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Data Science undergrad degree from business school isn't what I expected to be
I chose a data science undergrad degree in the best-ranked business university in Peru (where I live). I've always been very interested in math and in programming so I thought I made a great decision.
After studying this degree for one year and a half, I feel that I've made a very bad mistake; not because I realized I don't really like math and programming. I still like them and have achieved good scores in related courses. The problem is that I feel that the degree doesn't has enough Math/Programming/C.S-related courses in order to be a well-prepared data-scientist. I say this because I've compared study plans of data science undergrad degrees from universities in the US and other universities in Peru with the study plan from my university (in Spanish) and, although both include non-major related courses, I think that mine has much more to a point that it's counterproductive. Here are a few examples:

I only have 3 pure statistics courses in my study plan. Only one of them uses statistical programming languages. One is basic statistics in which half of the topics covered are already taught in school (at least in Peru).
In my study plan there are two introductory courses to programming that teach exactly the same thing (what is a variable, if, while loops, arrays, how to import .csv files, how to make graphs, etc.) and the only difference is the programming language (R and Python). It feels like a great waste of time.
Some mandatory courses that I find really irrelevant: 2 accounting courses, 2 economic courses, strategic marketing, human capital management, finance fundamentals, strategy (from the business administration college so it's not related), etc.
All of the above don't include the social science/personal development/humanities courses that are 6 in total (Some of them are Theology, Philosophy, etc.)

Since this is a 5 year program (I'm only 1 year and a half in), maybe it's to soon to say that my study plan lacks of enough courses. I've been thinking of some solutions:

Learn the things I don't learn in university on my own. But, I don't have any certificate for that knowledge.
Do nothing about it and just hope I'm wrong.
Complete this data science degree and then do MSc/PhD in a related field (Statistics/Mathematics for example) to ensure that I will have a well paying job.

Underemployment is very huge problem here in Peru so I don't want to end in that situation.

What is the actual question?

"mandatory courses I find really irrelevant: 2 accounting courses, 2 economic courses, strategic marketing, human capital management, finance fundamentals, strategy" um, what exactly kind of data do you think data scientists get paid to analyze? What kind of decisions is that analysis expected to inform? I was really with you on this question...right up until I read that bit.

Sounds like the problem is you are in a business school and what you want to be in is a school more focused on math, science, computation, etc. The business, accounting, and economics knowledge will serve you well, especially if you get an industry job. But I agree that you'd benefit from a note rigorous program focusing on the technical details since that seems to be what your are interested in.

Ultimately, the purpose of a bachelors degree is to teach you how to learn.  Once you get to graduate school you will continue to learn predominantly on your own (your advisor should be your primary collaborator).

Just want to reiterate @JaredSmith 's comment: as someone with OP's ideal job, I'd say these courses are just as critical, if not moreso, than the programming courses. You can learn new code languages on the job, but if you can't follow the motivation behind the work and communicate, you might not even land the job, get into the important meetings, etc. And I just want to plug those humanities courses too: you're going to be working with people the rest of your life, and you don't realize now how those can help you think differently and build relationships with people from other backgrounds.

If the unemployment rate is high, in general there is nothing you can do about it. It is a structural problem, out of your own control, and employment prospects of course depend on very many factors.

It sounds to me like you are enrolled in a program which emphasizes a classical liberal education. The program is intended to provide you a well-rounded education, which gives you more skills than an automaton who has only studied statistics and programming. Congratulations. This looks like an entirely reasonable course of study to me, similar to many data science programs that I have seen in American universities.
To give some further context, I am going to once again cite Matt Might's fantastic image:

In the classical Western tradition, a bachelors degree is not intended to make you an expert.  It is intended to give you a broad foundation of knowledge and skills, with some specialization into a particular field or topic.  After completing a bachelors degree, you should have a little bit of knowledge about a lot of different things, some specialized knowledge, and a set of skills which should enable you to learn new things more easily.
That being said, you are also just completing your first year.  This often involves taking a significant number of "general education" courses, which are designed to ensure that you leave the program as a more well-rounded person.  It looks to me like you will be taking more specialized courses later on (after Cycle IV, if I understand the program map correctly).
Having explained what I think a bachelors education is, let me also outline a couple of things that a bachelors degree is not:

A bachelors degree is not job training.  Generally speaking, if your goal after a bachelors degree is employment outside of academia, the degree demonstrates that you are teachable.  Most employers will not expect a person with a bachelors degree to know everything they need to know in order to do a job—some amount of on-the-job training will be expected (indeed, many employers would rather that you don't have too much specialization, because then there are fewer bad habits that they have to train you out of).

A bachelors degree is not a graduate degree.  In the process of completing a graduate degree, you are likely to be pushing at the fringes of what is known.  You may or may not be required to expand human knowledge (that is generally the difference between a masters and a phd), but you are expected to at least walk right up to the line.  A bachelors degree, in general, is not nearly that specialized.  You should be walking along well-trod paths (mostly).

I agree with this.  My spouse works at a FAANG company as a software engineer and has a degree in Economics.  He spent 15 years building skills in data science before it was all the rage.  He got his current job by doing LeetCode ad nauseum.  At some point the theory is great, but if you don't spend time practicing it, it doesn't mean anything beyond an academic understanding.  To your point, he says many of the younger generation do feel like bots instead of people.  He's the "old man" in his 30s, but his education was more well-rounded, and he has charisma and social skills, which matter.

@XanderHenderson Thanks for the answer! I realized I had a wrong understanding about what a bachelors degree is

@yourfriendlyresearchadmin At my current institution, which is a community college (about half of our student enrollment is in "vocational" programs like welding and cosmotology), we require all of our associates degree seeking students to complete a set of general education requirements (English, math, social sciences, etc).  This is not entirely the norm among community colleges.  However, the feedback that we get from our community partners is that our students show up to work with unexpected skills.

As one of our English faculty likes to say, "The degree gets you a job.  The 'soft' skills and knowledge gained through the general education requirements are what get you promoted."

@yourfriendlyresearchadmin I don't mean to undermine your spouse for his hard work, but leetcode is not a well-regarded website. It is generally seen as a grind that's required to pass an arbitrary test that some companies do during interviews.
I've done my fair share of leet code challenges, I can count the number of times I actually needed the knowledge I got from leet code on one hand. The majority of the content is so far removed from what actual software development is like, it might as well be useless.

"A bachelors degree is not job training." what?? Even specialized high school education is job training for many jobs. Also I wouldn't expect a major to know everything needed for the job right away. I'm yet to see education preparing you properly for real job, unless it's heavy on internship.

@akostadinov From your tone, I feel that you disagree with me, but the rest of your comment says almost the same thing I said.  "Job training" means "training for a specific job".  That is not what a bachelors degree is---it is not training for any specific job.  At a community college, you can complete an associates degree in welding, and get a certificate which qualifies you to work as a welder---that's job training.  You can go through a nursing program, and be certified as a nurse---that's job training.  A bachelors degree does not train you to do any particular job.

@ChellCPlus, like any sort of rote practice, it depends on how you use it. At this point, he can do many of the hard problems in a couple of minutes while also having a conversation with me. When he first used the site it would have taken him two hours to do the same problem, and it would have been slow. He still practices even though he got the job 8 months ago because he believes he uses the knowledge in his daily work. I'm sorry that the site doesn't work for others; I can't speak to why you don't use the content. It does work well for some and has made them successful and educated.

@XanderHenderson, many bachelor programs include internship requirements. And according to local law, you are qualified to do certain types of jobs. So one thing I disagree is that bachelor does not "legally" train you for that. But then the other thing I disagree with is that major teaches you to do a particular job without further training. Neither one does or doesn't. Usually both don't. But for more straightforward jobs, both do.

@yourfriendlyresearchadmin leetcode : software development :: SATs : college degree

Your program looks quite standard to me, compared to (say) US program. In a continental European program you would be taking far fewer "general education" classes, which reflects a fundamental difference in what secondary and tertiary education is supposed to encompass between the two systems.
Re only three statistics courses: it has long been a particular annoyance to me that data scientists do not know enough statistics and in particular do not have enough visceral understanding of randomness. Unfortunately, a rather narrow grounding in statistics is one of the hallmarks of most data science courses. So your statistics ingredient seems par for the course to me.
I agree that the programming courses (not computer science; but in data science, you need programming skills and not CS knowledge) seem rather thin. On the one hand, a data scientist should really understand a little more than "what is a variable" (but then again, they presumably also need to teach people who have never coded before, so they need to start at the very beginning)... and on the other hand, it looks strange to learn R and Python, but not SQL. Then again, there are thousands of online courses or bootcamps where you can pick up most of what you need, and in the course of your career, you will need to learn additional languages at some point, anyway.
Regarding those courses you find "irrelevant"... I could hardly disagree more. One could quibble with the precise courses you list here, but accounting is absolutely fundamental knowledge for a data scientist who wants to work in industry. The better data scientists stand out by understanding the business and economic impacts of their analyses and recommendations! Far too many data scientists believe their job is done when they have improved prediction accuracy by 5%, and perhaps written up a requirements document for the software developers to turn their ideas into production code... but the important decision then is whether these 5% are actually worth putting into production, and for that you need a solid grounding in business and accounting, and in the language that the business people use. Data science is not an end in itself.
In the end, data science - like many other fields - requires you to stay self-motivated and go above and beyond what you learn in school or at university. If you find you already know what your courses are teaching you, great! That gives you free time to delve more deeply into aspects where your courses only touch the surface. Read statistics books, learn more programming languages, or do Kaggle competitions in your free time, and do internships to see how data science is actually applied "out there".

Bachelor's programs in the UK are also often much more highly specialised than in the US (as our high school is also more specialised). I think it's worth bearing in mind with the programming courses, that this is just the first of 5 years.

I can only agree with "data scientists do not know enough statistics". Even in academia there are often three separate communities: (1) statistics, (2) decision support/operations research, and (3) data science/machine learning. Communication between these three communities is surprisingly rare.

Sounds to me like data science is not science.

@gerrit: I would consider it a mixture between science, engineering and art, just like forecasting, with more engineering and less art. The statistics parts are definitely science. The domain knowledge is engineering, often with a side of business/economics.

Incidentally, I would appreciate it if any downvoters left a short comment indicating what they found "not useful" here... I would love to learn, and the OP would profit from understanding just where people disagree with me.

I think what the OP is missing re: R and Python is it's not just learning different languages but learning different classes of languages. Eg Python would be C-like, SQL would be a declarative language, and R would be in the category of languages created by scientists/mathematicians.

Sounds like you think your current Bachelor's program lacks rigor.
I'd suggest two things:

First, check what data science jobs in Peru require. Go to your local jobs portal and search for job advertisements for data scientists. Here's an example. Then see if you learn all the things you need for the job in your current program.
Second, talk to someone at your institution. If there's someone at your institution who coordinates the program, or advises students on which courses to take, they would be the natural candidate. They can likely advise you on why the courses are the way they are, or accelerate you (waiving some courses), or otherwise find you challenging stuff to think about. One possibility you've not mentioned is that you might be able to transfer to another program. If your current institution cannot provide you the rigor you want, then this is a possibility, and your coordinator might be able to help.

Definitely don't do nothing and just hope you're wrong (solution "#2" in the question). It's better to worry about these things now, before you graduate and then find you can't find a high-paying job.

Thanks for the answer! Regarding the first suggestion, after spending the last hour inquiring about DS jobs in Peru, I can say I feel much more calm about my future because they ask for things that are, in someway, in my study plan.

Regarding the second suggestion, there is no one that suggest students which courses take in my university (at least not that I know) since there aren´t too much electives as there are in american universities and the one who coordinates the program is the dean (Not sure if it´s worth sending him an email arraging a meeting just for that and very sure he´s going to say no to me if I ask him to meet).
A transfer is definitely not an option since I don´t want to study things that I´m not interested in, I´ve already experienced that in school and it was like a nightmare.

@gnzlama by "transfer", I mean transfer to another institution. As for the coordinator, it's very likely someone at your institution knows more. Someone has to put together the list of courses that data science students needs to take, someone has to teach those courses, and they would know. It's just a matter of finding out who they are.

I largely agree with the other answers, but want to give a bit more context. Because data science is a new field, there is some disagreement about how it should be taught. I co-authored a paper, Curriculum Guidelines for Undergraduate Programs in Data Science, that has been cited almost 300 times. We lay out the following curriculum (the order is basically what we do at Denison University, where I teach):

Intro to Data Science I, where you learn how to manipulate data in R, visualize data, make confidence intervals, do null hypothesis significance tests, fit and use regression models, etc.

Mathematical Foundations I, which is a data-science themed calculus course focusing on applications.

Algorithms and Software Foundations = intro computer science, with a focus on real-world problem-solving. We use Python.

Mathematical Foundations II, with some multivariable calculus and some linear algebra. Ideally enough to understand principal component analysis. To squeeze all the math of the major into two courses, it's not possible to cover everything, and again applications are emphasized.

Introduction to Statistical Models. This teaches many statistical models, bootstraping, lots of statistical tests, any probability theory that is needed, etc. The full list of topics is in the linked paper above. This course includes a deep dive into R.

Data Curation—Databases and Data Management. This is basically "all the computer science (after intro) that you need to do data science." It covers tidy data, SQL, hierarchical data, data wrangling, web scraping, and APIs. I also co-authored a book, Introduction to Data Systems, that goes with this course. We use Python.

Another Data Science course, ideally a statistical consulting practicum where students put their skills to use on a problem provided by one of our industry partners.

8-10. Three courses in a cognate discipline, like economics, biology, physics, chemistry, sociology, business, health, etc. The reason for this is the Data Science Venn Diagram where you need math/stats, CS, and also disciplinary specific knowledge.

Statistical and Machine Learning.

Capstone where students do a full analysis on a data set of their choosing, based on all they have learned.

At Denison, our Data Analytics major is one of the largest on campus. Even though only nine courses are required, our students go on to great jobs. Because the field of data science continues to evolve, the expectation, coming out of any program, is that students will need to teach themselves some things; no curriculum can cover everything you will ever need to know. For example, sometimes we teach students Tableau in (6) above, but sometimes there's not enough time. Students can pick up Tableau on their own if they need to. Same for most tools.
Some other universities have a data science program that looks like CS + Stats + some math, all in one. It can be too many courses. We prefer to take a "big tent" approach to data science, where we invite lots of students in, teach them as much as we can, but don't set up barriers to people choosing this major. The OP wrote:

The problem is that I feel that the degree doesn't has enough Math/Programming/C.S-related courses in order to be a well-prepared data-scientist.

Based on the program of study the OP describes, upon graduating, the OP will have even more courses than our students (e.g., three in stats). Whenever an employer looks at a transcript, they don't know what precisely was covered in the course. If you find some of the courses easy, you are welcome to delve deeper into the material and do some self-driven projects and host them on GitHub. Our students often do 2-3 projects. That helps them get a job. It's a way of showing employers that the student really has mastered what's needed to be a working data scientist. There are many projects in the book I linked to above. Again, remember that there is no standard curriculum, so a lack of coursework can easily be compensated for via other evidence of mastery.
Lastly, the OP wrote:

All of the above don't include the social science/personal development/humanities courses that are 6 in total (Some of them are Theology, Philosophy, etc.)

Don't sell these courses short: ethics matter! Data science consists of a set of very powerful tools that can be used to carry out convincing analyses that can have real-world impacts. It's entirely appropriate to get students thinking about ethics as part of that! In the curricular guidelines, we discuss the importance of ethics. At Denison, we infuse ethical discussions throughout all the courses. If all goes well and you become a data scientist, you will likely find yourself facing thorny ethical questions at some point in your career, and you'll be happy that you took the time to think through related questions as a student.

I disagree with the other answers.
Data Science is the child of three overlapping disciplines:

Mathematics (Statistics)
Computer Science
The field the data in "Data Science" is coming from

It appears to me, that "Data Science" majors often focus on the latter two subjects (i.e. how you stir the data and how you implement the algorithms) and only do surface level courses into the mathematics which make them work.
If you actually want to develop the algorithms used in Data Science, you need a heavy focus on 1/2 and need to talk to people from the applied fields for inspiration. I.e. you want to be a toolmaker not a tool user.
If that is the case, you need to study maths (which typically also teaches you programming) and focus on probability and statistics when selecting courses. Mathematics is typically a thing which is required a build up from the bottom and higher level mathematics always requires lower level mathematics. You can't talk about the convergence of random variables, if you were never introduced to the concept of convergence (i.e. you need analysis before you can do probability theory). And multidimensional derivatives are linear maps, which will require some linear algebra. This is why mathematics bachelors typically require you to take Analysis I & II and Linear Algebra I & II before giving you freedom to choose courses afterwards (at least that is how it works in Germany)
Since mathematics builds on top of itself, this makes switching to it from a different subject very difficult. So I would not recommend switching to it at a MSc or PhD level. Don't buy into the sunk cost fallacy and continue something which does not get you where you want to be.

(1) I am a mathematician by training, and pretend to be a statistician. While the fields are closely related, on an applied level statistics is IMO quite different from math. (2) Few data scientists will ever develop the algorithms they use. Most will use algorithms others have developed. I agree that (a lot of) statistics is necessary for that, and that math is useful (and some math, like linear algebra, calculus and probability is indispensable).

@StephanKolassa that is why I make the distinction between applying data science and developing data science. Yes, if you just want to apply it you don't need the math. But it feels to me like OP actually wants to contribute to the development so I wanted to add this perspective.

And "some math, like linear algebra, calculus and probability is indispensable" is almost filling up a maths bachelor. I mean first and second semester are linear algebra and calculus, then you might have probability and numerical analysis (which you probably want too) in third semester and then statistics and more advanced probability theory in fourth semester (together with courses from a data science/compsci minor). At this point only the fith semester is left and in the last semester you write a thesis which could be in an applied field such as data science.

Functional analysis would also be useful to develop a deep understanding of kernel methods (as the reproducing kernel hilbertspaces are, well... hilbertspaces), and other universal approximation theory. You might want to have taken a course in optimization too. Dynamical systems (differential equations) establishes a view of gradient descent as a gradient flow. And at that point you could also do stochastic differential equations, etc. So you can easily fill up a maths bachelor just picking subjects useful to get a deeper understanding of data science.


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AI Tools for Restructuring an Doctoral Dissertation
I recently changed PhD programs and need to restructure my 325+ page dissertation according to the guidelines of the new program. The subject and approach are the same but the chapter outline/structure to which I am now asked to conform is a bit different.
To be clear, I am doing this with the ok of the new program I have entered as the my dissertation has not previously submitted or published.  Is anyone aware of any AI tools that can help with this.

I would suggest that actual Human Intelligence is required for this. Unless, of course, someone has trained the AI tool using your program's doctoral dissertations exclusively.

Why would you want to run your dissertation through a nonsense-generating machine?

As software questions are excempt, this is not a shopping question.

You've asked for "artificial intelligence" tools and tagged your question with "generative-ai".
I'm not sure that there's really any software tool in that genre which is appropriate for this kind of thing, or at least that's any more efficient than copy, paste, and your own judgement. A few points to consider:

Efficient organization of information and context about a field is part of the likely somewhat novel contribution of your dissertation. While this kind of synthesis isn't as heavily emphasized as your primary-source work, it's still relevant. Diluting your contribution by using tools which produce a kind of guided random walk through previously seen organizational structures seems less than ideal.
The world is now replete with examples of large language models making clear factual errors when prompted to produce technical content. You may find this mitigated somewhat if you're simply prompting to move big chunks of pre-written text around. One of the main components of your academic reputation, however, is trying to minimize such errors in your published writing. You will have to re-read anything that an LLM outputs with a fine-toothed comb to make sure it has not introduced factual errors.
As a technical point, the document you're describing is almost certainly too long to fit whole in the context window for generally available large language models. Here is a discussion of that issue for ChatGPT that pops up on a cursory Google search. Even if it were likely to be useful, I think you'd find it expensive in time, money, and/or computing resources to arrange a solution to this.

In a nutshell: You're already an expert on the content of your document and presumably not an expert on resolving technical issues at the limits of what LLM's can output. It seems likely to me that you'd find it much more efficient to do the reorganization yourself.

Your dissertation is meant to be the proof that you are qualified to be an independent researcher. It should be the thing that you are the world expert in, the documents that you are the only person in the world who is qualified to write.
If it's something that an AI can write, then it's not something that's worthy of a dissertation.
If you edit the AI output enough that it's considered your writing, is it really less work than just editing the original document? AI written text is the worst to proofread: it sounds plausible but contains factual errors. The plausibility of the writing makes the errors that much harder to find.
See, for example, Stochastic Parrots and ChatGPT is Bullshit.

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What are the opportunities after studying Bachelor of Philosophy?
I am currently studying Bachelor of Philosophy. I want to know what are the career options I have available. I am aiming for a Masters and PhD.

There are only a tiny number of jobs where a having a PhD in philosophy will help you get hired.  It's not a good way to make money.

Well, what do previous graduates from your program do? With or without an advanced degree?

If you’re not financially constrained, any advanced degree of interest to you might be worth pursuing. If, on the other hand, you’ll need to earn your bread by the sweat of your brow, your philosophical studies might better occupy your weekends and after hours.

This really depends on what the culture of your country is.  In the US, the vast majority of college graduates work in jobs that have very little to do with their major.

What happens after your degree is usually outside academia, and hence outside the scope of this SE.  Voting to close.

Search your local jobs portal.

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Obtaining Research Experience as a Post Baccalaureate
I graduated nearly 5 years ago with a BBA in MIS. Since then, I have been working in unrelated fields from my planned field of study. I recently decided to pursue a graduate degree in statistics and I am currently working on my pre-requisites and studying for the GRE.
A friend, recommended me that I apply as a research assistant at the prestigious program he just finished/graduated from, he recommended that I obtain research experience before applying to increase my chances of being placed.
For someone in my position, how can I go ahead and obtain research experience or relevant experience related my planned field of study?

What country/countries do you plan to study in? How much math do you have?

In the USA, there are so called "post baccalaureate" opportunities at the national labs. But they are not that widespread. Check out, for instance, Zintellect for USA government opportunities (national labs and military labs): https://www.zintellect.com/. If you're somewhere in the world, then I know EU has a nice site with governmental scholarships. Else, I am using this aggregator of worldwide scholarships: https://scholarshipdb.net/. Also, check out "glassdoor" and "indeed" websites. I remember I saw postbacc offerings somewhere, but I don't remember where exactly.

@Buffy I plan on studying in the US. Fortunately, I live here and have citizenship so that is not an issue. As for math, I only just starting Calc 2 and linear algebra.

Research administrator at an R1 here.  I help get people onto payroll.  My school has a postbac program in certain areas (not all).  There are rules, timelines to apply, etc.  I would just google for "postbac program" and the field of study you are interested in to see what is out there.  Here's a quick example that I found for Yale:  https://chem.yale.edu/academics/chemistry-post-baccalaureate-research-education-program
The old-fashioned networking way is to find a PI who will just hire you as a research assistant without a formal program.  We do that all the time as well.  The pay is less than the PhD students receive and also above minimum wage.  At my organization, this is actually the preferred way for faculty, because they can put these sorts of people on grants more easily.  Postbac programs may have requirements that make this more difficult -- all of these rules will depend on the organizations you are working with.  If you can get a recommendation to work with one of your PI's colleagues, that may be a good choice.
While it's been some years since you did your degree, if you were a good worker and productive, the PI may still give you a recommendation.  My PIs take on folks with unusual backgrounds when they know they will be a net positive (hard worker, contributes to the lab work).  If you will need a lot of oversight, you may be a harder sell. It depends on your background.  We have hired folks from industry who have a great work ethic, and they even end up with permanent staff jobs in the lab.  You won't know unless you ask.  If you don't want to ask, you will have to go through a formal program and be subject to those requirements (which may in fact be more work).


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When Is It Okay to Leave Mediocre Work Off Of Your CV?
I'm currently a first-year PhD student in a quantitative science. As an undergraduate I performed research with a postdoc which resulted in a co-authored paper on arXiv. To fulfill a requirement to receive honors with my degree, I also wrote a senior thesis based on the work. My department explicitly said we were allowed to incorporate past papers into the senior thesis as long as we cited them and clearly marked what material was adapted, so most of my senior thesis is just copied from the paper.
But I also had to write some introductory material to make the thesis accessible to non-specialists in the field, and to be honest I don't think I did a very good job writing it. I did this kind of quickly and wasn't very motivated because I didn't expect that anybody would ever actually read it.
If somebody in the future wants to know what I worked on in undergrad, I would much rather that they look at the co-authored paper rather than the thesis, which is just the co-authored paper with some poorly, hastily-written pedagogical material tacked onto the beginning. Would it be considered dishonest to leave the senior thesis off of my CV and ORCID page?

Anecdotally, I have hardly ever seen an undergraduate thesis listed on a CV of someone more than a couple years into graduate study.

You need not worry, one should not be listing their undergraduate thesis on their CV anyway. In terms of "products of research" one should list Publications (peer reviewed), Book Chapters, Conferences (peer reviewed), Conferences (non-peer reviewed). An undergraduate thesis is just a glorified assignment, nobody will dig it up or hold it against you.

In general one need not list everything on their CV. In fact, for some it would make an impossibly long document. What you include should be valuable to you and to others, but it is fine to leave off early work (especially).
It is unlikely that people will be much interested in your undergrad work once you begin doctoral study. For most purposes it isn't relevant. And none of us are experts at those earliest stages.
But others may not judge it as harshly as you do, if they know it was done early on.


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Are speeches by Heads of States academic sources?
I'm uncertain whether speeches by Heads of State or other governmental figures are academic sources? Can someone please clarify?

We want you to flesh out your question.  Give us some context: why are you asking?  How will you use the answers?    What purpose do you want to use the source for?  Give us your thoughts: what makes you think they would or wouldn't be a source?  What do you mean by an "academic" source?  What research have you done?  We expect you to do some research before asking.  Any record that others could review is a "source".  It's a different question whether it is reliable, or credible, or accurate, or useful.

A speech by a head of state is a primary source and can be used and cited as such:

Abraham Lincoln's Gettysburg Address begins "Four score and seven years ago our fathers brought forth on this continent a new nation" [Lincoln, 1863]

It is not a peer reviewed scientific document, however, and should not be used in that way:

The United States was founded in 1776 [Lincoln, 1863].

That's a very easy way to get things wrong, because speeches by heads of state are generally used for political effect, not for delivering factual information.  For example, if you used Lincoln as a citation, you'd be rather misled, because the United States per se did not exist until the adoption of the constitution in 1789.

There were the Articles of Confederation before then, but you could argue the nation didn't completely exist until the early 1780's.

+1 for citing Lincoln as the source for when the U.S. was founded. :) However, the last sentence probably depends on how you define "the United States." Its present government didn't exist until the Constitution was ratified, but it was an independent country since 1776. Arguing that that the U.S. didn't exist until 1789 would be like arguing France didn't exist until 1958.

@reirab, Panzercrisis: You affirm my point nicely: a head of state like Lincoln just needs to convey a point to an audience.  A scientific discussion needs to have clear and well-grounded definitions, (e.g. how do the fuzzy notions of "founding" and "nation" relate to the lengthy process of developing a new state).

Don't you need to cite a source for the claim that Lincoln said those words? See the Wikipedia article on the address, specifically Text of the Gettysburg Address. More generally, the claim that a head of state said something should be backed by some reference supporting it.

@PatriciaShanahan That is a fair point: I wrote my answer assuming that the person was actually getting the text of the speech directly in some way, but if the observation is not direct, then it's not a primary source and should be cited to the actual place where the information has been obtained instead.  For example, writing "Art thou the King of the Jews?" [Pilate, 30] would definitely be incorrect, and the quote should be sourced to the King James Version of the bible instead.

Speeches by public officials, and especially heads of state, are historical documents, as they often crystallize and clarify political priorities, justify policy decisions and set the tone and direction for action.
Thus I would not call speeches by heads of state "academic" sources, in the sense that they are neither written and delivered by academics, nor are they intended for academic audiences or purposes.
As @jakebeal points out, they are not peer reviewed, nor are intended to be, in the same way as are academic publications.
However, in certain disciplines, especially history, political science, and communication/media studies/PR, they are probably widely used and considered acceptable primary sources that can be tapped for citations to support academic arguments (e.g. political positioning, rhetoric, or persuasive strategies).

If President Obama says, in a public speech, that the Riemann Hypothesis is true, then this is sufficient backing for writing, in an academic paper, that "President Obama said that the Riemann Hypothesis is true."  It is not sufficient backing for writing "The Riemann Hypothesis is true."
This has nothing to do with his being a head of state.  The preceding paragraph would remain correct if you replace both occurrences of "President Obama" with "Miley Cyrus."

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


text:
When do I become a graduate student?
I recently was accepted into a Ph.D. program to begin in the Fall---I receive my Bachelor's degree this weekend. I wanted to know when would it be considered acceptable to start calling myself a graduate student? For example, when is it appropriate to change my LinkedIn and ResearchGate profiles (and my CV) to read "Graduate Student" at the institution in question? I asked my program director, but all he said was that I "was definitely accepted into the program." I want to update my status on my resume and online profiles to reflect this new position, but I don't want to be rude. What should I do?

You're not really a student at your new institution until you have actually arrived and registered there.  At this point, it would appropriate to say something like:

I will be a graduate student at Institute X with anticipated start date D

You can certainly update your CV to convey that information immediately.
As for other sites, typically they allow you to list a number of positions with starting and ending dates.  If their system lets you list a start date in the future (I haven't tried that on the particular sites you mention), then that would be a clear enough communication of your current and anticipated status.  Otherwise, you can write the same sort of "anticipated start date" line in your free-text personal description.

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


text:
How detailed should I be about my implemented system while writing a Ph.D. thesis?
While doing my PhD in computer science, I need a software system to perform and test my hypothesis/research, e.g. automatically perform a calculate or experiment. Even though I try to use as many existing frameworks/libraries as possible, I still have to do a lot of programming.
So, I wonder if I should thoroughly describe my implementation along with my research in the thesis. By thoroughly, I mean to provide something like database design, class diagram, etc...

I have taken the liberty to suggesting an edit to slightly modify your title by explicitly pointing out you are asking about a PhD thesis rather than a Bachelor or Master thesis. jakebeal's answer is totally fitting with respect to a PhD thesis, but I would consider it only a partial answer if the question were about a Bachelor or Master thesis (thus making an analogous question about a Bachelor or Master thesis a different question).

Have you actually implemented the system? Have others implemented similar systems? What would you do different from them? Without knowing those questions it is very premature to decide what to put on your thesis or not.

@Alexandros as stated in the question, I try to use as much existing libraries in my system as possible and only implement those that I cannot find, since some features are very specific to my research.

When I think about such problems, I find that there are two main aspects of mechanism documentation to be considered:

Reproducibility: The standard that you absolutely must adhere to is to provide enough information to allow others to reproduce your scientific results.  This means that you need to provide all of the key ideas that would not be readily re-generated by any skilled practitioner.  This means that all of the "ordinary" programming work doesn't need to be explained, and indeed would probably be distracting if you did spend time explaining it.  Note also that reproducing a result is not the same as reproducing data: e.g., if your system provides O(log n) growth in resource requirements, that's what needs to be producible, not the exact particulars of how you tested that experimentally.
Accessibility: Even if a result or system is reproducible, it's often a lot of work to reproduce it.  If your system is likely to be something that other people might want to use themselves, then you should share the code.  Architecture diagrams and similar documentation are nice to make that code more accessible, but they key is to make sure the code is readily available under a free and open license (see also the question: Should I share my horrible software?).

Now, the nice thing about a thesis is that there's typically no limit to the length or types of artifacts that you can attach to it.  I would thus strongly recommend that you make sure to bundle the software, with adequate documentation to allow a skilled computer scientist from another subfield to understand it and apply it.  Don't worry about explaining the "uninteresting" parts of the design, however, unless it's actually directly relevant to your scientific results.


text:
Using native language for research discussion in an English-speaking country
My university is in an English-speaking country, hence most of people use English for communication. However, it is possible to have a supervisor and her student that are from the same country, which is not an English-speaking country.
When that happens, is it acceptable if they use their native language in discussion regarding the PhD research while at school?

Let's put it the other way round: what makes you think that it would not be acceptable? (Assuming, indeed, that there are no other people in the room who don't understand the other language).

In my university (where most of people are from very different countries), it happens that a professor from country X have a PhD student from the same country. In places like seminars etc where there should be an interaction with other people, they speak in English with each other. It is a also an element of respect to others otherwise it can be considered as an effort to exclude other people from interacting or understanding or even saying something that should not be said in a public place.

I might recommend that, if the literature in the field is primarily in English, that it would probably be better to discuss the research in English, and day-to-day matters in whatever language is more comfortable. As an English-speaking post-doc the Netherlands, all the science (by everyone) was done in English, the coffee break was done in Dutch.

In language departments, it's super common, but that's probably low-hanging fruit for this question :-)

I see no reason why it would be unacceptable (as long as it doesn't exclude others). I see it all the time (particularly in Hebrew). However, if it hinders the student learning how to discuss their research in English, that is bad for them in the long term.

Yes. I see this all the time in my school. Although some professors avoid doing so. I believe it can loosen some faculty/student boundaries. Also, it is advisable to use English language when accompanied by other students/faculty who can not speak that native language.


text:
How can a researcher in his early career attract good PhD students?
As stated by the title, I want to know what a researcher in his early career, i.e., having not had any graduated PhD student yet, can do to attract good candidates. The reason I am asking this question is because PhD applicants usually look for supervisors who have many successful PhD students.

Do you work in a system where students are accepted by the department, take classes, and choose advisors later, or where students apply directly to advisors and begin research immediately?

@NateEldredge I work in a department where students are accepted by the department, begin research immediately, and choose advisors later.

Presuming you have some way to fund their work, and you have positions to offer, I suggest you start by recruiting bright undergrads from your upper-division courses. Get them involved in your research before they graduate. Offer them positions as PhD students before they apply everywhere else*. I was so excited by my PhD supervisor's work (our work really), the lab environment, and the place where I was living, that I didn't want to apply anywhere but where I already was.
Some people will object that students should move around between their undergrad and PhD, and there's some merit to these arguments, but if you have funding and can't recruit, you may want to think outside the box a bit. If you don't have any money, or your department won't give you any positions (or whatever the system is at your university), get some!
If you want to recruit from outside, keep publishing. Publish in the best places you can. Become a hot shot. Advertise openings on your website and at the end of your conference talks (if that's acceptable in your field). Be aggressive about finding good students.
*: If they do want to apply elsewhere, write them the best letter you can and wish them well. Maybe they'll stay anyway. Don't be one of those profs with a reputation for trying to control the lives of your students by refusing to write recommendation letters!

In some fields it's highly frowned upon for an undergraduate to go to grad school at the same institution, and in these cases I presume it would also be highly frowned upon for a professor to try to recruit students from their own university.

@DavidZ In others, like compute science, it is very common, and diversification is expected only after Ph.D.

@DavidZ, I'm curious what fields it's still highly frowned upon? I've found that for every frowny anecdote we can find a highly successful counter-anecdote.

@BillBarth physics for one, in the US. Or at least high-energy physics (my field), though what I've heard suggests that most other parts of physics follow the same convention. And I don't believe you can find a counter-anecdote to this - in other words, I don't believe you can find an anecdote which shows that there are no fields in which it's frowned upon for students to stay at the same institution. My point in posting the comment was not to suggest that your answer is wrong, only to point out that it's not universal and the OP should know their field's culture before taking your advice.

@DavidZ, no, the counter anecdote I was thinking of would be a successful prof who got their undergrad and PhD at the same institution. Just randomly poking around MIT's physics page, you can find someone like Joshua Winn who has their bachelor's, master's, and doctoral degrees all from MIT and is a professor there!

+1 especially for the last paragraph.  But assuming you're in touch with other academics in a similar position from your PhD/postdoc days perhaps encouraging your undergrads to apply to work with them will lead to reciprocal recommendations. And it's not uncommon in the UK, in some areas of physics, for people to stay on at the same university.

@BillBarth sure, I don't dispute that it does happen. I know three or four people myself who have done that. But each of them was a special case, someone who stayed at their undergraduate institution to take advantage of a fairly unique opportunity for research and who was allowed to do so by the faculty because of their exceptional talent in that area. All I'm saying is that it's very much not the norm.

@ChrisH, normally you'd be dead on about networking undergrads you know out to former labmates that are elsewhere, but in my case, almost all of my PhD supervisor's former students did not go into academia (including me!). Most are at national labs, research departments (me), or in industry.

@jakebeal diversification [in computer science] is expected only after Ph.D. — [citation needed]

@JeffE Easily confirmable: a few minutes of searching turns up a couple of examples from your own department: Karrie Karahalios, Ranjitha Kumar.

@jakebeal And two of my own PhD students!  But these are exceptions —   expected ≠ required.

@JeffE Was it highly frowned on by your department to recruit your own students, per the David Z comment I was responding to?  Otherwise, I think we may be in violent agreement here.

@jakebeal I didn't recruit them; on the contrary, I strongly encouraged them to go elsewhere.  But yes, I think we're in violent agreement.

Adding to Bill Barth's excellent answer:
Become known to other professors. Bright undergrads who may want to switch places for their graduate studies may ask their professors for good places... so be known to those professors so they can recommend you! (After all, few undergrads already have enough of an overview of the field to know an up-and-coming junior professor.)
Collaborate with people outside your institution. Become active in your scientific organization. Volunteer for program committees. Write good reviews. Offer to review for other things than journals/conferences, e.g., if a conference you attend awards travel grants to students.
Yes, all this is a lot of work, especially since you have your career to jump-start. But to be honest, forging connections now may be a much better investment of your time in the long run than yet another paper - both to attract bright grad students and in other respects.

EDIT: so, the above addresses attracting grad students "in general". What about attracting good grad students?
First, get enough applicants. If you have three applications for every position you can fund, you have the luxury of picking the best applicant. Fill your sales funnel with enough candidates!
Second, see the above advice. Once someone approaches you and says you were recommended by professor so-and-so, you can deduce something, at least to some degree. If you know that so-and-so is good in his field and you have a good relationship with him, you likely can trust the candidate that so-and-so sent your way not to be a total loss. It makes sense for someone at the early stages of his career to give a little more weight to this kind of signal than later on, after you have had your share of Ph.D. students. You will likely end up with some better, some worse students over the course of your career, and you will learn what to look out for in a candidate - but you don't have that kind of experience yet.
Bottom line: work on your relationships.

I joined a new advisor's lab; he was in his second year as a PI when I joined, and there was only one other graduate student. I am now in my third year and we have six graduate students, one postdoc, and potentially three new postdocs on the way. We have done very well in terms of funding so far. The plural of anecdote is not data, but perhaps this will be of some help. I nearly joined a more established professor's lab, but decided to go with the new professor for these reasons, not necessarily in any order:

Other faculty spoke highly of him.
Older graduate students who had taken a class from him spoke very highly of him.
The prospect of helping to build a new lab excited me.
He was excited about his research.
He had several interesting research projects available, with clear and understandable 3-minute summaries of each.
He comes across as intelligent, capable, passionate, kind.
In my first interaction with him, he handed me a paper containing the description of an experiment, and asked me to draw out the physical diagrams of how the system should respond to the experiment. He then pushed and challenged me for about two hours, and I wound up learning a lot. This gave me the impression that he would push me to become the best scientist I could be, while aiming to teach me along the way. I haven't been disappointed.
I wasn't worried about depth of knowledge after this interaction, but he went out of his way to mention that as a member of a new lab, I would be forced to gain a great breadth of knowledge and skills, as well. This was a plus for me.
The senior graduate student assured me that it was incredibly easy to schedule one-on-one time with the advisor, but that he did not micromanage.

Students interested in working for a new advisor will be looking for, among other things, a challenge, greater personal attention from their advisor, and interesting research. You might during the first few years feel a lot of anxiety about publishing, but you should hopefully be able to keep that anxiety in check to allow your students to grow and climb the learning curve for the first few years before putting them under too much undue pressure. You don't want to earn the reputation of the nervous overly-demanding associate professor.

Adding to Bill and Stephan's answers. There are many good PhD students who aren't just looking for advisers with massive reputations. Some sense of personal connection can go a long way in attracting students. Volunteer to teach introductory level grad courses and commit yourself to being responsive to your students. You will develop a following of people willing to pass up the opportunity to work with a more prestigious adviser for an adviser that has demonstrated a commitment to his students.

From the perspective someone who recently went through the PhD application process, I'd say that the best way to recruit good PhD students is to recruit good PhD students.
While I make no judgment as to whether or not I fall in the "good PhD student" set, I can say that my experience (and from what I've heard--most people's experiences are) was extremely opaque. The best way to convince someone to work with you is to make them feel like you want to work with them. There's nothing wrong with reaching out to students whose applications look promising in advance. In fact, most of the faculty I heard from before decisions were younger faculty. They often arranged informal Skype conversations with them and any current students they had, and they brainstormed interesting ideas for research projects that met our mutual interests. As an applicant, this made those groups much more appealing, as it seemed that the professors were excited to work with me rather than me work for them.
Then again, this answer is more specific to the US system where departments tend to make decisions about candidates rather than specific professors. I believe in Europe it's a bit different.
I would also say that if you're concerned about students choosing more established professors over you, maybe approach this the way some borderline applicants do: find the diamonds in the rough. That is, find the students that might be passed over by more prestigious/experience professors for one reason or another, but have some promise to them. This is kind of a hard thing to quantify, but sometimes a raw applicant can become your best PhD student (and similarly a new/young professor can be the best advisor).

All the answers above are good, but there is one more important thing I have learned from observing the woes of some colleagues who need graduate students. [Good ones, of course; no one that I know is actively seeking not-so-good ones!]
Good research! Ph.D. students are attracted to good and interesting topics. I have a colleague who thinks he is doing wonderful work but privately many colleagues are not surprised he's having trouble attracting students. Masters students have different goals and shorter timelines.
In my experience ultimately it has less to do with personality (exceptions, of course) and / or the stage of career you're at (early, mid or late). Students might come visit for the decor, the ambience, the attractive appetizers, but they stay only for the main course!

Well, the other way of looking at it would be the 'Theory Y' approach.
Instead of thinking 'what do I need to do to attract good PhD students', think 'what do I need to do to GROW good PhD students?'
Consider that how someone comes to you is relatively insignificant compared to what you can make them in five years. (especially at typical PhD age)
I knew a guy who was an executive at one of the largest companies in the country, and he told me "give me a rag-tag bunch of misfits, a project, and nine months, and I'll give experts, specialists, and a completed project"
I think the important thing is you have to have people that are willing to change, even if it means loosing face.

Given my scholarship I was relatively free to choose my PhD supervisor. What drew me to my current supervisor, is that although he is young he has an awesome publication list! this gave me the impression that

he is a successful researcher,
his work meets the standards of top conferences in our field and
I'll learn a lot from him.

These three points were enough to make me go through a long and risky procedure with my scholarship provider, in order to transfer to my current supervisor's department. It all worked well and indeed I am thankful that I work with him now.
So I would say, focus on advertising yourself and your work. His personal homepage was always updated with his most recent projects and publications, his name appeared everywhere when I searched for his research area, and his work was cited a lot.


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In Canada, Is it more likely for faculty members to allocate funds for a PhD applicant than a master's applicant?
I am currently applying to a Masters degree in neuroscience (McGill, in Canada). In order to get in, I have to contact an advisor that is willing to fund my studies. If I fail to do so, I cannot enter the program. (This is apparently common in Canada.)
I have already contacted two possible advisors but neither of them responded positively, arguing they had no positions available.
Speaking with a friend researcher, he told me that it is less likely to get an advisor (and funding) for a Masters degree (even though the university is asking me to do so) and that I should better apply for the PhD program because researchers are not interested in investing money in a Master student.
Is this true? Do researchers reject master's degree prospective students because they do not want to invest money in them?
Thank you.

It's somewhat unusual for a masters program to require its students to be funded by their advisors (i.e. to disallow self-funding at the masters level.)

Yes, it is in fact unusual and is stressing me out more than I expected because in McGill they make it clear that a Master student MUST have an advisor willing to fund his/her studies in order to start the program.

Funding for masters of science at Canadian universities is very common due to its requirement for entry to a PhD program. Direct entry to a PhD program is possible in certain circumstances, but is much rarer. My guess would be that most PIs prefer to hire master students (funded), and then depending on their performance, offer them to convert to a PhD. This is a much less risky investment proposition overall.

That presumably varies quite a bit between schools, (sub)areas, ... and perhaps even vagaries like the type and breadth of work the faculty requires from their graduate students at any given point in time. And obviously doesn't necessarily apply to any particular case, like yours.

@ff524 I don't know that funding is the only issue -- equally or more important in this policy is that you need to have an advisor agree to take you on. I did my grad work at McGill, and every grad student is associated with a professor. You can't get in and then find an advisor - you need an advisor to get in. It sometimes happens that you switch advisors, but you're never without one. That said, I am adjunct at another Canadian school and I cannot take a grad student unless they have a scholarship or I can support them from my grants. It's not so unusual here.

You friend might be right as well as the advisors you have contacted with.
It is true that a master's student is less likely to be productive (in terms of publications) compared to a PhD student.
Therefore, even if you are an above-the-average applicant, the advisor would not want to allocate funds for you. This is because when your time for productivity comes, you will finish your master's, you might go to another university for PhD. Therefore, allocating funds for a PhD student is more likely.
The advisors that you have contacted with might really have insufficient funds.
However, the solution for this problem, I think, is not applying for a PhD programme. Rather, you may state that you intend to continue your PhD in the same university, in case they do not want to invest into a master's student.


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Getting research award and then ghosted?
I am being offered a research award at a university in a western europe country, and it's a quite known one as well in that country (well you can say in Europe as well). The research award is not from the university directly, it is from a research center within the university.
The award is a funding for a short research stay and I have accepted it. However, I have never heard back anything and my e-mails are not replied; it's been months since my acceptance email, and a month from my last e-mail (asking about the next step).
Is it possible that I am being ghosted? I don't mind if it's being cancelled (it's unpleasant, but what can I do, right?) at least there is a news.
Note: I don't know if I am ghosted. It is my question if it's possible that I am being ghosted? Because there is no response or news from the people that offered me this research award. Or is this a normal thing?

Hi. Welcome to Academia SE! I'm not sure what is your question exactly, could you please clarify a little?

My question is: is it possible to be ghosted after being awarded a research award?
I meant, is it normal not to get any news from them concerning the award? and no response to my emails like in months? I am not expecting a quick one day email response. Is this clearer now?

Bureaucracy in some EU countries (not all) can be quite slow and unresponsive.

You're being too passive. Call them today and get some kind of status update.

Indeed, sometime email gets blocked.  Phone, fax, send via an different email account on a different server.

You guys think so? because I dont wanna come across to be a nuisance.. I meant I know that people get lots of emails and my email can be buried among other emails. But, I did get reply before I accepted their offered.. It just frustrating that I get no news.. but, yes thanks, I will think about it.

@TerryLoring Fax ?!


text:
How often does an advisor suggest a research problem to his PhD student?
I started my second year as PhD student this Fall and I was wondering how often do PhD supervisor suggests research problem to their PhD student? Does it happen only during the initial years of PhD? If the supervisor gives the research problem, will it affect the student's progress in becoming an independent researcher?
I asked my supervisor this question once at the beginning of my PhD and he told me that it's difficult for me to come up with research problem at this stage.
I'm particularly interested in the answer to this question in the context of Pure Mathematics, but I would be happy to know the answer to this question in other fields, like computer science, engineering, biology, etc.

My advisor's strategy was to point out a few interesting papers, that might inspire a direction, but it was my responsibility to propose a thesis topic. This was pretty common, though there were exceptions where a colleague's advisor actually handed him or her the core problem.

What do you mean "suggest"? Advisors throw out ideas all the time, that's practically what they're for. Or do you mean "set research problems" as in you can't choose something else?

@AzorAhai-him- By "suggest", I mean suggest. Advisor suggesting a problem to his student, saying that this might be a good problem to work on. It's not that he's forcing the student to do that particular problem.

So, like, are you worried this prof is suggesting you more things than other people, and that might be some indicator of how skilled you are?

This varies widely, even within pure math. Some advisors will refuse to give students any very specific advice about problems to work on—for instance this was known to be the policy of Marshall Stone, and I think I recall Mandelbrot writing that in his generation it was only just becoming conceivable for a French mathematician to suggest a problem to an advisee. Noting that both these guys are from a few generations ago, it’s probably much rarer now to get almost no guidance from an advisor. Plenty of students get extremely specific guidance; in European PhDs, which are shorter than American ones, it seems to me that many students are given a problem to solve before they’re even selected to enroll. Perhaps the majority of students, at least in the US, fall somewhere in between: it’s a critical function of the advisor to stop students from pursuing unreasonable or unimportant directions, while ideally not forcing them away from their particular interests.


text:
Is it appropriate to cite a piece of academic work when the author has been charged with child pornography?
It has recently come to my attention that a renowned scholar in my field whose academic journals have been highly influential in my area of research has recently been charged with child pornography. Is it possible to separate the scholar from their work? And will my thesis research, in turn, be judged for referencing the work?

Related: How does it affect the treatment of a mathematician's results, if that mathematician was a Nazi?

Also related: How to deal with sources whose authors I don't have good relationship with?

Sounds like an ad hominem fallacy

Citation is not the same as "liking".  If you cite "Mein Kempf" then you should credit Hitler as the author - regardless of your position on the Holocaust.  You can cite Hitler in a disapproving way.

Just as "citation" is not the same as "liking," "charged" is not the same as "convicted."

@WayneConrad indeed, it is possible to even approve and "like" the convicted.  Sometimes the people are right and the law is wrong.  We are learning that the Trump administration is criminally charging people who provide food and water to migrants.  Are we to stop citing the scholar criminals among them?  To stay on topic, the multiple concepts of "citation" and "conviction" are completely uncorrelated.

I think the title of this question is poorly matched with the content. "Arrested" can have a huge range of reasons that could be anywhere from highly negative to neutral to positive regarding the author's character. The key point is not "has been arrested" but "has been [credibly?] charged with [child pornography/sexual abuse of children/???]".

Leonardo Da Vinci butchered pigs alive, and is still very famous for his influence in art.

In addition to the other points, in the US people are still entitled to be considered innocent until proven guilty.

Another related issue: If someone conducts unethical research unbeknownst to anybody, but produces positive results, what would happen to the research once discovered?

What about people who cite Adolf Hitler's book? And Theodore Kaczynski is a serial murderer serving eight consecutive life sentences, and before his criminal career began, he published a novel proof of Wedderburn's theorem (which states that all finite division rings are commutative). I don't think it would have occurred to me to avoid citing that if the occasion arose, on the grounds of its author's crimes.

I think that in almost every field you can separate the work from the person who did it. Many people in the history of science and mathematics, at least, have turned out to have "feet of clay." You aren't tainted because you use someone's work.
The only exception I can think of is if the charge of misconduct is somehow related to the research - unlikely.
The Unabomber was a prize-winning mathematician before he turned evil. His mathematical work doesn't disappear from history.

If only every scientific organization was reasonable enough to separate the work from the person... "In response to his most recent statements, which effectively reverse the written apology and retraction Dr. Watson made in 2007, the Laboratory has taken additional steps, including revoking his honorary titles of Chancellor Emeritus, Oliver R. Grace Professor Emeritus, and Honorary Trustee."

@JonathanReez That doesn't seem relevant to me. If a journal in which Watson had published decided to retract his paper(s) because of his recent statements, then it would be very topical, but the article you linked is talking about his titles, which is different.

"before he turned evil". Are you implying that being a prize-winning mathematician precludes you from being evil?

@DavidZ if the title was earned fairly, no future actions should lead to losing that title. At least not actions outside of academia.

@JonathanReez ok, that is an interesting and very irrelevant point. You could ask a separate question about that if you like ("should titles be revoked for crimes/etc?"), I'd be interested in reading about it. But titles being revoked doesn't really have anything to do with the actual research or the paper being revoked.

@Sparhawk I don't see that implication. I think buffy is saying that before acting, he was just disturbed or something. It's only when he ACTED that you would call him evil.

@JonathanReez specifically, the titles they revoked are honorary titles. Nobody revoked his doctorate, and the statement you linked explicitly acknowledges the significance of Dr Watson's previous work...

@user94036 I just found the wording slightly odd.

@Sparhawk ok, yeah, I get that. I agree. I don't really like using terms like good and evil, because I don't believe in concrete binaries like that. I would have said "before he committed his crimes" or something. I just thought I would clarify what I THOUGHT buffy meant (but I could totally be wrong).

I agree completely with guest2's answer. I'll add this: The author has only been charged. He/She hasn't been convicted.
Presumption of innocence - i.e. the idea that people are innocent until proven guilty - is one of the most sacred principles of the criminal justice system. Even if someone disapproves of you citing a child pornographer, they can't really fault you for doing so before the author is convicted.

@guest2 "Arrested" isn't "convicted" either, too.

@guest2 no, you can be arrested without being charged, let alone found guilty - see e.g. https://www.legalmomentum.org/sites/default/files/reports/answeringcrquestions.pdf question 1.

maybe you're thinking of "probable cause", which I believe in the US is a requirement both for being arrested and for being charged with a crime.

To add another perspective to this, you even knowing of this author’s (alleged) crimes is a great coincidence:

A considerable amount of criminals is never caught.
In particular for ownership of child pornography, I would expect the dark figures to be so high that I have likely cited somebody guilty of it.
In many countries, e.g., Germany, privacy laws or at least codices of press, police, etc. forbid publishing the name of a criminal for protection of their rights, allowing rehabilitation, etc.
Names are usually made public only if the person in question is in public spotlight anyway (e.g., in this case of a member of parliament) or the case itself is of extraordinary public or historic interest (such as this one, and even there, it took a while).
So, you probably only know about the crimes of the author in question because they were in a country with another attitude to privacy, were considered in the public spotlight anyway, or information leaked out somehow.
You either had to investigate this author or this was a widely available knowledge in your field, which is also something you cannot assume to happen in every case – at least I do not investigate whether there is some public criminal track record of every person I cite.

So, what makes that specific author different from authors whose crimes you never get to know?
And even if we presume that something is different, what should the scientific community do about it?
Reiterate the author’s entire work and publish it again, so it can be cited?
Is every convicted criminal’s work free game for plagiarism?
(This becomes particularly absurd in fields like pure math, where a paper can be fully self-contained.)
The only exception from all of this I can see is if the author’s research can be expected to be biased due to pedophilia.
But then it’s upon the scientific community or the respective journals to judge this and retract or annotate the respective publications.

Others calling this an "ad hominem fallacy" or saying you must "separate the scholar from their work" are wrong. In a system where citation is the currency that fuels careers, you can't separate them; any resolution of this dilemma must acknowledge and attempt to mitigate the fact that the author cited does usually benefit from citations. This is particularly true in cases where the author is still alive and still has a career, but in the case of hateful ideologies (e.g. Nazi works), there is also the aspect of citation benefiting the ideology that lives on past the author.
Now, how do you do that? It's hard, and academia doesn't have good solutions right now. Fixing the whole academic publishing system and citation economy is related, but not really something you can wait on. From an academic integrity standpoing, you do have an obligation to accurately cite work you used, were influenced by, or built on. But if this person is a "renowned scholar" and "highly influential" in his area of research, there's also a good chance that he's going to get off with a slap on the wrist, partly due to the justice system and academic community considering him "too big to fail" or considering the "value of his work" too important to jeopardize by ostracizing him.
Sadly, I don't have any good answers for how to fix this or even avoid being complicit in it. But I do think it's worth saying that the people who are telling you it's not an issue are morally bankrupt.
On a related note, the Unabomber was once cited with a footnote "Better known for other work."

Sorry, but THE ACADEMIC COMMUNITY IS NOT RESPONSIBLE FOR THE IDIOTIC CITATION-AS-CURRENCY SITUATION (if anything, we are working hard to fix it), so there is no reason why we should throw our backs out just to work around it. RTs are not endorsements; citations are not applause.

You might want to add to your answer WHY exactly people calling this „ad hominem“ are wrong, and WHY one can not (in opposite to: should not) separate scholar and work.

"But I do think it's worth saying that the people who are telling you it's not an issue are morally bankrupt."
Disagreeing with you on moral issues doesn't mean other people are morally bankrupt, and it is a bad idea to jump from a disagreement on a moral or ethical issue  to making such conclusions about a lack of morality in others.

@darijgrinberg: You don't get to decide if they're endorsements or applause; the system/the market does, and it's decided that they are.

@JoshuaZ: Telling someone who's conscience is telling them there is a real moral dilemma here that it's not real and they can just ignore it is what I call "moral bankruptcy". Whether or what you should do about it is a valid question, but dismissing the OP's concern is not.

@R..: You don't get to decide if someone is morally bankrupt either :P

@R.. First of all, none of the replies asserted that it isn't "real" - they made specific arguments about why it shouldn't be a concern. Second, if someone does have a moral concern and one doesn't think it is substantial, are you arguing that one shouldn't tell them that when they are specifically asking about what is or not appropriate?

Re "in the case of hateful ideologies", hateful to whom?  There are all sorts of ideologies I personally dislike, from communism to fundamentalist religion to sports fandom.  Should my opinion of these relieve me of the obligation to cite relevant work?

@darijgrinberg I'd love to know exactly how the academic community is not responsible for citations being highly valued within the academic community.

@ElizabethHenning: Administration and administrative pressure, as well as publishers' pushing of citation-based ratings is. You may count them into the academic community, in which case we are misreading each other's language.

@darijgrinberg So you're saying that citations play no part in your own evaluation of someone's work? Or how strong they are as a job candidate?

I have not had the occasion to evaluate job candidates, but citations of their papers would not play a significant part in my evaluation on their work. The only cases where I see them possibly be useful is when you are evaluating someone outside of your subfield; however your vote doesn't count for much in that cases (I would recluse myself from mathematically evaluating an analyst, for example). Personal qualities, as far as anything is known about them, figure separately in an evaluation; there is little chance that a proven(!) child abuser (the OP isn't great at distinguishing arrest ...

... from conviction) would get through a hiring process at a job that involves teaching anyway.

"Currency" is only useful if you're allowed to open a bank account. And no, citing Bieberbach for his work in complex analysis and citing Kneser for his development of normal surface theory do not give credence or support to their virulent anti-Semitism.

In mathematics, are we supposed to completely abandon the study of the dynamics of complex variables, because anything we do in that field would be building on the work of Ludwig Bieberbach, who was a virulently anti-semitic Nazi? Of course not. Hopefully, the academic system or the courts will punish the offending scientist, while his work remains available to future science.

@PeterShor: No, and I told readers I don't have any good answer for how to fix it. My answer is that there is a real moral issue here, one of what is morally appropriate, and that we as individuals and communities should be seeking ways to mitigate the harm of giving prestige and attention to individuals (and in some cases ideologies) who will use that to harm others and escape consequences for that harm.

@JeffE The present situation doesn't involve dead Nazis, it involves someone who has very recently been charged with a repulsive crime. And the issue isn't endorsement of child pornography, it's not wanting to treat a child pornographer as a colleague. A lot of the point of #metoostem is stop looking the other way when scumbags do "good work" and consider how much other damage they might be doing.

@ElizabethHenning I agree. I have been meaning to write an answer here including something about the differences it makes when the person is still an active academic, but I have not been able to formulate anything properly. I still agree with the general answers though that one cannot avoid citing the person, when it comes to providing an accurate account of where ideas originated. But for example the possibility of meeting this person at a conference is something that might require preparation.

I think it's worth having a conversation about whether academic citation protocols should evolve to admit citing that an idea was known from past work by others, without citing the particular work or authors, when the authors of the paper making the citation have consciously chosen not to do so. A community where you have to accept and credit people who have done and continue to do awful things is inherently toxic and exclusive of people who can't do so either because of their conscience or their own need for personal safety (including against reliving similar trauma) from such people.

@ElizabethHenning The answer explicitly referenced Nazis.  And of course we don't want child pornographers as colleagues; that's why we fire them and throw them in prison. (Hence my comment about bank accounts.) Not citing their work is not just an inappropriate response because it distorts history, but because it's insufficient.


text:
Duplicate submission issue since first journal never responded to submission or withdrawal
I submitted my research article to journal A earlier. They neither responded to my submission nor sent me any confirmation of my submission. I mailed them for the withdrawal of our submission. They did not respond to that email either. By that time I decided to submit my research to another journal B, which  published my paper. The problem arose as journal A sent me an email regarding the acceptance of my submission.
What should I do now to overcome this situation? Will it be considered as duplicate submission?

Remind them of the previous communications and insist that you already withdrew the paper. Provide copies if possible, with dates.
No, it isn't a duplicate publication, but make sure they understand that their "acceptance" is moot. It isn't required that your withdrawal be acknowledged for it to take effect since the rights to the paper are/were still yours.
I hope it isn't a case of your emails being sent to the wrong address and lost somehow. It would take some effort to clear that up. They might have been acting in good faith in that case.

Thanks for your suggestion. I later found my mail was blocked in first case (The email to journal). Then I have mailed their publisher as well (No response from them either). Probably they changed their previous email address (which is the case I assume).

I think the default assumption should be that the first journal is acting in good faith. It certainly sounds like a lot of time passed—a predatory journal would presumably have acted faster (and with a request for money).

@GregMartin, In this case, should I offer to submit a revised and developed version of my research work (I am currently working on it), and let the editor know about the issue? Will there be any consequences for this issue? I am a little confused about what should I do now.

@GregMartin On the other hand, I find it very unusual that a non-predatory journal accepts a first submission without any changes required from the reviews. At least in my field.


text:
What are the benefits of becoming an academic administrator?
Why do some professors choose to go into academic administration (e.g., become associate deans, etc.)? I've always assumed the main attraction is that they get paid more.
But I've also thought that if I wanted to make more money than I do as a professor, I'd just leave academia entirely and take a job in industry, where I could probably earn 30-40 percent more. I'd have less flexibility in industry than I have as a professor in terms of what I research and what my work hours are. But the same would be true if I went into administration at a university (which would probably still not pay as well as industry).
So I've never seen a real benefit of going into administration, given that the extra pay does not (to me) offset the extra responsibilities (especially when the best-paying jobs are outside of academia entirely). Are there other benefits of being an academic administrator that I'm overlooking?

Related: What is the point of being the head of the department?

Well, there isn't really an "industry" for professors in many fields to make big bucks.

One reason -- they become an administrator because they failed in research and teaching.  In other words, administrators are failed academics.

@Prof.SantaClaus So it's the Dilbert Principle - incompetent people get promoted to management, because that's where they do the least harm.

@SimonB or because both skillsets are required to some degree at the entry level, and those with a relative advantage in the skills that correspond to administration naturally gravitate there.

@SimonB Yes and no.   They could bring a whole organization to its knees, especially when they don't really understand why they failed in teaching or/and research in the first place.  On the upside, I have met a few administrators who are good managers but sucks at being a teacher or researcher.

I would just note that it isn't always a position that they choose.  In the math department where I went to university the department head was a rotating position through all permanent members of the department.  When it was your turn you were just stuck with it for a year, I don't think anyone particularly wanted to do it.

I feel like I'm pointing out the glaringly obvious, but many people actually want to make their institution a better place, design policies that benefit future students and colleagues, etc.

Perhaps some also do it for the prestige and power that come with such a role. Or they prefer the managerial rather than research side of their job.

Yeah, just about everyone would like to see their institution become a better place. But like the original poster, I, too, find it curious that someone would spend so many years as an undergraduate and then a graduate student and then a faculty member becoming an expert in an academic field that they love only to give that all up and become an administrator. If their life goal was just to become an administrator that's a pretty roundabout path. Think that there must be more involved here (e.g., changing priorities as one ages, or a sense that one has reached one's limit in their field, etc.)

@SamuelWeir also job security, burn out from research & the grant cycle.

But sadly for many institutions, "wanting to make it a better place" and "having the right leadership skills to succeed in making it a better place" are two different things.

@SamuelWeir I don't see how its "giving it all up" at all. The President at a former institution continued to teach and had several postdocs. One of the Vice Chancellors was a well-known scientist and I actively collaborated with him and his research group. In his case, the administrative position replaced teaching duties but allowed time for research. Additionally, a lot of endowed positions come with discretionary funds which replace some grant money. And when they leave the position, they usually revert to their professorship, so they are motivated to keep research going.

@user71659 - I think that it would be very, very rare for a President or Chancellor at a medium to large university to both lead a university and also continue teaching and/or research as well as supervise postdocs. Being a President or Chancellor of such a university is a full-time job.

@SamuelWeir This guy did it, and it was a top-10 school. There's some strategies: teach a journal club type class where you just show up, or teach the same large service class where you can do the material on autopilot and tests and grading are handled for you. Postdocs don't take much time in themselves, it's proposals that takes most PI time, and as I said, there's unrestricted cash to substitute, and then you can split them with another PI. It's actually easier than those professors who run a company on the side, since you aren't expected to do 80% on the academic side.

@user71659 "This guy did it, and it was a top-10 school." - Well, tell me the name of the university and the name of this President or Chancellor, then. There's no reason to keep the information discreet or confidential, is there? He or she wasn't hiding the fact that he or she was running a research group and supervising postdocs while also being President of the university, right?

@SamuelWeir I'd like to maintain privacy, but simple web searching found that  Columbia teaches, USC, OU, UVA, Georgetown

A benefit not mentioned in the other answers is a reduced teaching load in many universities. Possibly zero teaching load. If someone does not like teaching, but likes (or has a lesser dislike of) administration and research, then the reduced teaching is a motive to become an administrator.

Oftentimes, these administrative positions are only temporary and after a while, professors having such positions will become regular professors again.
This means that they can (1) work on improving their department, (2) perhaps get a higher salary for some time, (3) get reduced teaching load, and (4) help to grow their professional network within the university, all while having the assurance that the job that they actually like to do (Professor) for will be waiting for them when they don't want the higher responsibilities any more (or are not re-elected to these positions).
This is something that you can't get by switching to a job outside of academia -- once you are out of academia for a few years, it's quite difficult to get a good academic position as your scientific output is normally lower (or insignificant) when working for a company (perhaps with a few exceptions). Hence, you won't be such an attractive candidate (again, with exceptions).

I've also seen them be mandatory/strongly encouraged with each professor required to serve a term in a departmental administrative position.

In our university, you get to keep the higher salary, or at least a big chunk of it, when you go back to being a regular professor.

One factor I don't see mentioned in other answers: pensions. In one school I'm familiar with, your pension is 75% of your salary averaged over your five best years. So if you are department chair or dean near the end of your career (as is usually the case), you get the ~40% bump not only for those 3-5 years, but effectively for the rest of your life. Particularly since the increased administrative load may be partially offset by a reduced teaching load, this is not a bad deal.

This is, I presume, highly country-specific. Not the case in Finland or Denmark, for example.

True for Australia AFAIK

Echoing the other answer: some people do see the opportunity to better their university. Also, in my experience, some people decide that, even though they did well-enough in research-and-teaching to get tenure, etc., that this is not their genuine strength. So, having considerable understanding of both research and teaching, they are uniquely qualified to try to administer things related to that. We rarely hire "professional managers" in academe, because we do not trust that they know what is going on here.

Why do people want to be professor? They do less direct research (compared to post-docs or full time researchers) but instead they let other people execute their ideas. If you become head of department or a whole university, you can "steer" a lot more people but you become more indirect.
It is similar to why people climb the hierarchical ladder in industry. They loose contact with the real work but their thinking influences more people.

@user2357: By full-time researcher I mean someone without a team below. These positions exists, e.g., in France or Germany in goverment -funded research labs which are outside of Universities. Examples are Inria or Frauenhofer. Even in your sense, professors are more indirect regarding research.

A few more aspects of motivation I have not yet heard mentioned are:

Salary. In many universities, senior administrative positions offer a salary increase over the (often uniform) salary grade of a professor. For most people this is a secondary motivation, and Professors' salaries are not that low to begin with, but it does carry some weight.
Prestige. It makes many people feel more accomplished to have reached higher positions or ranks; to be more in the public eye; to speak for their departments or universities in various circumstances. Some might consider this vanity (and there are certainly vain people in such positions at times), but social recognition does motivate most people at least to some extent.
Getting away from research. Paradoxical, isn't it? Well, some Professors are not as motivated as they used to be. Or - dislike the current fashions in their field. Or have just evolved as people and want to try something different in life, without quitting their job. An administrative position is a way of doing that instead of focusing mostly on your research.

Having said that - I agree with @RaghuParthasarathy's answer, that the primary motivation is hopefully the wish to actually do right by your institute or department.

Here at the college I work for, our state retirement system (FRS, florida) administrative positions above a certain level accrue retirement pay at 3% per year vs. 2% per year for us peons.  Additionally, retirement pay is based on the average of the highest 3 years of earnings from any FRS employer.  Rather common for admin types to teach a few classes as adjunct or run for city/county political positions, or to teach overloads, etc. all as a way to crank that retirement paycheck up as high as you can get it.
Our administration also pull faculty away from the classroom (we aren't a research institution) to work on special projects like our SACS accreditation, etc. though this is usually time limited (1-5 years) with a promise of returning to the classroom and no loss of tenure, etc.
So from here at a "junior college" type place, it is about that retirement pay.

The difference between "administration" and "teaching" in a university is like the difference between the managerial and technical tracks in a corporation.
The "average" university person probably wants to remain on the "technical" or "professor" track, but some people will prefer administrative duties, and a chance to exert executive power, either inside or outside the university.
As a child, I didn't want to be a movie star, but I did want to be a "producer," which would have made me their boss. That was true even though I knew that the stars made more money.

In systems where internal appointment of academic management still exists some staff members who are collegial or union will put themselves forward in order to prevent the filling of the role by an unknown external, an uncollegial peer, a non-union worker, or a yellow dog or scab.
To the extent that the academic management role has some freedom of action they may be able to protect their unit from higher levels of management for a period of time.
This perspective assumes a higher level of proletarianisation, class conflict and class consciousness amongst academics in a unit; but, I have seen the case in the Australian system. Sometime everyone else steps backwards faster when colleagues ask for a volunteer.