and journal.  There are currently efforts underway regarding improving data citation, but others felt it better to not lump in software at this time.

If they referred directly to your system, aren't they thereby indirectly referring to you? Certainly, the primary purpose of any reference with respect to authors is to make sure that anyone who wants to find out who was responsible for a piece of related work, and if that is done by referring to the name of your software, that purpose might quite sufficiently be fulfilled. Consider that space is sparse, and if the software used is only a side-note rather than a crucial point of the paper, it may not have warranted a full-fledged citation, or there may have been no space left for an ...

... acknowledgments section. Furthermore, while other papers can generally be easily found once the name of the author is known, the same cannot necessarily be said about non-paper publications, such as software. The real name of the author is not necessarily very visible on sites such as GitHub, and even when it is, criteria such as the software name or the language it is written in are often more suitable for finding a particular software, hence the way in which one effectively provides a reference to software that was used might differ from how to refer to a paper.

As per the comments on your question, I think this is really context specific and the role of an author can vary quite subtly from area to area. I'll try give a general answer to the general question first, and then look at the specfics of your case afterwards.

How to handle not being credited for research software development in papers?

The first question is whether or not you should be credited for the software you developed in the paper. The answer is predicated on what precisely the contribution of the paper is and what the contribution of the software is.
If, for example, (part of) the core contribution of the paper is describing optimisations and techniques that you invented and applied in the software, then I think it is more than fair that you should be credited as a co-author.
If, for example, the core contribution relates to a methodology for doing X where your software was specifically designed for that methodology, then you should probably be credited as a co-author or, at the very least, mentioned in the acknowledgements.
If the core contribution of the paper is not directly related to the software itself but the software is used to some ends, then you should probably not be credited on the paper (otherwise Linus Torvalds would have millions of publications). But if the software system was described in another paper and played a significant/specialised role in the current paper, you could expect a citation.

Since the publications wouldn't be possible without the systems I developed would I have any remit to ask for credit and/or authorship?

I think your reasoning is a little flawed here. Making a research paper possible does not entitle you to co-authorship (as a simple counter-example, if paper A builds upon the results of paper B such that paper A would not be possible without paper B, the authors of paper B should not expect co-authorship on paper A). Providing part of the core contribution of the paper—the reason why it was accepted in the first place—entitles you to co-authorship.
I noticed something crucial in your comments that you didn't clarify in your question:

"Since xxxx our system has collated data on x cases and found that.."

The authors should absolutely not be claiming credit for a system they did not design or build. This is clearly wrong. (And it also indirectly suggests that part of the contribution is indeed the system and the authors are trying to claim credit for it.)
Since you know the authors, you should talk with them, show them the relevant quotes in the papers and tell them that you are not happy with them claiming the system as their own. Tell them that if they wish to continue claiming the system in future then you should be a co-author on the paper.
If you wish to escalate, you can contact the editor(s) of the journal(s) involved and tell them your story. The editor(s) might agree to let you publish a letter referring to the specific paper and outlining your case. This should be considered the "nuclear" option.

EDIT: Not in answer to the question, but this quote in the transcript of Hamming's address "You and Your Research" (well worth a read for anyone in research) reminded me of this question:

I also did a second thing. When I loaned what little programming power we had to help in the early days of computing, I said, "We are not getting the recognition for our programmers that they deserve. When you publish a paper you will thank that programmer or you aren't getting any more help from me. That programmer is going to be thanked by name; she's worked hard." I waited a couple of years. I then went through a year of BSTJ articles and counted what fraction thanked some programmer. I took it into the boss and said, "That's the central role computing is playing in Bell Labs; if the BSTJ is important, that's how important computing is." He had to give in.

Nice answer :) As for the systems in question they are a mix of software and databases - the databases were generated from a mix of legacy data that was in flat files (it had to be validated, parsed and linked to other data before being imported for further processing). I would probably estimated that around 50% of the data on the legacy system was generated by myself the rest being entered by external stakeholders from around the country (before being processed and linked to other data in the system). I'll have to recheck the papers for how the authors have referred to the system(s).

@Pasted : from what you're describing, I'm not sure if you should be recognized for your work with the data, the software or both ... either way, it's important to distinguish between that indirect contribution vs. a direct contribution to the paper.   You can have an indirect contribution without even agreeing with the work; if you were a co-author, you'd be effectively signing off on all of the work done.

@Joe : From what i gather from at least one of the papers, which analyses the data in the system, the paper itself wouldn't be possible without the system. It's not as if the data is available anywhere else or that the processing of the data is trivial. I would think that a lab assistant who say sequenced a series of genomes, the sequence data from which was then used in a paper, should be recognised in the authorship. I feel that my work on the system and integrated data should be treated in the same way.

@Pasted, I think the key point is really whether or not they are taking credit for your work, by claiming that the system is theirs or that they collected and curated the database, etc. Otherwise I'm afraid it will come down to your opinion vs. theirs and you won't have much to gain from that.

The style guidelines used for citations in the paper usually include guidelines for the citation of software. In most cases, the question is not so much who was responsible for writing the software as how can other researchers acquire the software (theoretically to reproduce the results). As others have mentioned, though, if they are discussing algorithms that you introduced into the software as if they were their own work, there may be a significant problem.
Finally, there is the question of the conditions (contractual and otherwise) under which the software was developed and whether that software is somehow unique, or just automates tasks that are otherwise tedious. The vast majority of the software I have written belongs to the people that paid for it to be written and the majority of the credit I have received has been in my salary and my resume, and the occasional thank you from a co-worker that wasn't followed up with a "...now can you change this?".

Ah yes the "thanks but could you move this image 1 pixel to the right"! I do agree with some of your points however I would say that often in a research environment the people who write and maintain the software / databases used by others in the group are themselves looking to work towards a research career (and often have equivalent qualifications). Software development in these organisations (at least in the UK) doesn't pay a fraction of the renumeration available in the private sector and often access to the software after you leave is restricted (due to Patient Identifiable Information).

+1 for the useful link ... but I think you're being naïve in thinking that the salary means the software doesn't need to be credited; if the software is recognized as a vital component to research, it's easier to justify continued funding for it ... and thus you continue to get paid.  (just like recognizing someone's contributions to science as a whole helps them get funding later via grants, etc.)

It's probably too late to deal with this after the fact, but much of the discussions that have occurred over the last few years regarding proper scholarly credit for data also apply to software.  In 2012, I presented a poster at the American Astronomical Society on Recommendations for Data & Software Citation in Solar Physics that is broadly applicable to any research data or software.
The one tricky part is that many journals will consider something simply posted on a website is often considered grey literature rather than 'Published'.  For this reason, it's useful to get a DOI assigned to it, as you effectively make the citation look like any other cited work.  If you're not attached to an institution that can mint DOIs, a possible work-around would be to post something to FigShare and they'll assign one.

Thanks for the link to the poster looks useful. I'll have to read up on the DOI part.

Just to update the DOI reply with some developments with Guthub https://www.biostars.org/p/100669/

@Pasted : um ... that links to something talking about bulk discounts for accounts ... you probably want to refer people to : https://guides.github.com/activities/citable-code/

Ah yup sorry the biostars forum post included links to the citable code URL (or at least it did last time I checked :P)

The key here is that it is a publicly funded effort. Any intellectual property belongs to the public.
Just as if you developed that system for a corporation let say at work. The product is owned by the company. They paid you to develop it.
In your example you do not own the system it was developed with public dollars. Even if you were volunteering the works you created belong to the organization.

Ownership of the system isn't really the issue - it is authors of papers either describing the system or using the outputs from the system and not crediting the actual system developers. However just to take your point to it's logical conclusion - if public ownership meant that anyone in a publicly funded effort, say the Human Genome Project could not be attributed credit then I doubt that such projects would ever be undertaken. For most people in research, citations in papers are as important as renumeration.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


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Would my paper get rejected if the related work section takes up too many pages, and what would the optimal maximum length for this section be?
I have recently submitted my paper to the journal of IEEE Access. They wanted me to only expand the related work section, so I have additionally included dozens of papers, most of which were suggested by them, into this section and will resubmit the paper in the following weeks. However, that section takes up three and a half pages now.
Would my paper get rejected because the related work section takes up too much space? Or, at least would it make a bad impression on reviewers and, if accepted and published, on prospective readers? If you suggest that I shorten this section, what would be the optimal maximum length for the literature survey section for a prestigious journal?
I ask this, because the related work section of most of the papers I've come across in this journal takes up one page at most in general. As I said, when the reviewers asked me to include all the papers they recommended, the related work section had to be much longer.
FYI, this journal has a specific policy such that you can resubmit your paper only once after the original submission. That is, if they reject your paper one more time, no more resubmissions will be allowed under the name of revision.

"most of the papers I've come across in this journal takes up one page or two at most". Are you speaking of the length of the paper or the length of your write-up of them?

Most of them suggested by reviewers?  The IEEE Access has a specific policy against that.  The point of the related works section is to convince the reviewers your work is new.  IMO, the related works section should be at most 1-1.5 columns in length.  However, if it contains irrelevant works, then I would ask for a revision, assuming the work has contributions.    As an aside, I find IEEE Access to be a poor journal with many editors and reviewers with a poor track record.

If the papers were suggested by the reviewers, there are high chances that those papers were from "reviewers" and they wanted to increase their cite count.
In my experience (non IEEE Access), I cited those papers and the paper got accepted. No need to cite additional papers (OTHER THAN THOSE OFFERED BY THE REVIEWERS). I think because the contribution you made was based on your prior study of literature and it should suffice.
If you think that few papers SUGGESTED BY THE REVIEWERS are not under the scope of your research, simply explain politely that "We found paper XYZ to be out-of-scope of our current research and could not fit into our paper. However, paper XYZ-2 did shared some interesting ideas. We have included a detailed discussion on this in Section ABC"... and

Yup.  Consider the cites as payment for reviewers' time.

@Prof.SantaClaus Consider the cites as shameless plug of the reviewers, unless they are really relevant. It's ok for reviewers to mention papers of their own that are really missing, but it's patently not ok to self-advertise. Unfortunately, whether the editor is going to protect the author from pointless citations or not is not always obvious from the outset.


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Is it common and acceptable for a postdoc to leave before contract ends?
Is it common and acceptable for a postdoc to leave before his/her contract ends, for whatever reason? How early should a prior notice be submitted in this case?

To add to previous answers: if your postdoc includes teaching, you should finish out the academic year, or at the very least finish the current term, and give your department chair as much notice as possible, so that they can find a replacement or adjust teaching assignments.

Indeed. When I switched jobs, I made it a point to fly back to my old university to wrap up teaching. To me it is just common curtesy to wrap up your loose ends as good as possible.

In my experience (I'm a postdoc now, and hence also know a few other postdocs) leaving before the end of a contract is perfectly normal. The nature of postdoctoral work is that you have to be ready to take the next opportunity when it arises - other people certainly aren't going to make sure the timing lines up nicely for you! Of course if you leave very early in the contract, that might annoy people, but assuming you're reasonably sensible, everything should be fine. Remember that the people employing you have either been through the same process, or observed it over the years1.
As for how early the notice should be, that depends on the contract and the labour laws where you are. There may be certain legal minimums that have to be observed, but the figure seems to normally be 2-3 weeks. Of course your employer may let you out early by agreement, but as your employer is probably technically the University, don't expect a bureaucracy to be particularly flexible.

Little side note, I gather this might be a bit different in the US, though currently in the process of changing (perhaps someone who went through the US system can add to this). A postdoc in the US has lower status that it does in Europe/Australia/New Zealand/..., so it might be the case that your employers haven't been postdocs at all.

I am currently a Computer Science postdoc in Europe. I have the understanding (and full support) with my professor that I will be gone if a faculty position presents itself. I guess, given that postdocs are really just researchers in queue for a faculty position, I am assuming other postdocs will have similar arrangements.
Other than that, I am assuming whether your professor is annoyed by you leaving depends on how much he depends on you, how much time you give him, and how valid your reasons are.
Before my current job I did postdoc at the university where I also received my PhD. I told my old advisor many months in advance that it would be good for me and my CV to leave my almer mater and home country, and he was fully supportive (== good reason, many months of changeover time). On the other hand, the same professor was pretty pissed when another postdoc quit his position to work in industry more or less without prior warning. Circumstances matter.

In math, it's reasonably common for people to leave a 3 year postdoc after 2 years to take a tenure track job (or rather, it was reasonably common before the job market crashed).  But fields which have postdocs attached to particular grants may be quite different.

It should be indicating in the contract, and as long as you are following the written contract, the professor should not take it against you. It is like any other job.
Although, in my experience I have found professors who are not professional enough to respect that. It kind of questions the recruitment process for faculty positions, a mere PhD or some publications should not be the only factor when choosing a faculty member, it should also consider the moral ground, leadership skill and ability to motivate. The same way the professor is not your mom or dad, the student is not your maid.

I am a postdoc and I believe that it is right of the postdoc fellow where ever he wants to go though he/she should tell professor few months in advance so that professor can arrange some other postdoc. And if possible, it is better to finish your current project. Professors usually have funding and therefore it is not difficult to find the postdoc replacement for them. However, the postdocs are struggling for a permanent position that is quite competitive now a days specially in some high ranked universities.
There are normally two situations I have observed from the research point of view that a postdoc likes to leave. One is that the professor does not provide good feedback to his progress due to either he has no time since he is more interested in applying for funding or he is not capable answering postdoc questions (it does happens). In both cases, postdoc should leave and make his career better somewhere. The second reason of leaving the postdoc is when the promises that professor did at the time of hiring are not fulfilled. For example, if professor tells you that we are doing empirical as well as theoretical study of such such things and postdoc job will be to make such and such models and analysis, however when you start working, you come to know that they don't have any data and they will get it right in the middle of your postdoc when half of your time will be elapsed.
Sometimes you really feel that your research philosophy is not matching with the research objectives of your group. In such situations, it is beneficial for both to set apart as soon as possible so that professor's funding and postdoc's time can be better utilised.

In the USA, postdocs don't have a contract.   Instead, there is an informal agreement between the postdoc and the advisor, that
a project be completed.  If the project proves to be a dead end,
then another project is agreed upon.  Leaving a project is viewed
negatively, because in the USA, the advisor typically must invest
at least $300, 000 on salary and supplies for the project.  If the postdoc leaves - that investment is lost.    For the advisor, the
failure of completion of the project could jeopardize the success of future funding for their laboratory. In the USA, in addition
advisors must maintain a certain level of laboratory funding to
keep their laboratory space.  So, no leaving the lab without finishing the project is not viewed favorably.

Untrue! See here for example.

In the USA, some postdocs don't have a contract.

If either or both parties are miserable, then this is not indentured service and therefore the contract should be terminated. However, the termination shouldn't be to the detriment of the lab i.e. if the PI needs time to hire a replacement, then the postdoc terminating the contract should stay on until the new hire is made and trained.
Sometimes it's better to let a toxic person leave rather than remain for their technical skills. I hired a very unpleasant postdoc in 2012 and luckily she resigned while I was still trying to figure out how to work with her. Now she is probably making her new boss miserable. She never asked for a recommendation from me so perhaps her 5 month stint in my lab was overlooked when she was recruited.
Postdocs out there take note:

Your boss is not your mum or dad, so if you hate either or both of your parents, punishing your boss is just psycho behavior
Your boss is not your mum or dad, so if either or both dote on you and spoil you, consider that your boss is not obligated to treat you that way. A postdoc is a trainee position but it's also a JOB. There are expectations and you are being evaluated by your boss/mentor and colleagues in your field. You are not a student anymore, so time is precious and is not to be wasted with drama and procrastination.
If you regularly got 'A' grades as an undergrad and graduate student, beware that these top grades may have been due to grade inflation. You will now need to get used to the idea that there is grade deflation for academic professionals and faculty. Peer review (journals, grant applications) is harsh and often brutal and you have to deal with it. Your postdoc mentor is not your enemy and can help guide you through that process.

This seems less like advice for the OP and more like a bizarre rant about a postdoc that you didn't like.


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What does top-tier mean when referring to journals/conferences?
I see people here often use the term 'top-tier' when referring to journals/conferences. Is it used to refer to the top publications in terms of metrics like the impact factor? Or is it used to refer to venues which are traditionally highly-regarded but not necessarily having high impact factors?

Top tier can mean a lot of different things to different people - and indeed, can mean a lot of things to different fields. Ask a diverse group of academics drawing from math, health science, and the humanities what a "good" impact factor is sometime if you have the chance - the variability is massive.
Personally, for me, when I say "Top Tier" I'm usually thinking of soft factors - journals where solid publications go, venues that you'd highlight on the cover letter of a CV, etc. That often has an association with Impact Factor, but it's not necessarily fixed. For example, PLoS One has an impact factor that's about on par with several journals I'd consider "Top Tier" in my field, but PLoS One isn't on that list.

I many parts of Asia "Top Tier" tends mean journals and conferences that are high ranked by their National Research Foundation. The NRF tend to rank journals based on journal index, and other criteria.
In Europe top tier tended to mean the journals that the respective community believed to be of high quality.
Very generally, journals could be ranked by impact, but even high-impact journals may not be considered top tier by your research community / university / funding agency.


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What is normally done if there is an underspending of a research grant?
At the conclusion of a research project, what is normally done if there is an underspending of the research grant? Is the remainder returned to the funding agency? If this is true, how is it possible to build one's funding over the years?

NSF calls it a "No cost extension." I believe it is discretionary. Unfortunately their website is not currently operating.

First, you need to plan expenditures to make sure you don't end up with too much money at the end of the project. If you have grants, you're essentially running a small business. It's your job and responsibility to have a budget, plan with and for it, and to track expenditures.
If, however, for reasons that may not have been under your control, you end up with unspent money at the end of the grant period, you can often ask funding agencies to give you an extension of the project. For example, the National Science Foundation pretty regularly provides a one-year no-cost extension on "normal" grants. To get a second year, however, you probably need to have good reasons, or they will take the money back.

I've had several no-cost extensions on a single grant, but this may depend not only on the funding agency but on the director of the particular program. My impression is that the NSF would rather not have to take money back if you can legitimately spend it; if they take too much money back, they might get less money next year.

Yes, I think that is correct.

Is this applicable to the US only or can it be generalised to other countries?

I have no idea. You'd need to ask someone in the country you care about whether they have no-cost extensions.


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How far can research deviate from the original research proposal?
During the course of a research project, one may have new, more promising, ideas that were not foreseen and thus not covered in the original research proposal. What should one do in this case? Is it all right to work on the new idea under the original proposal? Or should one stick to the lines suggested in the original proposal and perhaps submit another proposal for the new idea although this means that one has to wait before he can work on the new idea?

See this question and this question for related discussion.

Smart colleagues of mine avoid the problem by asking funding for research they have already almost completed, which allow them to carry on new innovative research with the current grant. Being one funding ahead seems like the best strategy in this context.  It is also less stressful if you can manage it.

There's two opposing forces here:

You're probably not doing research if you can say exactly what you are going to do beforehand in the research proposal. At the heart of research is exploration. Hence it is expected that you demonstrate some agency and adapt the research as you explore the topic more.
On the other hand, the people who fund you are funding you to work in the area of the proposal. Switching to a completely disconnected topic on a whim is (generally) not an option.

How far can research deviate from the original research proposal?

This is difficult to give a concrete answer to and it will differ from fund to fund. Most generally, if you can show the funding body a path from what you proposed to what you ended up doing (i.e., you can show that your intent was to follow the proposal, but you discovered new evidence that a better alternative was possible and you followed that alternative instead), then I think you're on safe ground.
In other cases, it will depend entirely on what your funding body is looking for and how they justify their funds to their bosses. You will have to try understand what the objectives of the funders are: is it publications, creation of a product, teaching and dissemination, contributing towards some high-level national/EU directive, etc.? Will your new direction likewise satisfy those objectives under what was originally agreed in the proposal? These are important questions. (Even if you stuck steadfastly to the topic proposed, groking the motives of your funders and what they will look for during reviews is vital for success. If you understand these motives well enough, then as a side result, you'll have the answer as to how much deviation is acceptable or even desirable.)

+1 for "you need to understand what your agency actually wants". I have seen people get money from a company to do X, did Y instead because it seemed more interesting, and were surprised when the company told them that they really wanted X because Y solves a problem entirely unrelated to them.


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Should one disclose his/her family information in an academic job interview?
Should one disclose his/her family information (i.e. whether he/she is married and have children) in an academic job interview, including postdoc interviews and interviews for PhD, if any? In what situations would disclosing family information in such interviews be considered advantageous or necessary for the candidate? If not appropriate during interviews, then when would be the best time for one to disclose his/her family information?

This may actually depend upon the country one is interviewing in. Such questions may even be illegal in some countries, as it may lead to discrimination.

... whereas in other countries family status is usually visible in the CV...

@DaveClarke: It may be illegal for the interviewer to ask, but the interviewee could still disclose voluntarily.

I have been part of several hiring committees in a New Zealand university and:

We are instructed to never ask about a candidate's family situation, religion, sexual orientation, etc.
Personally, I do not want to know about your family situation to avoid any perceived bias in the hiring process.
Any mention of the situation on your part (like 'we are a bundle') could be perceived as making the hiring more complicated. Unless your application is miles ahead of the rest (unlikely) why would a committee want the extra complication?

The only situation where I could see an advantage is when you family situation would suggest higher chances of success. For example, you are applying to a job in the middle of nowhere (and potentially candidates would feel isolated) and your family happens to be from there.
I would wait to say 'we are a bundle' (if that's what you have in mind) to when you are offered the position. Ideally, you would never have to disclose your family situation until the university is willing to pay for your moving costs.

I heard the following advice from several people; I have not been involved enough in hiring to know if it is right, but it sounded right to me:

If you would not accept a job offer unless the university can find a
  position for your spouse, then you might as well disclose that
  information early. The universities who do not make offers to you because of your    disclosure would not have made offers to your spouse had you kept it silent and revealed later.

If you do want to follow this advice, you will have to bring it up, since HR should have told interviewers not to raise the matter.

Are you referring to the case where one's spouse also works in the academia?

In academia loosely defined: For example, my wife works in medical research administration, so universities are major employers for her (as are hospitals or pharmaceutical companies) but she is not an academic in the sense of someone in the standard tenure track.

One situation in which I have seen this work to a candidate's advantage is where the candidate has an extraordinarily strong record. Schools knew going in that solving the two-body problem was important for this candidate, and the university that was fortunate enough to make the hire did precisely that.


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Withdrawing my application for a research fellowship when getting a job elsewhere
While waiting for the outcome of my application for a research fellowship, I was offered a job at a university in another country. It turned out that my fellowship application passed the first stage, but after some consideration, I decided to accept the job offer and not to pursue my fellowship application any further. My question is, should I tell the secretariat immediately that I would like to withdraw my application or should I just let the deadline for the second stage of application pass without informing them anything? Also, what should I say to my potential collaborators?

It is customary to withdraw your application(s) as soon as you have accepted a post elsewhere. By doing so you increase the chances of fellow applicants to be shortlisted / admitted to the next stage.  Also, non-attendances and late withdrawals create a considerable mess at the interview stage, and it is better not to annoy your prospective colleagues, with some of which you may need to collaborate in future.
Actually, prompt communication is typically a win-win situation, and unnecessary delays in communication usually give nothing good to anyone (despite maybe some illusion of control).

Thank you. Could you answer my second question as well, please?

@adipro - If there is some reason you are hesitating to let them know as well that you have accepted a post elsewhere, please explain.

@aparente001, I am not hesitating; I just would like to know what is normally said.

I am not hesitating; I just would like to know what is normally said.

How's this as a starting point?

Dear [name of collaborator],
I wanted to let you know that I've taken a position at __________, and have withdrawn my application at _________.  I took this decision for a variety of personal and work reasons, and I think it's going to be a positive move for me.
I hope that we can still collaborate in future, albeit perhaps in a different way than we were hoping to do.  OR This means that I won't be able to work on any collaborations with you in the foreseeable future, but I wish you well in your project.

What will help you write this letter is the conviction that if you were in their shoes, you would want to be notified, the sooner the better.
Feel free to provide more information about your situation if this draft doesn't suit your needs.