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My supervisor is making me work on something that is not my PhD project. What should I do?
My PhD project is a very little part of a big study that my supervisor is currently running. I am already four months into my PhD and I just have a 3 years position to finish my thesis. So far I haven’t worked in my PhD topic because I’m helping her with other aspects of this big study. She says doing that is working on my project, however she asks me to write and do research in things that are not really going to be on my thesis. And during all our meetings so far, we haven’t really discussed my topic, the chapters of my thesis, my research questions, the literature I should be looking at, or the instruments I need for answering my research questions What should I do??

Please add a country tag. In the UK it would be highly unusual to discuss thesis chapters by month 4.

Also it would be helpful to know how defined your PhD topic is. What I mean by this, is that I've heard of cases where what gets chosen before the PhD beginning needs to be your thesis title, but quite often I've seen people allowed to slightly adapt their thesis title as they progress, the topics evolve, the questions one was hypothesizing about in the beginning change completely. Have you discussed how flexible your topic is with your advisor? Most common practice I've seen is to try and include any and all research the student did into their thesis; why is this not true in your case?

Make her employ you for this separately. Or get a research engineer. She probably doesnt want to because those usually  cost more.

Even if I indeed consider that your situation is a bit extreme, please bear in mind that (to me), the purpose of a PhD is to teach you how to "do science". So it is at the end of the 3 years that you have to show that you are now able to do proper research. So it can be helpful at the beginning of your thesis to bring you onboard of ongoing projects so that you can observe how science is "done".

And during all our meetings so far, we haven’t really discussed my topic, the chapters of my thesis, my research questions, the literature I should be looking at, or the instruments I need for answering my research questions

So, that seems to suggest a natural course of action.  Let her know that at your next meeting, you want to focus on making concrete plans about your thesis.  Then do it.  Come with a list of questions and make notes.  Be sure to bring up the topic of how the big study will tie in to your thesis work, and how she expects you to balance the time spent on both of them.
It sounds like maybe you've been pretty passive so far in your interactions with her.  Clearly it's not working, so time to try being more assertive.
If she won't have that discussion, or won't stay on topic, then you probably ought to start looking for a new supervisor.

Both existing answers provide useful perspectives, but one aspect that I feel is missing is: how are you funded?
Option 1:
If your PhD is being paid from funds of the "larger project", you will invariably need to do work that comes up in the scope of this project, even if not all of it will directly contribute to your PhD. Raising the issue with your supervisor that you only want to do stuff that directly relates to your thesis is unlikely to go over well. Of course, both, you and your supervisor, need to ensure that your work can ultimately be framed in a thesis, but you need to see this as a discussion of "how can we turn what I am doing in the project in a thesis?" rather than "I will stop doing work on the project and work on my thesis instead".
Option 2:
If you are not funded, or through a stipend that comes from an external source (e.g., a stipend from your home country, rather than a project acquired by your supervisor), using your workforce to contribute to this project becomes a bit more dubious. Even in these cases, it still often happens on a tit-for-tat kind of basis - you help your supervisor in their project and get something else in return. However, the parameters of this exchange should be much less of an automatism, and there should be more an open discussion between supervisor and student. If your supervisor continues to see you as free labor for the project, it may indeed be time to move on.
Option 3:
If you are funded by your supervisor, but through a different project, things tend to get a bit muddy. In theory you should work on the other project which funds you, and not on the "large project", but in reality borders between projects of the same supervisor tend to be blurry, and (again) refusing to work on a specific task may not go over all that well. In practice I would recommend threading this the same as Option 1 (make the best of it, and ensure that your work culminates in a PhD), but if you end up doing the work for multiple projects you may need to raise the issue friendly with your supervisor.
For all variants, it cannot be overemphasized how important it is that you are (1) proactive (as Nate correctly observes) and (2) transparent with your supervisor. You and your supervisor typically share the goal that both of you want you to finish in time, but for you the stakes are much higher than for your supervisor. This makes it imperative that you communicate what challenges you see actively and openly, and don't just expect that your supervisor will make things right for you.

Thesis structures can be rather fluid for at least the first year of a PhD.
An anecdote to hopefully encourage you: The main topic I started on looked like a dead-end for over a year, starting about 6 months in. While that was making no progress my supervisor got me working on a loosely-related  system, which turned into a first-author paper and a results chapter. Then we found a way to get results on the original topic as well, but the digression actually provided a nice introduction to the problem we were trying to solve.

There's not really a need to have a "why am I doing other peoples' work?" conversation.
Flip the issue, and manage it in a positive way by managing your own project's time aspects.  Instead of worrying about whether this is a side trip, I suggest working out a careful schedule with which to complete your studies within your time limit.  A Gantt or PERT chart would be a preferred vehicle.
Now, once you have that, sit down and discuss it with your mentor.  See if she sees any time issues you may not have anticipated, or has any other suggestions to offer.
After you've both agreed that this is a reasonable schedule, I suggest meeting with your mentor just to go over the status, and handle the inevitable changes that will come up.  I think every two months will be a good starting point, unless higher frequency becomes necessary.

Seems unrealistic to me. Go rogue? Will the advisor likely permit it?

@Buffy -- this is the opposite of going rogue -- it's developing a plan, getting input from the mentor on the plan, modifying the plan, and then periodically reviewing the plan.  What I didn't say is that lots of "extra" stuff can still happen, but as long as the timetable keeps ticking along, both the student and the mentor understand everythings OK.  If the timetable gets interrupted, then both the supervisor and mentor will notice before too long.

I won't tell you what to do specifically, but I will suggest an approach for making this decision.
The conundrum you present goes to the heart of the question of what is a PhD candidate, really?:

Are you a student who's getting financial assistance to focus on your studies?
Are you a hire of your funding entity (e.g. the supervisor's research funds), working towards fulfilling its needs in an "academized" settings, with a degree on the side?
Are you an employee of your university as a whole - with it being an institute which does research and you being one of the people who carry out that research - who is a sort of a junior version of a Professor?

I suggest you have a look at these two questions here on Academia.SX:
Education or employment: What is a post-doc? What is a PhD student?
Why do universities fund Ph.D. students in the sciences?
(and specifically in my answers... but not just them.)
So I see your dilemma as an incompatibility of expectations: Your supervisor treats you as an being in the second category I described, while you yourself perceive your status, or intended status, to be more in line with the first and/or third categories. Also, the 3-year restriction on funding * make for a harsh conflict of interests in the allocation of your time, and forces at a decision, albeit a temporary one, regarding your effective status.
As for concrete advice: If I were in your position, I would try to:

Get my Graduate/Junior Researchers' Union to to give me advice and possibly to either intercede on my behalf or to back me in demanding that non-thesis-related work be contingent upon either additional compensation or extension of the funding period.
If such a union does not exist, become active in forming one.
Read up on all relevant regulations, for students and academic staff, to determine what rights and privileges are specifically afforded to you and to your supervisor, so that you at least know whether your supervisor has formal justification for requiring this work.

but that's not to say that non-thesis-related work is a bad thing, or is not useful to you, in itself.
* - Speaking from the experience of most people I know and most universities and countries I know about - You are unlikely to conclude your PhD in 3 years. Either you get more funding somehow, or finish the rest while unfunded, or start doing work elsewhere while concluding the latter parts of it.

Regarding the 3-year time limit: come countries, 3 years is actually common and people do finish within the timeframe. I did my PhD in France, where 3 years is a standard duration - and was told in fact that if I were to continue my career in France, a duration any longer than 3 years and 3 months would be something to explicitly explain when applying for academic/research positions. In the lab(s) where I was, some people did get different funding, a job, or finished their work while not paid, but a) that was not the majority and b) most of the ones that did finished within half a year tops.

@penelope: Ok, I hear you. France is not one of the countries I have knowledge of in this respect, though; they are Israel, the US, the UK and the Netherlands (and for the latter it's partial). And maybe a bit about Germany.

Just trying to say, it might not be as universal as you imply :) Tho (maybe since I did my PhD in France, so I only notice the familiar thing), most of the PhD students in my new lab in the UK seem to be finishing within the 3 years on their grant, with an odd one taking the writing "home" after the funding expires, but still returning for the viva within at most half a year. But, I might just be noticing those cases better as they are what I'd expect.

You have not provided a basis for whether you are required to do course work in your PhD Program of Study. When no courses are needed, four months in to a three year project is ... awfully short. In some respects, this is a "let me stick my toe in this water" period. When the three years includes perhaps a year or more of course work too, you are still well beyond the point here to worry. Allow that you will explore the topics that are presented by your advisor and find your strengths. After all, by the end of the dissertation, YOU should be advising your faculty advisor what is and is not appropriate to do because YOU will be the expert in your study. Also, a PhD should be based on YOUR choice of a topic, not on a choice handed to you by your advisor.
In any case, at some point, you should recognize the need to establish a Dissertation Committee and defend a Dissertation Proposal.
A Dissertation Committee helps the faculty mentor to guide the student to success. Our Dissertation Committees consist of four other faculty besides the advisor, with one outside the home department.
A Dissertation Proposal provides a foundation for both the mentor and the student to establish a common ground for the work. While it may include preliminary results, the proposal is NOT about work that has been completed. I believe the proposal should be more than just a "white paper". It should be written somewhat in the format of what is required by a national funding agency to solicit for research support. In the sciences in the USA, the template might be an NSF or NIH proposal. In engineering, the template might be a DoE proposal. I defer to other countries and disciplines to substitute their appropriate funding agencies and templates.
Our program asks the graduate student to defend a Dissertation Proposal in this way. First, the proposal is submitted to the Dissertation Committee. The Committee reviews the proposal over two weeks. The Committee can submit questions in writing to the student over this period. The student has one week to review the questions and reply in writing. The student then gives an oral presentation to defend the proposal and the answers to the questions. The Committee passes or fails the student to continue the research work.


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Building an academic career abroad
Good side of my background
I'm 27 years old with a Ph.D. in electrical engineering and now I'm about to finish my postdoctoral research.
During my Ph.D. program, I've developed an innovative device that successfully passed a trial operation period and entered into commerce operation. I'm one of the authors of the patent on this device.
Also, I have a number of papers published in scientific journals that are widely known in this field in my country. I have experience in lecturing and supervising students.
Bad side of my background
All of my accomplishments are made in my country's language, not in English. Almost all of the engineering journals in my field are not indexed in Scopus or Web of Science (some of these have translated versions that are indexed).
In short, I'm known in my field in my country only, not world-wide.
Does it make any sense to try to get an academic job (tenure track) with such a background or it's better to build my career "from scratch"  by publishing new research papers in English so that I would get a chance ?
I'm interested in getting a job in Europe or Canada.

My guess is that you could move without a lot of turmoil. As you describe your background it seems pretty solid.
But you could try an intermediate approach first. I'm assuming you are still active. I suggest that future papers be in English (or some European language) and submitted to journals there. I also suggest that you make sure to reference your older work that isn't in English as appropriate.
You can, in parallel, see to getting some of that work (the stuff you cite) translated. If you need practice in English you could use the older work as practice to get an English body of work available, even if on some informal sites. You are probably the best person to do this sort of translation, actually. And if it improves your language skills, so much the better, though you may not need that, I realize.
You can also, if funding is available, start to make a presence in conferences in one or more of the countries you'd consider moving to. Preferably get on the program with talks on new stuff that references the larger body of work.
It might be easier to move if you establish some visibility in the world you want to move in to, but it may not be essential to do that.
And if you have a tenured position somewhere, or even a tenure-track position, you can often move and get some credit for that, resulting in a shorter probationary period than the typical (for the US) seven years.

No, I could not in good conscious advise you to start from scratch just because your publications are not in English, especially given that you clearly have mastery of the English language.  What you need to happen is to be judged for the work that you've done.  Here are two options:
1) Create a detailed summary of each of your papers, in English, to go alongside each of your publications
2) Translate, yourself, your top 3 publications into English
3) Hire someone to translate all of your papers into English.
The point is, you are your work, so let your work be known.  You still face a hurdle with the journals perhaps having low impact factors being outside the English speaking world, but you can work through that to a large extent.
Good luck!

Do you mean that translated publications or detailed summary are needed to be sent to my potential employer or it's essential to publish it in journals?

I don't think publishing translations is feasible or reasonable.  Just make translations available.

Sorry I did NOT mean publishing translations. Just making them available

To build an international career in academia, you need to show mastery of your field. Your background makes good progress, but you've yet to publish in an internationally leading venue in English. This creates a risk for any potential employer. A further postdoctoral position overseas could help mitigate, if you can get one. Regardless, you need to showcase work in English. That could be future work or existing work, e.g., you could publish or extend & publish some existing work (depending on rules of publishers).

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What if PhD application date is closed?
I sent an e-mail to a professor along with my CV explaining my research background and expressing an interest in joining his research group as he has asked to send CVs if anyone is interested in joining his group. After a few hours, I got a reply from him saying that my research background is quite relevant to his. He has also encouraged me to apply to the college and take the English tests.
When I checked the college website, it seems that the interview date for the possible candidates has already passed and they have stopped taking further applications.
I haven't yet taken the English exams as well. What should I do? Shall I send an application anyway? Is there a chance for me to get the scholarship?

If someone has told you they are interested and to do it, where is the doubt? I say do it.
You can always send the professor an email saying that you have seen that the application deadline is over, and ask if they think you should still apply.
A lot of PhD offers have no academic deadline other than the funding deadline, very often you can just start whenever.

He has not mentioned that he is interested. He has just said that my research background is similar to his. And has said that he encourages me to apply. The problem is, I have to do a master and then do the phd. They have a separate track for students without a master. So for that the application date is closed. Do you still think it is worth to do english exams and apply?

@Umayangani so he encourages you to apply, therefore he is interested in you applying no? If he had no interest, why would he encourage you? Don't overthink it, apply. If you are in general interested in PhD, you will anyway need to do an english exam, so you are not wasting your time on doing it. I dont understand your worry, what do you fear to lose by applying?

Ok, i will do the exams and apply. Do you think it would be good to send a reply to the professor asking if he thinks that I should apply even if the deadline is closed? Thanks you for helping me out. :)

@Umayangani yes, it is not a bad idea. Maybe the professor did not realized or maybe he thinks that you are OK with waiting a year, or maybe he has a position for you to start right now. Just ask. Note that in some countries (e.g. UK) a masters is not needed to get a PhD.

One more question, I initially addressed him as 'Dear Dr. XXX' and he has sent a reply saying 'Hi '. Should I still send a reply addressing him as 'Dear sir' or is it okay for me to say 'Hi' too?

@Umayangani Adress him with the signature he uses in the bottom on the email. Again, do  not overthink it, he is just another human being. #professorsarepeopletoo.

Don't apply blindly, because there's a good chance that an administrator will filter it out as "past the closing date" and delete it.
But it's reasonable to respond to the professor explaining that it's closed, and asking if you should apply anyway. As others have said in comments, don't overthink this - professors are normal people, and you can interact with them in normal professional language!

In the United States, the closing date for applications is generally set at the level of the university, not by the department. If you missed the deadline, you will have to wait until next year to apply. I'm not aware of any exceptions being made to an application deadline.

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Importance between the American Mathematical Monthly and Research Journals
I am an undergraduate and have been working with one of my math professors over the past year on proving some new results.  These results are interesting and are approachable enough to be understood by most graduate students.
My professor and I have agreed that this work can certainly be published in a "professional level" research journal (I would be first author), however because the problem is approachable and would be interesting to a wide audience (and is not highly specific) we think it could be a good fit for the American Mathematical Monthly.
I want to go to graduate school, and I am wondering if publication in the American Mathematical Monthly would carry more or less weight in graduate school admissions than publication in a journal specific to the research area of the work (dynamical systems).
I understand the AMM is geared more towards publishing expository work, however publication in the journal would tend to yield more reads than in a more specific journal (I am looking at the Journal of Dynamical Systems and Geometric Theories for example).  Which would be best to publish in, or look best to a graduate school admissions committee?

This is likely to be closed as a shopping question, but having coauthored a paper that appeared in the Monthly would be a very positive factor in your applications to graduate programs.

@BrianBorchers Thanks, how could I change this post so that it will not be closed?

Perhaps one of the moderators will have a suggestion.

Actually, I disagree that it is a shopping question.

@Buffy but it is opinion or is there a definitive answer?

Why not pursue publishing it in both? I was considering writing an answer saying as much but I don’t know the norms of that field very well.

@nick012000 in maths it is extremely frowned upon to send the same paper to two different venues simultaneously; I would go as far as to say that would be unethical conduct. Of course the OP can try one option and then, if they don't succeed, try the other

@YemonChoi Obviously not writing the exact same paper in both; one would be a more general audience/science communication  “look at what we did” thing while the other would be more formal “this is our methodology, experimental results, and conclusions based on those results”. Does math not do that?

You're probably right that more people would see something published in the Monthly than in a "research" journal. But if you are aiming primarily to optimize the positive impact this paper would have on a CV for grad school (in the U.S.) I think it would be much better to say that it has been published in a good-quality research-oriented journal, since that will attest much more strongly to the quality of the research (as opposed to attesting to general interest).
EDIT: in contrast to some other answers' implications... The Math Monthly is not at all a research journal. It is an expository and (mostly elementary) problem/puzzle-oriented journal. Publication there is viewed (in my experience, and in my own opinion) as a good thing, but no indication at all about "research potential" or such stuff. That's not the fundamental criterion for publication there!
So, if you are wanting to give evidence that you have already made (and thus in the future can make) research contributions... publication in the Monthly gets you close-to-nothing. Sure, "it's nice", but it's not at all about making significant new contributions to live subjects. It's an entirely different thing. It's not just "a somewhat different journal"... it's in an entirely different category.

The Math Monthly might be the right choice if the OP were interested in a math ed degree. Otherwise I can't imagine why anything publishable in a decent research journal would be submitted there.

+1.  Let me also point out that the Monthly has very high expository standards, and trying to write a paper suitable for the Monthly may be a lot more work.

(On the other hand, I think for a joint paper of an undergrad with a professor, it's hard to tease out too much about the research potential just from this, and one really relies on the letters...)

I can't speak for other academics, but I think it is unlikely to make a large difference which journal you choose.  Whenever I assess a prospective graduate student for a PhD program, if they have published papers, I read these to get an idea of how well they can write, and the level of insight in their existing work.  (Applicants for graduate school will usually have no more than a handful of publications, if any, so it is not onerous to read them all.)  Since I am reading and assessing the content of the papers directly, it really makes no difference to me what journal they are in.  Even if the student had published a good paper in a predatory journal with no real peer review process, if the content was good, it would be a good sign for their application.  (Although it would mean that the student should be advised to seek publication in a better journal.)

If you are in the US and intend to go to grad school in the US, I wouldn't be too concerned about this. Few undergraduates in the US have any publications at all and so having some record of published results would make you stand out. One advantage of the MAA is that it has wide readership, whereas a more specialized journal will only be seen by a few.
But it is the quality of the work itself, not just the journal in which it is published that is important.
Also, if, as you say, the problem itself is approachable others similar to yourself would benefit from a wide audience.
However, if you aren't close to graduation, then you could explore publication in a more specialized top journal and see what reception the paper gets. You can always send it to the AMM or even JAMS later.
Actually, however, the fact that you are in a position to even ask this question suggests that your acceptance into a good graduate program is pretty much assured. A lot of us got there from more humble beginnings.

Caveat: The EU situation is different, as the European educational system is quite different at the BA/BS level.

What is "JAMA"?

@ElizabethHenning, Actually, JAMS, Journal of the American Mathematical Society.

@Buffy forgive me if you know more about maths journals than I do but the gulf between AMM and JAMS seems vast to me. JAMS aspires to be just below Annals in terms of prestige and selectivity, I think, and full professors would be happy/proud just to get some of their research output into it. So I am not sure about your calibration here

@YemonChoi, indeed. I picked it only because it is another general, rather than specialized, journal. And also has wide readership.

@Buffy Why do you think it has wide readership? Have you looked at what's published in it? It is a general journal, yes, but it is one which is meant for groundbreaking research that will interest various specialists around the world. I don't think it is sensible to be encouraging the OP to even think about JAMS, especially when they describe their results as "approachable enough to be understood by most graduate students. "


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Is there a specific field which addresses how to develop new software specifically for natural sciences?
I am a physicist currently doing a master's degree and I have a background in programming with a knowledge of computer science — not on the level of someone who studied computer science but I would still say a bit above average.
As I program a lot for physics and astronomy applications, I have become increasingly interested in how to further expand software and scientific software to be more usable for natural sciences and physics in particular. Now I would like to know whether there is a specific sub-field that addresses this.
Examples of what I am interested in include:

how to include units in a standardized way in file formats specifically made for storing scientific data (e.g., HDF5)
whether it would make sense to create a programming language with the specific intent of making scientific calculations easier (including units and measurement errors standardly)
how to create better plotting solutions that can be edited after they have been created. (Similar to Matlab .fig files but not proprietary)

It would be super helpful if anyone knows something here.

This feels more like an entrepreneurial pursuit than an academic field to me.

In what way would you consider this to be entrepreneurial? My interest lies in the applications to science with free and open standards aswell as software. My intent is not to make money but in advancing science by providing newer and better applications and standards.

You might find the Programming Language Implementation and Design Stack Exchange site useful. (It doesn't exist yet, but it probably will in a few months.)

I'm not sure about standardizing, but the type of folks who work on research software are, appropriately, called "Research Software Engineers". See here: https://us-rse.org/

@wizzwizz4 this is very cool I will watch the SEs carrer with great interest :D

@do-the-thing-please that is exactly what I was looking a name for this "field", research software engineering. Thank you so much.  And i just found that my country (Germany) has its own RSE society.

Maybe ask in the Computational Science stack.

You might like to look here ( https://www.math.colostate.edu/~bangerth/ ). The webpage relates to a person who is a very well regarded contributor to this forum.

Rather than consider creating a new programming language to better handle units, you would almost certainly get better buy-in and adoption by developing packages for a language that people in a given field are already using (and hence invested in and reluctant to migrate from). eg see this talk by Clinton Roy on better handling of physical units in Python: https://www.youtube.com/watch?v=aw7ZADfwOOM

@ScottSeidman Inventing or improving software tools for specific purposes can be research topics in computer science. For example, there's a tool for detecting excel errors.

One problem in the UK is that universities are neither inclined nor able to pay salaries to software developers that are comparable to salaries in the industry. Another problem: Computer science doesn't necessarily teach you programming.

There is a lot of niche scientific software out there that's some PhD student's pet project from several years ago. Generally they are held together with just enough sticky tape and chewing gum for said student to get enough data to pass their doctorate, so expect some instability and incomplete functionality. Thankfully with the rise of open source research group software repositories on sites like GitHub and GitLab, a lot more of this software can be improved and made more robust by future researchers in the field.

@CrimsonDark he seems realy cool, and he studied at my alma mater (Heidelberg). Will check this out definetely, thank you.

@MichaelMacAskill, very cool talk i will definetely watch it.  I am currently a big fan of Unitful.jl because of the way how julia packages like to interopt. To be fair in this regards I always feel like there is dozens of solutions (Packages) but are rarely used because they are obscure or not well known. If it was built in you could just use it without having to find or create any package that might or might not have all the functions necessary.

@Crazymoomin, this is exactly what i haveseen so far. Alot of very wonky software done by masters or phd students with sometimes questionable documentation. Usualy the realy good software is maintained by a Non Profit for example Numpy, which does gods work in python.

Closest to your second point is the Julia language and its package ecosystem, where the Measurements and Unitful packages let you write code with units and error propagation very easily.

@Dronir yeah I just recently found julia and for what i have tried so far i was having a blast. I might write more code in julia in the future.

I think what you are looking for is "Research Software Engineering" (RSE). Currently this is more of a job title than a discipline, but RSEs are starting to organise more and more like a discipline: they have their own conferences, learned societies and journals, etc.
Its a concept that started in the UK within the last decade, but is spreading internationally now. See https://society-rse.org/
For institutions that buy into the model properly, there is often a separate RSE team or lab. In my university this sits within the CS department, but most of those in the RSE team started as domain experts in something else, like Physics, Chemistry or Biology and had been involved in code based research in those disciplines.

In my experience, attempts to standardize scientific software fall into this problem:
https://xkcd.com/927/
When you're doing research, it's really difficult to have a set of standards that actually apply to everyone, because everyone is asking a slightly different set of questions, and those questions may not be compatible with previous solutions in how to organize a problem or organize data.
The easiest places to address are where specific equipment used in a field dictates a particular format. If everyone is using the same machine that produces output in a ".sci file", there will be a need for software that operates on ".sci files". The same happens if there's a particular open source software that everyone uses. I doubt, though, that you'll find anything standard to "physics" or even "astronomy"; rather, you'll find standard usage within a specific subfield of physics or astronomy, where people are asking and answering related questions and sharing data amongst others working on the same problems.
Besides that, there's always going to be a trade-off between ease of use and flexibility of use.

Ah i love xkcd. Thanks for reminding me of this. Sometimes I realy like to overstandardize, but i think there were alot of attempts of creating standards that were actualy sucessfull and decluttered everything, take USB for example.

@TheMastermage Ah yes, now that we have USB, all of our small connectors connect to each other without need for specific adaptors for each and every one of them. (I joke, a bit, yes USB standardization has been helpful, but also not a panacea)

i mean of course not but it has been greatly reducing the amount of weird propriatary connectors. And while i know there will never be just one standard thats not only utopian but unrealstic considering that different applications have different needs. But I do personally think that open standards with a wide adoption can have numerous benefits.

@BryanKrause. I miss the days of stacking 9 to 25s and 25 to 9s, with various male/female arrangements and some both wothcand without null modems, maybe withca security dongle or three thrown into the mix, and diddling around until you made ccx a connection.

Speaking of USB, despite the physical USB-C connector being standardized, we’re now seeing a  plethora of different standards and capabilities, so even if you can physically connect something you don’t necessarily know that what you want to do will be supported …. various things like data transfer rate and maximum power draw, among others

@ScottSeidman u wot m8?

@AzorAhai-him-  not sure what you're asking.

@ScottSeidman It was a joke. Without field specific knowledge your comment is completely impenetrable.

@AzorAhai-him- ash, I completely forgot I had commented earlier.  Trust me, it's no field specific, it's age.  Many people who have had to plug any serial device into any computer before USB without the OEM cabling will understand.  Serial port connectors were generally DB9 or DB25, of either sex.  For most, but not all, devices, pins 2 and 3 needed to be swapped, but sometimes the device did that for you, and it was nigh impossible to tell if that swap was done in a cable or adapter just by looking at it.

The end result was that you took whatever cable you found, and then you would go into your drawer of unlabeled adapters, and stack them in different ways until a) the sexes and pins counts enabled a physical connection, and the net swap of pin 2 and 3 was such that the serial bus worked.  Once it worked, you would avoid touching it ever again.  It could take 20 minutes or more to get it right.

@ScottSeidman Well, I got as far as "will undesrtand."

@user253751, while i agree that in the begining this worked great because of what you said, I dont see the reason why this complicates things in the current world, I see it more like because of technological constraints we couldnt use USB for Video (for example) and now that it has advanced we can use it for more, and more complicated things. Of course that complicates things but everything gets more complicated with time and technological advancement.  I personally dont see this as a problem. Now what the USB IF does to complicate things thats where i will agree with you thats just a pain.

You are, in fact, asking about applications of certain fields, including CS, to a certain class of problems.
It is natural, in applied fields, to bring a variety of tools to bear depending on the needs of the specific problem. Those tools may have (probably have) a theoretical basis, but the problem at hand may be very messy.
So, the fact that you may need programming in some general or specialized language, statistics, graphics, human factors, etc. etc. etc. isn't at all surprising. Teams of people with different backgrounds and skills is often a productive way to proceed. CERN, for example, has lots of specialists in lots of specialities and their expertise is brought to bear, or not, according to the needs of the day.
If a class of problems (applications) is both important and recurrent, then specialized tools might arise in solving one that might be used in other, similar, problems, but the world as we know it is still messy and requires some ingenuity to deal with.

So basically the Discipline i would be thinking about is Computer Science or maybe a sub-field thereof?

More like "applied CS" which depends on the normal tools of CS to solve real world problems. It isn't really anything special beyond that.

I have not looked into the correct direction here, as i was just looking at Physics and subfields like Comp Physikcs. My main goal was just to find a way to describe what i am interested in and to find if this was an existing field. Thanks for your answer.

@TheMastermage More like a sub-sub-subfield of applied CS. There's been plenty of attempts to e.g. incorporate robust unit/dimension handling into programming frameworks (Boost comes to mind) but this is very much a practical concern rather than an academic discipline. Oh, and I'd suggest that "inventing a language" is something completely outside your scope, at least until you have completed a comprehensive review of the many thousand that exist already... with perhaps the most recent notable attempt being Rust.

I wouldnt have tried creating a language myself, just had some ideas about it. I am not going to write a language, especially not alone, that wouldnt be feasible. I agree that this is more of a practical concern it was meant to be more of an illustration of what these topics that i am interested in are. I have looked at Rust which seems very cool, currently also looking at Julia to replace some of my python. Also last I checked F# actualy supports units in the base distribution.  Havent seen any that include error handling.

@TheMastermage well, it was your OP that mentioned creating a language :-) I think possibly I might pick up on that as an answer, since I think there is not enough space in this margin :-)

Upgrading from my comment, a don't think you'll find a scientific or academic discipline that aligns perfectly with your goals.
You seem a little bothered by my description of your description as more "entrepreneurial".  Don't be.  It's not meant as an insult.  There's nothing wrong with an entrepreneurial pursuit.  Plenty of great things happen because of clever entrepreneurial people.  You may not be planning on making money, but I suggest even the person with the purest motivations in the world needs to eat and keep the lights on, and if one can't find a way to do that while making the world a better place,  then one needs to keep thinking.
The reason why I call what you want to do as entrepreneurial is that you seem interested in provided a service or tool to people in scientific or academic pursuits.
That said, there are some academics (I think) that do some work in the visual representation of data, like Tufte (https://www.edwardtufte.com/tufte/books_vdqi; https://en.wikipedia.org/wiki/Edward_Tufte)  Note though, that I don't believe Tufte  was into making his tools available to academics.  There are, however, many companies that have incorporated many of Tufte's ideas and recommendations into their products (SigmaPlot, Prism, ...)  They do this to make their business models work.
Now, think about how tools get to investigators.  I believe the most frequent models is that somebody does work, becomes expert in it, and then build the perfect tool for them and their group.  They find a way to make it available  (think ImageJ, or just about every R module).  If it's a good enough tool, with enough of a user base, maybe a company will pick it up, and package and sell it.  The people that develop the tools from the ground up usually aren't in the business of tweaking the tools.  In fact, no academician would want to be saddled with packaging, distributing, and (perhaps hardest), supporting the application.  That requires a business, with a staff, a budget, the logistics, ...  -- repeat business.
I'm not even beginning to imply that you can't have an absolutely marvelous and rewarding career doing what you describe.  I just think you'll have the highest likelihood of making it work as a business model.  I'd encourage you, if you want to pursue this area, to actually build a business model, figure out who your customers would be, the size of the staff you would need, how many instances you would need to sell, what you would need to charge for them, how much seed money (diluting and nondiluting) you would need to set up this business, where are you most likely to find this money, how long before your company would have a positive cash flow, etc.
Alternatively, you can find a company already in the area, or close to the area, and see if you can find employment in there.  Convince that company that with the right sort of hire, they can maybe expand their customer base with little risk if they expand their product line, and then do your magic on their dime.

So far my plan has not been on creating my own company while I highly respect those who do become self employed it is not the way for me. I will take your comment to heart tho especially your last paragraph.  :D

The Mastermage: Regarding "entrepreneurial" -- Given the extremely high visibility of Stephen Wolfram's Mathematica in physics, I would imagine you're aware that his original motivation sounds very much like yours: "I started out—in my teens—as a physicist. And doing theoretical physics requires doing lots of mathematical calculation. And I figured: these calculations are kind of mechanical. (continued)

Why can’t I just get a machine to do them?" [...] "Well, that was in the mid 1970s. And at the time there were a number of research systems for doing algebra and things by computer. And I started using them—and managed to do some pretty interesting physics with them. But it didn’t take long for me outgrow the systems that existed. So late in 1979 I decided that the only way I was going to have the kind of system I wanted was to build it myself. So I did."

@TheMastermage I agree this sounds like a business venture to me, because I think you dramatically underestimate how large a team of programmers would be required to create a whole, new, useful, supported and adopted scientific programming language. If you really think there are specific things missing, create an R package. If you really think standardisation is missing, make sure to republish your package for R, Python, Julia, SAS, MATLAB, STATA, SPSS, Maple, Mathematica, etc. Then at least most people can use your new 'standard'.

@niemiro or publish it in C++ with a C interface, because most (all?) of these languages can link to C with relatively little effort. The other way around, try using a piece of code written in, say, Matlab from another language, and you'll be in for a much more painful experience.

@niemiro yes of course, i might have not cleared that up entierely but I do not plan on doing any of these things completely alone if i would attempt them. I just wanted to figure out what the "discipline" is so i could connect with like minded individuals and maybe start discussing if such things have merit. But other than that i do plan on packaging certain smaller things myself, so other people can use it.

@leftaroundabout that is what i was planning for some things, altough C++ might sometimes be abit painful due to its low level capabilities, it makes it all the more usefull when distributing software.

@TheMastermage modern C++ can be pretty high-level though, which is not to say that it doesn't have aspects that are, to say the least, clunky compared to science-oriented languages. There are actually a bunch of other compiled languages that can also be hidden behind a C interface quite well: Rust perhaps most notably, Fortran for historical reasons (some people would also claim that it is still the best for this role, which I disagree with), and even languages that may be surprising such as Haskell (my personal favourite language), though with some caveats.

@leftaroundabout you can compile rust and haskell to c code? I did not know that thats interesting do you maybe have a link for me?

@TheMastermage not quite, but you can compile them to object files that can be linked to an executable as if (-kinda) they were written in C. Simple example in Haskell: https://wiki.haskell.org/Calling_Haskell_from_C (This one uses GHC for compiling also the C main module; that makes it easier but is not strictly necessary.)

Data visualization is a subfield of computer graphics, and is distinct enough that it has its own major conferences, and you can get a CS PhD through research specifically dedicated to visualization.  I don't think this gets OP exactly where they want to be, but it's a place to start looking.  Data visualization tends to be multidisciplinary (data does not exist in a vacuum) which I think might be a plus for the OP.

@leftaroundabout, this seems similar to ctypes in python or rather cython.

@TheMastermage no: with Python you need to either carry around an interpreter with you (in which case it's not much better than having a seperate CPython process that communicates with the C program only via pipes), or you need to stick to a very limited subset of the language that Cython can actually compile to efficient C code first before that is compiled to object files (in which case IMO it makes more sense to just write it in C++ right away). — I won't detail further on how this differs in Rust/Haskell etc., because we've already gone quite off-topic here.

@leftaroundabout yes, we have gone quite off topic but I still appreciate the time and effort you have put in to try and explain a probably pretty complicated concept in a short 600 Character comment. ^^

Is there a field which specifically addresses how to develop new software specificaly for natural sciences?

... further expand software and scientific software to be better usable for natural sciences and physics in particular

What I understand you're alluding to is #reproducibility, alongside #reliability and #validity
As a role, these are scattered across: computational biologist, computational physicist, computational scientist, lab scientist, research scientist, research engineer ... even software scientist.
Of late, they are 'morphing/converging' into research software engineer/scientist with a path to research technical manager.
Consequently, this also brings in 'new field/discipline' loosely referred to as Research Software Engineering.
[For me though, it might be inconsequential if it's research software engineer or research software scientist].
RSE as a discipline is getting well entrenched in the UK, in the States and the rest of the world catching up fast. Invariably, it's gaining ground globally.
In that regard, RSE are known as people with a specific set of skills that combine the research knowledge and software engineering. A fear I have is RSE becoming 'just' another SE in a 'vertical industry' (which just happened to be Research or R&D) and hence loosing out the research software scientist or the science/research of the research software.
A paper on RSE -
Woolston, C. (2022). Why science needs more research software engineers. Nature.
Alliance/funding org for RSE - ReSA
Bodies for RSE:
Aligning together internationally International Council of RSE Associations /  Research Software Engineers International

UK/'global': Soc-RSE - Society of Research Software Engineering - UK
US-RSE - The US Research Software Engineer Association
BE-RSE - Belgium Research Software Engineers community
DE-RSE - Society for Research Software in Germany
NL-RSE - The community of Research Software Engineers in the Netherlands
NORDIC-RSE - Nordic Research Software Engineers Community
RSE-AUNZ - The RSE Association of Australia and New Zealand
Danish RSE - Danish Research Software Engineers Community

In the UK for instance, apart from university research centres, RSE abound in places like STFC, DiRAC ...
"Software is the ubiquitous instrument of science. - Carole Goble, Professor of Computer Science, University of Manchester, UK"

TL;DR:
The field involved in development of software for the purpose of research is called Research Software Engineering (RSE). In the USA one resource for more information is https://us-rse.org/.
Details
I can't speak to standardizing research software. I'd caution that any attempt to standardize research software is fraught. After all, if we knew exactly what we were doing, it wouldn't be called research.
That said, there are definitely standards to developing research software, at multiple levels. For context, I have worked as an RSE and currently work as a research computing facilitator. I have built or facilitated a wide variety of research software solutions. The list that follows comes from my personal experience.

Numerical computing. The field of mathematics involved with understanding the behavior of approximate mathematical calculations in the context of finite representations of real numbers, i.e. floating point numbers. Practically, this informs us how to develop mathematical algorithms that will converge to an error tolerance as quickly as possible, rather than compounding floating point errors.

Software engineering best practices. Things like version control, unit and integration testing, issue tracking, developer and user documentation, minimize complexity, maximize readability, appropriate abstraction, assert pre/postconditions and invariants, code reviews, continuing education, etc.

Verifiability. Does the code produce outputs that are expected? This is distinct from unit and integration testing. The goal of testing is to check parts of the code do what they are intended to. The goal of verification is to check that the software output matches what the science says the output should be. This can be deceptively hard!
Each scientific domain may have a different approach to verification. Some may have multiple approaches, which can be a research subfield in its own right. Some may have not developed an effective means of verification.
Some may use rigid comparison of results to know values. Some may use stochastic comparisons, i.e., checking a distribution of outputs matches a known distribution. Sometimes a sensitivity study is suitable, i.e., varying inputs in a systematic way to observe the behavior of outputs. Very much like a Design of Experiments. Some may have to compare to proxy models.

Reproducibility. How easy is it for someone else to set up and reproduce your results using your code? There are myriad tools for this at different levels. RNG seeding, Anaconda environments, containers (Docker and Singularity), workflow managers (Snakemake, Nextflow, Pegasus, etc.), version control.

All of these are things Research Software Engineers should be aware of. Many of them will touch all of these at some point in their career, if for no other reason than they solve a lot of problems and make software engineering and deployment work easier.
If this sounds like the type of thing you are after, then you are absolutely looking for Research Software Engineering.
Physical Units Management
There may be various packages out there that manage physical units, and there may be some proprietary libraries used by groups in mission critical domains (Aerospace, Medical) to avoid costly (or deadly) errors.
For high-throughput applications, automatic unit management at runtime may be an unnecessary burden. For statically-typed languages, the types could be used to check unit errors at compile time, which could be fantastically helpful for catching errors early. However, you'd need to design a lot of classes (or use something like template metaprogramming) to allow changing types through multiplication. This is definitely possible in, for example, C++, but the up-front design and maintenance costs may be substantial, and it adds burden to developers.
Oftentimes in practice it's easier to use a modified Hungarian notation like the following block.
width_mm = 3
height_mm = 2
area_mm2 = width_mm * height_mm

mm_to_m = 10^-3
area_m2 = area_mm2 * mm_to_m^2

length_cm = 100
volume_mm3 = area_mm2 * length_cm

It should be immediately obvious from the variable names which lines are correct and which have errors. I'll leave it to you to figure out how to denote negative powers of units.
Bonus Information!
If there is any chance your research software will need to scale up, be sure to consult with your local Research Computing (RC) or High Performance Computing (HPC) staff before and during development. Most academic institutions have some amount of RC/HPC staff. They can help you design your work to ensure less friction when scaling up.
RSEs often work closely with RC/HPC folks for exactly this reason.
It may be helpful to read this recent blog post on what research software is: https://upstream.force11.org/defining-the-roles-of-research-software/. It has a categorization of research software into seven different types. The blog post goes into greater detail on each.

Component of research instruments
A complete research instrument
Data analysis pipeline
Data presentation or visualization tool
Integrates components into a whole (glue code, workflow management)
Is infrastructure, a platform, a framework, a library, a language, etc.
Facilitates research-flavored collaboration

Summary
It is clear that there are many different types of research software, fulfilling many different roles and functions. This huge variety makes it hard to come up with a good classification that captures all aspects and does justice to all the hard work done by the developers of the software. Nevertheless, we hope that we have succeeded in proving a bit more insight into the value of research software, the importance of sustaining said software, and recognizing the people involved in developing the software.

Thank you for your input on this especially the nice list up of the different things to consider here. I do not plan on standardizing software as a whole but I am interested in Standards that facilitate an easier exchange of scientific data e.g. unit support.

Thanks for the kind words. I'm not familiar enough with scientific programming of units to speak to it in great detail. I'll add a section with my thoughts at a high level.

@TheMastermage You may find this blog post illuminating as well about what Research Software is, at a high level. I'll add it in the bonus section to keep it around long term: https://upstream.force11.org/defining-the-roles-of-research-software/

The closest thing I can think of as a discipline or field would be computational biology, but it's still not really getting at what you're thinking about. I actually spend a lot of time thinking about the same things as you: making better tools for scientists. And Scott Seidman's answer really gets at what that looks like in many cases. Scientists have a project idea, and a tool comes of it as a side-effect, and sometimes they try to share it with others. I've used a lot of these tools, and often, they have a lot of room for improvement. In my field (ecology), there are definitely job opportunities for developing and/or improving research software at all levels (non-profits, business entities, academic research groups, and local/state/federal government agencies). This is what I do right now; I volunteered to develop and publish a software package some years ago as a side project during my PhD, and now I'm employed part-time by an academic group to continue developing and improving it, as well as have a contract with a government agency to add some novel features to it. The funding from the academic group came in part from a research grant that specifically supports developing research software, and now we are working on getting an NSF grant that would support me full-time for a few years to keep working on the same software. There are tons of research grants available for this type of work; it's just a matter of finding the right research group to work with.
In the meantime, I have some software side projects that I work on in my free time that are aimed at addressing other issues I see that aren't really specific to a discipline. Some are small and aren't really big enough to warrant funding; they are more just fun toy projects that will help to build out my portfolio, and maybe a few people will find them useful. But I have a big one that's kind of generic and might be a little harder to find a research grant and group to focus on (if I wanted to go that route). Instead, I'm hoping to turn it into an entrepreneurial opportunity down the line when it is more stable.
I've also considered applying for jobs at businesses that do this type of work. A good example is Posit (formerly RStudio). They don't just develop an IDE, they develop a ton of R packages and other tools with a focus on improving scientific computing. Then there are other businesses that develop software with more specific scientific applications in mind. The only reason I haven't gone this route (yet) is more related to why I got into programming in the first place (long before I did any kind of scientific computing) and my desire to develop my own ideas.
But I do have one piece of advice: be careful about thinking you have "better" solutions (like file formats, plotting, etc). First, we all live in a bubble and often don't see many things that already exist, so there's a good chance someone has already had the same idea and implemented it somewhere, and you just haven't found it yet. Second, it's really hard to convince people to try new things; it can take a lot of time and effort for people to learn new tools and modify their workflow around them. Even if the long-term payoffs are worth it, budget limits, other deadlines, and just plain other things they'd rather do get in the way of your new software gaining traction.
I say this as someone that has had tons of ideas related to programming language design (I have yet to encounter a scripting language that I like using for scientific computing), file formats, data management, plotting tools, etc. I toy around with these ideas with the hope that maybe some of them are novel (or at least a better implementation) and people will use them someday, but I basically assume that they don't have value until they exist and are widely used. In the meantime, as I said, I depend on working with research groups to develop more specific tools to keep me funded.

Thank you for your deep industry/science insight here, gives me quite an interesting perspective. I will take your advise to heart, I dont wish nor think i am the one who has (as we in germany call it) eaten wisdom with a spoon, so all of these things i mentioned are more like ideas, that i think could have merit. But I am open to discussion and in fact that is the reason why asked for a field here, to find the people with whomst I can discuss this.

I also dont have any illusions onto the fact that people, me included, are very inert when it comes to changing to new software. The reason I stayed with python and matplotlib the past couple years even tho I dislike them in certain areas. I also sometimes would rather like to expand on existing things with my ideas if possible. So If  I will write some side projects then in the knowledge that it might never gain traction but atleast it will maybe satisfy my needs. And maybe someone else will find them usefull, which I realize is exactly how projects go as you had mentioned in the begining.

There is the Scientific Python community:

The Scientific Python ecosystem is a loose federation of community developed and owned Python projects widely used in scientific research, technical computing, and data science. This website is part of the Scientific Python project, which aims to better coordinate the ecosystem and grow the community.

At least for the examples you provide, there are existing and actively developed software solutions. A very incomplete list:

xarray "[...] makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun!" It is used widely in climate science, and comes with Dask support out of the box.
Pint and Astropy provide physical units. Integration of Pint with Xarray is actively developed further.
NetCDF and NeXus specify how to store units in files. Both are formats built on top of HDF5, as per your question.
Scipp supports multi-dimensional data arrays with labeled dimensions in a manner similar to Xarray. It is a Python library enabling a modern and intuitive way of working with scientific data in Jupyter notebooks. It supports units out of the box.

Disclaimer: I am the lead developer of Scipp.

Scipp looks good. Well done!

as a heavy user of the whole SciPy stack, I am very grateful to them and they are doing an incredible work to the scientific comunity.
May i specifically ask what Scip does differet than xarray? They seem very similar?

@TheMastermage Scipp and Xarray are indeed similar. Scipp's direct support for units provides a considerably better user experience. It is fast and multi-threaded out-of-the-box, whereas Xarray users have to rely on dask, which is more of an expert tool but allows for scaling beyond a single machine. Finally, Scipp provides algorithms and data structures for non-destructive binning and histogramming, for irregular data such as event data. Xarray is more mature and feature rich in other areas, but also more complex, in part due to its Pandas-legacy.

See also What is Scipp and Comparison with other Software.

@Simon, this sounds realy cool thanks for the explanation. I will probably be using Scipp in the future.

@TheMastermage since I saw you're thinking about standards and you're a Pythonista, you might also be interested in pyOpenSci: https://www.pyopensci.org/. I think other answers are on the mark that "RSE" is the general term you were looking for in your original post :)

Upgrading one of my comments into an answer, in two parts:
First, several other answers mention visualization as a sub-sub-sub-field of computer science.  That's correct:  Data visualization is a subset of computer graphics, which is a subset of computer science in general.  However, even sub-sub-sub-fields of computer science can (and do) support vibrant research communities.  One can obtain a PhD with a focus exclusively on data visualization, and there are several respectable conferences in the field every year.
That said, I don't know that Data Visualization gets you fully where you want to be.   It's certainly an area to investigate, though.  And DV tends to be interdisciplinary (data does not exist in a vacuum, and it helps to understand the challenges of another research field before you go charging in trying to solve their visualization problems for them) which may be a definite plus for you.
Second, the notion of trying to include units (I think you mean physical units) into software brings to mind the last well-publicized push in that direction, at least the last one I recall: The Fortress Programming Language.  It was a major effort which, alas, did not pan out.  It was shut down about ten years ago.  Surely these issues are faced all across many industries, as well-- mechanical simulators, electromagnetic simulators, astronomical software, particle physics software, etc.  Unfortunately, I do not know anything at all about how they handle it.
But there are areas of computer science dedicated to programming language design and compiler design.  And that's just a sub-field, not a sub-sub-sub-field.  These will also be related to "scientific computing" at large.

Thanks for the expansion on this, I havent heard about Fortress so far. As with all new programming languages it always difficult to get people to adopt it especially when it is not used much yet which sadly leads to the decline of some interesting ideas.

@TheMastermage well, it's even harder to get people to adopt a language which could not be made to work....

Apropos "creating a language", there are two aspects here:

Representational languages
Computational languages

The dominant trend these days is for newly coined representational languages to be based on XML, often zipped, the practicalities of which are thoroughly "applied CS". The details of this- the schema etc.- are highly application-specific, and are likely to be very different for e.g. 3D particle tracks captured at CERN vs statistical analysis of broadband noise on a communication channel.
There are of course exceptions, and I'd highlight the work that Lenat has done representing ontologies etc.: I'd not like to attempt to comment on whether that could have been done using e.g. PROLOG.
As far as computation goes, there is a regrettable tendency these days to assume that Python is adequate for all possible cases (obligatory xkcd: https://xkcd.com/413/), even if when one scratches the surface one finds that all it's doing is pulling in some decades-old FORTRAN.
However there are a couple of cases which might possibly justify either a custom language or compiled-in extensions to an existing one:

Where it is desirable to specify attributes such as units at the language level rather than using an existing type system or as strings to be parsed at runtime.

Where practitioners are so used to their custom representation that they are extremely resistant to any language which doesn't support it directly.

As examples here I would offer the PostGIS extensions to the PostgreSQL database, plus of course the continued use of PROLOG in certain fields of knowledge manipulation. However my opinion is that both of these benefit from being able to be embedded in or intermingled with a conventional procedural high-level language such as C++.
So I think that might possibly suggest some areas for research, but the fundamental issues are the structure of the data being manipulated and the expectations of existing workers.

"As far as computation goes, there is a regrettable tendency these days to assume that Python is adequate for all possible cases". Sure, Python is not always the best choice. But it seems to have achieved a critical mass : many people in general, and scientists in particular, are using it. Which means that if you google "python + something_you_want_to_calculate_or_display", you'll find a nice, recent library allowing you to do it. And with two or three imports, you can achieve awesome results, which you would have to write from scratch in another language otherwise.

Very interesting look at this, and I do agree on the Python part.

Do not invent a new language
The idea that a different language will save you is a popular misconception from CS. Sure, you can add features to a language. But it doesn't buy you anything over adding a library to do those things.
And by the very definition of creating a new language yourself, you've limited the number of people who will ever use it to one. Yourself. Software engineers have had 60 years to write languages, and you're highly unlikely to find a new niche and get critical mass of new users. The surest way to guarantee no-one else will ever use your work is to invent a new language for it.
What's wrong with making these features a library?
You're describing having units and tolerances/uncertainties attached to a value. This is literally a beginner's C++ exercise for writing your first class. If you're not aware of OO (object orientation) principles, I strongly recommend reading up on this. It's key to how pretty much all modern languages work.
What's wrong with existing languages?
Industry overwhelmingly uses C++ or C# for historical reasons, and because they run fast. Rust is a new entrant in the same area, and it may pick up enough to get popular since it was adopted by the Linux community, but it's still well behind. For server-side work, you'll meet JavaScript. In the maths/science community there's a strong user base for MATLAB, again for historical reasons (and because it does what it does very well). You may also meet R and Julia in academic maths/science stuff, or Fortran for older stuff, although they're less common. And Python is popular everywhere for writing programs quickly, but it runs slowly (because it's an interpreted language).
When you know enough about those languages to know features that are missing from all those languages,and you know they can't be added as libraries, and the language maintainers reject your request for those features, then you may have a case for writing your own language. I'm going to say that's pretty damn unlikely though.

My main idea with this is, there of course are unit libraries for most of any Language available but this adds extra dependencies, and why add extra dependencies when you could have this shipped by default. I am open to discuss the merits of this.
I dont quite agree with your reasoning, I think new languages can be developed even if they dont reinvent the wheel. Just because there is 60 Years of development doesnt mean there can't be new programming languages. Look at Carbon for example, everything could also be written in C++ but Carbon builds up on the positives and adds modern features.

"The idea that a different language will save you is a popular misconception from CS..." While this is a pernicious trap, an even worse one is investing time and effort into an improved compiler for an existing language because some feature of the syntax makes it difficult to parse. I submit that if a computational language is difficult to parse it is better to fix the language than to try to write an improved compiler, and by extension would suggest that if a representational language is difficult to process one should look for flaws in the schema describing the data layout,

@TheMastermage What's your problem with dependencies? Unless you're writing embedded software running on raw metal with no OS or anything else, you've got dependencies up to your eyeballs from the moment you write Hello World. Why could one more dependency possibly matter? Especially since it's something you've written yourself. Or hey, you've got the source code, so you don't even need to make it into a library if you don't want to. You're trying to solve a non-problem.


text:
Current advisor wants me to graduate with my PhD soon but I don't feel like I learned a lot or obtained enough skills. Should I discuss this with him?
I'm a soon to be 4.5th year Ph.D student currently in a 10 week internship. My advisor wants me to wrap up my dissertation as soon as I can. However, I'm not feeling good about where I'm at right now as far as outcomes go. I'll use myself as an example to illustrate what I've managed to get at the graduate level:

Three conference posters (2019, 2023, 2024)

Instructor of record for two online classes where I did my Ph.D, one semester of adjunct experience at a community college, and a full year of teaching as a full time instructor at a small liberal arts college

Fellowship sponsored by the state where I'm doing my Ph.D for diverse applicants with future faculty and/or staff potential

10 week internship at one of the top 10 children's hospitals in the country that's only 20 minutes away from my hometown (and am still struggling to adjust to the responsibilities. My boss wants to discuss authorship with me and says he's happy with my updates, but I'm far behind compared to the other interns for sure).

My rocky experience (e.g., Master's program advisor ghosting me and prior advisees, first PhD advisor dropping me since she thought I wouldn't make it through the program) has certainly illustrated the cracks in my training and severe lack of experience in domains where I'd be expected to adapt quickly otherwise (e.g., learning R Studio). It's to the point where, should my boss at this internship secure the funding to hire two of the interns close to graduating (me being one of those two), I'm not sure if I'm in a position to take it at all. I'm also fumbling a fair amount in SPSS and my advisor has corrected some careless mistakes I've made with the variables.
I know I can't quit because my advisor will never write a letter of recommendation for anything again if I do. I'm considering taking a break from working as I simultaneously work with vocational rehabilitation in my home state (I'm neurodivergent and autistic so I qualify) to finish my dissertation and graduate since I'm convinced I'm dealing with burnout from all of the responsiveness I've juggled in the past two years as well as still recovering from the abuse I experienced from my first PhD advisor.
Even though my advisor wants me to graduate soon, I'm not confident in my skills or what I've learned to feel like I should be in a position to graduate. Should I bring up my concerns to him? Would it also be worth broaching ways I can learn other relevant skills?

What are your plans for after you graduate (whenever that may be)?

Should I discuss this with him?’ From your perspective, what are some reasons you think this might be potentially worthwhile to do?

When you say you're considering taking a break from working, do you mean taking a break from non PhD work to focus on finishing your dissertation, or taking a break from PhD work and coming back to it?

"I know individualized advice isn't something done on here but I'll use myself as an example to illustrate what I've managed to get at the graduate level:" And you're using yourself as an example of what exactly? It looks like you are using yourself as an example of yourself, which does not generalise the situation. There probably is a general situation in there – PhD approaching end of their studies, lots of pressure and self-doubt - but you would have to [edit] the question to flesh that out, probably by dropping most of the details about yourself.

I can't think of any reason not to discuss your concerns with your advisor. It sounds like you need reassurance more than anything. Certainly a break at this point seems counterproductive if your advisor thinks you are on the verge of finishing.
You probably have access to some professional help with things like burnout. You seem to have been pretty active. But you might also explore imposter syndrome.
And note, importantly, that your education doesn't end with graduation. If you have a position in academia, learning is a lifelong profession. Don't imagine that you need to know everything to graduate and move on in your career.

To add on to the other answers, I will just offer my perspective on when a student should wrap up their PhD.
In an ideal world, I would like to think that the student has produced a beautiful hypothesis-driven work with all the main parts wrapped up neatly, and some exciting results left over for future graduate students to build on.
In reality, that isn't always the case. Sometimes it becomes clear that the student is not really gaining anything new and is not improving further. This can be due to a lot of things including burn out which is very common (it's a long haul from undergraduate to end of PhD). So at that point, what is the benefit of more of the same? Especially if the PI believes that the student has met the threshold for obtaining a PhD in that program. At that point it is more beneficial to wrap up, take a break, and start fresh somewhere else (whether it is a new job, a postdoc, etc...).

Yes, definitely talk to your advisor about this! If you're feeling too burned out to wrap up your dissertation, tell them this. Maybe you do need a break.
What if you take two weeks away from your PhD work completely, and schedule a meeting with your supervisor for your first day back to come up with a concrete roadmap of what needs doing. You might find it helpful to get a very explicit idea of what your supervisor thinks needs to be done for you to graduate.
As you mention being autistic, you might also contact your institutions disabled student support service and see if they have anything like a "specialist mentor" who can talk through options with you.
If you're struggling with your current responsibilities, can you drop anything? I was very overwhelmed recently and quit every extracurricular very abruptly - until the point that I dropped everything I felt like I wasn't allowed to drop anything. In retrospect, I could absolutely have dropped some of it sooner and not burned out so spectacularly. If you're at the point where you're considering quitting, I suggest dropping everything that isn't absolutely necessary to graduate. What would it take to drop the internship? Obviously it wouldn't be ideal, but it would definitely be better to talk to them now and quit in a controlled manner than flame out suddenly.

Aside from university timelines/deadlines, when and how you finish is mostly up to you.  This assumes you work with your advisor on a schedule for closing out your research & defense, publications, presentations, etc.
I wouldn't worry about an advisor ghosting you since the landscape on student mentoring has changed over the last several decades.  When I was a center director in a large university hospital, I couldn't count the number of students who approached me to serve as their advisor because "their advisor ignores them, and won't return emails or calls".  Such ghosting was a result of (a) new/young faculty who were unaware of the responsibilities of being an advisor and (b) the dept chair not really knowing the young faculty were terrible advisors.  The cultural background of young faculty has a lot to do with it.
Your comfort level for knowledge gained and your skillset for competing in industry is more a reflection of your required/elective coursework and workshops, and not so much your program.  Regarding software issues, you might consider attending e.g. summer workshops to get more exposure/experience. To eliminate occurrences where people find coding errors, just start checking your work, since it sounds like you turn things in before you review/confirm they are correct.
I would recommend prioritizing graduation, and curtailing participation in external-outside superfluous, unnecessary activities, which are diluting your focus for now. Spend what remaining time you have in the program on finishing your research and strengthening your skillset for industry.  If you work with your advisor on a plan to finish and minimize the perception that you're priorities are "all over the map," then everything should pan out.

Further to the recommendations by other collocutors, it would be important to raise with your advisor the issue of your further employment.
I am not sure how things are done in your specific field, but in most areas it is wise to wrap up a dissertation when either a postdoctoral offer is already there, or at least when the advisor realises that (s)he can fix one for the candidate.  Of course, each advisor is different and each situation is special. Still, a reasonable professor will care about their disciples' future career stage. So you may want to raise the issue with your advisor. Can he use his connections to get a postdoctoral or similar job for you? If not, then what are your chances for your getting one this year, in his opinion? In my view, this is a key matter in your situation.


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How would you respond if you were forwarded an email by your secretary that clearly wasn't meant for you?
I'm an undergraduate senior seeking to continue on into academia, and one of the avenues I'm pursuing to accomplish this is through an NSF GRFP proposal. I spent well over a hundred hours agonizing over every word of this, and I'm happy to have three professors that I've done research with who agreed to write my letter of recs.
The deadline for these letters is just a week away, and over the course of the past three weeks, one of my professors stopped responding to my emails, as he is prone to do on occasion when he gets busy. In the past, I had a negative experience where a careless professor forgot to submit a letter on time and this led to my sophomore study abroad proposal being returned without review. In this context, I sent the following email to his secretary (names redacted):

Hi [Secretary],
I wanted to inquire about an issue I'm having with Prof. [professor]
  at the moment. I asked him a number of weeks ago to write me a letter
  of recommendation for my NSF proposal (due November 2nd), to which he
  kindly agreed. He asked me for various things, including my transcript
  and statement of purpose, which I provided. Unfortunately, since then,
  I haven't been able to contact him in any way. I've sent him three
  emails over the past two weeks with no response. It is extremely
  important to me that this letter actually gets sent in; to put it
  bluntly, it's my future that's on the line here. I have in the past
  encountered the terrible experience of having a professor miss a
  similarly non-negotiable deadline, and I suffered from the loss of
  that opportunity as a result. I would greatly appreciate it if you
  could gently remind him of the deadline and ask him to confirm that he
  did indeed receive all of the application materials I sent him. If he
  needs anything else from me, I am more than happy to send it to him.
Thank you very much,
[Me]

Of course, the first thing she does is forwards this email directly to my professor, even though it was never written with his eyes in mind. I'm now worried that my professor (with whom I otherwise have a good relationship) will think less of me and write a poorer letter of rec on my behalf. I was wondering how academics would feel if they were put in a similar spot, and if I ought to be proactive in rectifying the situation, or if I should just let sleeping dogs lie.
A Socially Anxious Student

Always assume that emails could be or become public, then you'll never write something you might regret later (unless you explicitly decide to take a calculated risk). I would just not write anymore, though - if you want to push further, go to the secretary in person.

Your email is totally fine.  If your professor reads it, I'd guess he will not be offended or annoyed at all, and will think your email is reasonable.  At least, that would be my reaction.

It's very simple. Never, ever commit anything to print (digital or otherwise) that you would be embarrassed to have read back to you in a court of law. As it is, your email is perfectly fine.

@littleO And a little bit ashamed of not replying to the student.

In this situation, you should phone.  Secretaries do often still answer the phone, or respond to a brief voicemail.

"Oh goodness, this student is motivated to meet deadlines and takes polite by firm action with a reasonable amount of time to react to keep them. That's terrible!" - no professor ever.

show up at Professor's office, bring baked goods (especially if they contain chocolate).

To quote a professor I had Prof: "I only read emails from addresses I recognize" Me: "How many emails do you recognize?" Prof: "One, my secretary's"

My first question is what you thought would happen? In my experience, many secretaries would handle it exactly in this way - your email is fairly unspecific in what you thought the secretary would do ("remind him about this" - he/she did by forwarding your mail, and at the same time probably wondering why you did not send it directly to him). If you thought that the secretary would take responsibility for hunting down the recommendation letter for you, I think this is an unreasonable assumption. The secretary is the professor's assistant, not yours. In the olden times this may have included managing the professor's work backlog, but nowadays most people prefer doing this themselves and just having the secretary arrange meetings etc.

even though it was never written with his eyes in mind.

Tip for the future: never write something to staff where you would be embarrassed if the boss saw it, especially if you don't know the person well. However, I see little in this email that would offend me, other than maybe the implication that you thought I could not be approached directly about this. But of course other people get offended by other things.

I was wondering how academics would feel if they were put in a similar spot, and if I ought to be proactive in rectifying the situation, or if I should just let sleeping dogs lie.

I am for "letting sleeping dogs lie". Very likely there really isn't a situation to address, and if there is you can still tackle it once it comes up.

About the wondering why not directly contacting the Professor: I think for the secretary it is clear that mails that she forwards will likely have a higher priority for reading by the prof. He might have not even opened the mails by the student as they are low prio for him, a mail forwarded by the secretary - his well trained filter - has higher chances to be checked out. Whether he then finds the time to act on it is a different matter again. So this approach does have some merit and forwarding - and thus pushing the issue - makes probably total sense to her.

I would extend that to "Never write ANYTHING you'd be embarrassed if anyone saw it"  Data breaches and subpoenas happen.  If you commit something to text, especially digitally, assume its going to be read.

Although it’s correct that many assistants would handle the email in the way that the current one did (and also true that OP likely has nothing to worry about), in my opinion forwarding the email was poor form, and a really competent assistant (yes, such people exist!) would handle the request more tactfully. So OP was perfectly reasonable (if a bit naive) to assume that their request would be handled in precisely the way that they were asking for. (I have made a similar incorrect assumption myself several times.) So your “what did you think would happen?” tone is needlessly judgmental IMO.

Always assume, at all times, that the people you interact and work with will take the path of least resistance to solve any problem you present them with.
In other words, if you're writing an email to a coworker asking them to remind another coworker to do something, then just assume that, no matter what else was said in the email, it will be forwarded whole cloth to the person in question.  It only takes a couple seconds to forward an email, but it takes significantly longer to write a new email from scratch and both will satisfy the requirement in their mind, no matter how insensitive the original email might have been.
A little exercise I use with every work email I send:  "Would anyone be greatly offended if they read this and they weren't supposed to read it?"  If yes, then reword it or go tell them in person and in private.
I've seen this happen 20 times across a very long career, sometimes resulting in huge backlashes and sometimes not.  In your case, the wording is very mild.  I wouldn't be offended at all if I saw this email in my inbox from one of my students/employees.  In fact, I would be more irritated at myself and apologetic that I didn't act sooner.

If I was a secretary with dozens of unread emails every morning, I would probably do a glance-only first swipe of them (what I and some colleagues call "diagonal reading").
And if I glanced over your email, and read something like this:

"Hi blahblah Prof X blahblah write me recommendation blahblah not able to contact him blahblah extremely important this letter gets sent in blahblah"

I would immediately stop reading and just forward the email to the prof., which is what you seem to be asking for, and exactly what the secretary did.
Next time:

be less chatty and more specific about what you want;
please make use of line breaks and paragraphs to break your emails into:
Greeting
  Introducing yourself (when needed)
  Current Situation
  Effects of not solving the situation
  Requested action
  Thanks

This would make grasping of the important concepts easier for people that are in a hurry. Your email would be clearer if it looked like this:

Dear Secretary,
I'm Me, a former student of Prof. Professor. We have an ongoing issue right now, maybe you could help me?
Professor agreed to write a recommendation letter for my NSF proposal. I provided all the information he asked me to, including transcript and statement of purpose, so everything should be good to go.
  Unfortunately, I haven't received the recommendation letter yet. I've tried contacting him by email several times over the last weeks, with no response.
The deadline for the recommendation letter is November 2nd.
  It is very important that we don't miss that deadline, my future depends on it.  I have in the past encountered the terrible experience of having someone miss a similarly non-negotiable deadline, and I suffered from the loss of that opportunity as a result.
Could you please check with him if he already wrote the recommendation letter, or remind him to do so before November 1st so as to meet the deadline?
  If he needs anything else from me, I am more than happy to send it to him.
I greatly appreciate your help.
  Thank you very much,
Me

Even when "diagonal reading", human eyes tend to focus on the beginning and end of paragraphs and lines. So, by putting your most important points in there, it is less likely that they would be overlooked.

Yes, but put the requested action first! Follow with everything else.

I've said this before and I'll say it again - phone. If you have this secretary's number, or even his number, phone it. He won't begrduge you for being proactive in the situation.
I have done this before when chasing a reference after waiting a few weeks with a deadline approaching. I phoned the supervisor in question and she provided it within 24 hours.
Definitely be proactive. It's more likely to gain respect from him/them rather than thinking you're some kind of academic irritant for getting what you need.

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How to become a good advisor
At some point in my career I would like to become an advisor (or at least have some people working for me). I would like to start learning the skills to be a good advisor before becoming an advisor. Having in mind that most skills will be similar to those needed by managers outside academia:
How can I acquire the skills needed to become a good advisor before becoming an advisor?
Here's what I've thought of so far:

Organize congress/symposium ... (Do it within student organizations)
Advise students

Some background of my situations: I have recently completed a MSc in Bioinformatics (Europe) and I still haven't started a PhD, but I already have almost 2 years of experience in research in Bioinformatics and would like to keep working on this field.

Only one skill, learn how to choose good and right ppl, if you cannot, be careful with personalities.

@SSimon nay tip about how to choose people?

@liopis Yes, please ask that as a separate question, you will get more upvote. and it is better question. I think you are not aware that academic advisors dont care about personality, they are highly egocentric, including me, and others I know.

I'm not sure how to ask that question, as there are other question related in the site  and I don't want to create a too broad question. But I think that academia varies a lot (as much as people do).

something like, how to consider good candidate or how to develop or train student, but ask first on META academia @Llopis in order to help you elucidate which question is appropaite and doesnt eexist yet

@LIopis meta meta

Please narrow it down -- are you asking for a list of skills, or are you asking how to acquire them?

@aparente001 Thanks for the comment. I mean the later, I don't want a list of skills.

I see that the question is voted as off-topic, too-broad and unclear what I am asking, further comments to improve the question are welcome :D

I think you need to specify your area. At least at good departments, PhD students are not "working for" their adviser.

Please check my edits.  Also, what do you mean by "have some people working for me"?  And I want to make sure we're on the same page about what it means to be an advisor.  Do you mean a thesis advisor?  Someone who is guiding a PhD student in choosing a topic, doing original research, writing it up in a thesis, and defending the thesis?

Should this be a community wiki?

@aparente001 Perhaps that sentence is a bad translation, I mean being a boss/manager and having employees/other people who report to me. Yes, by advisor I refer to thesis advisor.

@Orion, Why should the question be community wiki? I still don't know if it will remain open...

@Llopis - If you want to try to rescue your question, I suggest you try splitting it up into two.  Being an academic (thesis) advisor is a different endeavor from managing a lab, for instance.  No guarantees, but that is my suggestion.  You can ask for guidance at Academia Meta.  From the voting, clearly there are others who are interested in this.  (I myself voted to close because I found the question unclear, if I remember right.)

Communication skills are very important, maybe more important than academic ones.
Stay human and be empathic. When you ask someone 'how are you' make sure you actually mean how the person is, not just the result. People need to be treated and seen as people, not as machines. Never ask judgemental questions, never say: 'I have told you so!', 'I have already shown/told that', 'why don't you know?'.
You must gain people's confidence and you can only earn it if you are open and non-judgmental.
Before becoming an advisor, it is important to improve your communication skills through activities that might be outside your curriculum: courses about public speaking are a must have, but theatre is a good and funny way for achieving this as well.
Moreover, it is important to discover if you really like being an advisor.
You could be a trainer for various small events, like a workshop.
Or you could teach school kids different topics, as a volunteer in an ONG. Even if it is not your topic, even if they are not students, you will have the experience of teaching.

"Never ask judgemental questions' -> I guess you've never encountered poor students who understand only a small fraction of what you say in every meeting, or non diligent students.   Be honest to students, and let them know when they fail.  No point leading them on. End of the day, they waste your time and theirs.

You will meet students with different cultural and academic backgrounds. Something obvious for you, might be hidden for them. You definitely loose a student when you rush to consider him/her as a waste.
And if you explain the same thing, multiple times, but the student still don't get it, then you should change your explanation or your approach.

Even that doesn't help.  I've done this for decades and know when a student has no hope, and he/she is kidding him/herself.   Best advice is to be honest, and put them on a non-PhD path.

@Llopis you see? the case study of ego...as I mention below your question

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