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Should a reviewer critique language and text copy-paste issues?
While reviewing research papers, I often find that the author's English is not very good. Considering that English is not everyone's native language, I understand their difficulties, and I recognize that they struggle (using dictionaries and translators) to get their work published at all.
That being said, I sometimes find papers that have contain English that is frankly terrible, with a few select fragments in pristine, almost Shakespearean English, using words that sometimes I didn't even know existed. When this happens, a quick Google search will occasionally reveal that these fragments are copy-pasted fragments from textbooks.
What should I do in these cases? I feel that being too harsh might come of as being mean, but I would really like to emphasize that the practice is very bad for the scientific community.

This reminds me for asking whether native English scientists have an advantage over non-English ones.

@seteropere I think in general there is no problem with not being a native speaker when the student reaches a certein level. A good set of brains is much harder to acquire than polishing the language of a paper.

At my university, we call copy-past issues "plagiarism"!

As Mikael said, copying and pasting from a textbook (or any other source), unless it's properly quoted and cited, is plagiarism. Now, it's possible that this varies a bit between fields and between cultures, but generally speaking, plagiarism is one of the most serious academic offenses, probably a step below outright fabrication of results. Well-respected tenured professors at major universities have lost their jobs and had their careers destroyed by a single instance of plagiarism. The point is, this is a very big no-no and you should treat it accordingly. You should definitely notify the editor, and see how they would like you to deal with it. I don't know exactly how that process works, whether the editor will just handle the paper themselves and let you know that your review will no longer be necessary, or whether they'll ask you to finish your review anyway and state your objections in it.
In the latter case, I would write an unequivocal recommendation against publishing the paper because of the plagiarism. Personally, I would be inclined not to even look at the scientific content of any such paper, although that may not work out in practice. But anyway, just pointing out the plagiarized parts and recommending against publication, in and of itself, is not mean. Just don't get carried away and start attacking the author. You could apply the same principle that is used at Stack Exchange, namely that it's about the behavior, not the person.

If you put aside the plagiarism, there is also the issue of sloppy English that you mentioned. In my experience, (nearly?) all reputable journals require papers to use proper spelling and grammar for standard English, or something reasonably close to it. Usually, the instructions for authors will advise non-native speakers of English to get a native speaker to check the paper for grammatical errors before submission. So it's reasonable for you to point out any such errors that you come across. Now, grammatical errors don't have to condemn a paper to oblivion the way plagiarism might, but if the grammar is bad enough, I would think it reasonable to recommend against publication in its current state. If the underlying ideas are sound, and otherwise qualified for publication, then you could recommend that the authors edit the paper to improve the grammar and resubmit it.

I consider the demand to get a native speaker to check the paper for grammatical errors before submission unreasonable. I am no English native speaker, and who might I ask such a thing ? The two native English speaker in my department would be overwhelmed if we all asked this.

(continued) For expensive commercial journals, the proper way to go would be for the journal to offer help with language during the copyediting. Up to some point, native English speakers should accept some sloppy language in exchange for the research being written in their language. The only way I can see to solve completely the language problem would be for everyone to write in her language, and have a huge amount of translators to exchange between languages. This seems not reasonable either.

With a little effort you can find plenty of people who could clean up your English. You might have to pay them but it would be the same for me if I wrote in French. What kind of image do you want to present of yourself?

I suppose it doesn't have to be literally a native speaker, but someone with a sufficient level of English competency to proofread the paper. "Native speaker" in my answer should be taken to refer to the  person's reading and writing ability and not necessarily whether they actually have been using English from birth.

This is plagiarism. You should at the VERY least point this out to the editor and suggest (s)he insist the authors get language, translation or copyediting assistance if they can't write their paper themselves.

In the couple of cases that I have found plagiarism (and that is what has happened here). I always contact the editor directly telling him/her what I have found and that I no longer feel comfortable writing a review. This then puts the burden on the editor to make a decision of how to deal with it.

I understand exactly what you are going through. I have seen it happen many times before. In some culture it is extremely common for students to think that it is acceptable to plagiarize in this way. However, in my experience, even when students think it is acceptable, they still know it is wrong.
You should call it out for what it is: Plagiarism. In my experience, some students only wake up to the impact of plagiarism when they are shocked into it. I've had students call me mean (and much, much worse) but I've never had a student say that I was unjustified in calling their plagiarism out. They knew what they were doing and they got caught.
I also explained to them how to "right their ship" so this did not cause future problems. In my school, the punishment is quite lenient for plagiarism - just fail (no removal, no academic probation, etc.). But, it is still expected that it is called out when it is found.

I would see two situations here:

The English is so bad that you cannot properly review the paper. If you suspect that there is merit in the paper, suggest getting the language polished and encourage to resubmit. Otherwise reject the paper.
You can follow the story, and provide an in depth review. Just reply as you would normally, and mention polishing the language as one of the additional remarks.

Just copying without reference is just plagiarism, and should lead to rejection of the paper.


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Am I reading enough of the scientific literature? Should I read for breadth or depth?
Ever since starting graduate school I've tried to make scientific reading a part of my daily ritual; I track pages read using Beeminder, and the graph doesn't lie.  It keeps me honest.
I aim to closely read and summarize 5 pages per day and skim a few other abstracts besides that.  I spend about half my time looking at data and figures which doesn't contribute to my daily "page count."  When I'm reading about a new topic these five pages can take several hours, but on topics I have more background in five pages might only take an hour per day.
I guess since everybody defines "read" in a different way it's hard to get an objective answer about how much reading is enough.  How much people read seems like a bit of a sensitive topic among real-life colleagues because everyone has a bit of anxiety that they aren't reading enough.  But for those further along in their academic path, I'd like to hear how you approached the literature early in your graduate school career and what you think is a sufficient amount.
I guess this all distills down into two main topics:
When deciding what to read each day, should I focus on depth or breadth?
Is five pages of close reading per day enough?  I know it doesn't sound like much, but it takes significant mental energy to meet that goal.  And consistently reading 5 pages per day adds up to a lot over time.
Edited to add: I mostly read about petrology, volcanology, structural geology, and tectonics if that makes a difference.  By "page" I mean "page of text" so if I'm reading a structural geology paper with lots of maps and figures I discount for those and a "ten page" paper becomes a 5 page paper for my purposes.

I have a blog post about how to sift the literature that you may find relevant: http://scienceinthesands.blogspot.com/2011/10/searching-scientific-literature.html

+1 for "consistently reading 5 pages per day adds up to a lot over time."  To quote Benjamin Disraeli: "The secret of success is constancy of purpose."

I'm doing this as well for the past few months. In addition, I'm keeping a track of what I read on my website and on citeulike, so that I can participate in discussions with the knowledge gained and cite papers when required to do so.

My experience is almost exclusively with mathematics papers, and applies little or not at all to other fields.
Much of eykanal's post applies to math as well, but one big difference is that math papers are much more varied in their structure, not having an actual experiment to tie them together.  A good paper will generally explain its organization in the introduction, however.
One point worth emphasizing is that reading a paper from front to back, trying to understand everything at each step, is usually inefficient.  The most common instance is that a paper often starts with definitions which may be hard to make sense of without understanding the theorems they're used in.  It's generally more effective to skim the paper several times, trying to understand more and more with each pass.
Relatedly, you'll eventually pick up the skill of picking out the most interesting ideas from a paper without reading the whole thing.  Early on, though, it's probably better to read things carefully; it's very easy to fool yourself into thinking you've understood something.
As to your main question, about breadth versus depth, your first priority has to be depth, because that's what you'll ultimately need to be able to do your own research and get your degree.  But if you're learning enough to do that, you want as much breadth as possible.  It actually gets harder and harder to learn completely new things as you get on in your career, even when there may be direct benefits to your research to do doing so.  Laying the foundations of a broad understanding of your field while in graduate school will pay off later.

"...reading a paper from front to back, trying to understand everything at each step, is usually inefficient." +1 for that sentence alone! It took me years to realize that they author didn't write that paper so I could solve my problem, they wrote it to tell me how they solved their problem. Find the part that's relevant to your problem.

+1 for "as much breadth as possible." Especially in sciences, knowing more about the instrumentation (e.g. radar) and underlying physics (e.g. viscous fluids) can greatly inform your research.  This can set you aside by giving you better insight into expected behavior and disparities between actual and expected observations.

This post refers to research in the STEM fields, and may not be applicable to other research topics.
One of my biggest epiphanies in research came when I learned how to read a paper. Reading scientific publications is completely different from reading literature or news. At the beginning of your research career, you can expect to spend a full day (if not more) reading through a single 8-page paper. Some tips follow:

Most papers are divided into "Intro", "Methods", "Results", "Discussion". These are roughly broken down as follows:

Intro - Read this for background. There will be nothing "new" in this section. You will find it very useful to read the intro section to as many papers as you can get your hands on. While you do this, you will become fairly depressed that so much research has already been done, and you will wonder what you can do to add to the field. Speak with your advisor, he has many good ideas.
Methods - This will take you a VERY long time to read initially, because they go into ridiculous detail. They do this so that you, the researcher reading and interested in replicating their results, can do so. If you don't understand everything here at first, don't worry.  NOTE: If you finish reading the methods section and still want to know how something worked, email the author! This is research; the guy who wrote the paper is likely another grad student/postdoc like you. He'd love to hear from you.
Results - This is the meat of the paper. Read this very carefully to find out what they found. Between this section and the methods section you will determine what went right, what went wrong, what is new, and what they should have done that they didn't that you can now research and publish and become a superstar.
When you cite a paper, you will be citing from this section. If you find yourself citing a paper based on something in the Intro, you're just citing another citation.
Discussion - This is the author's thoughts on what the results mean. Take note of this; the author is using his or her expertise to interpret the results. If you disagree with something he or she says here, and you can back up your findings, more power to you.

Most accomplished researchers don't actually read papers; they just read figures. A good paper will be completely in the figures. (This is particularly true in some biological sciences fields, less so elsewhere.)
Take notes on the papers you read. Keep those notes. My method was to keep my notes in a 3-ring binder, put a little post-it tab with the author's name, and then put the paper in there as well with the notes, so each "tab" is my notes and the paper. You will read hundreds of papers during your academic career. You will want to remember what you've read.
This is a very arduous process, and the learning curve is steep. Don't be discouraged! Reading papers is a skill, and the more you read the more proficient at understanding them you'll become.

Note that this doesn't apply to all the STEM fields.  Mathematics papers, for instance, re structured quite differently.

Good point. Could you post an answer about how to best read a math paper? I'd be interested in your answer.

Excellent post. To avoid the feeling that “everything has already been done” it can oftentimes be good to first identify your problem, to think about it, see what you can formulate yourself and then when you’ve made some progress with it, then go to the literature and see what other people have said. You may well find that you’ve done something different from what they’ve done.

You forgot the References section! It's the original Wikipedia.  Combined with good skimming, you can get a good understanding of the hows and whys of the Methods section more efficiently this way than by thorough reading alone.  And always read the rest of the papers from the group or person on that topic!  There is often a bigger story to be told through their cumulative successes and failures approaching the problem than through a report on any one of them alone.

This is about my experience in computer engineering
I found that reading for breadth was the more important approach. The area of research I was interested in was pretty fluffly and ill-defined (I thought I could make a difference by organizing it better), so that many relevant articles were categorized in totally different areas. This meant I had to have a hummingbird approach: flittering around, but drilling down when I found an important vein of data. I also kept a journal where I'd put a citation and a very brief summary of the article, so that I could come back and say "I think I read something about this last September" and then go look in an older journal. Today, I'd have my own wiki at home to keep track of this. I used to have my own "library" of PDFs that I got through university access, but that removable hard drive was stolen.

Is five pages of close reading per day enough?

If you can stay consistently at 5 pages (or 1 article) every day, you will end up far ahead of other people who study only in spurts.

At the beginning read anything and everything (and take notes). You should always be reading something and writing something. The hardest part for most students in the sciences to get past is embracing the unknown.
You will probably feel the need to understand everything, right from the start. Unless you are exceptional, you probably will have to read the important papers several times. You are looking to develop a broad-scale understanding of your field. To know where your research fits in, you have to develop an understanding of where your field in. This takes time.
As time goes on, you'll pick and choose more carefully the papers you read closely. Often you can get the idea from just the abstract. If it sounds promising, then read on.

I'll chime in with a quite different opinion (or maybe a related opinion phrased in a different way), which grew out of advising/supervising PhD students and post-docs: reading for depth is a job requirement, but reading for breadth is what will make you stand out.
As a PhD student, you are required to read in depth the papers that directly pertain to your particular subfield. A PhD is the process of becoming an expert in your discipline, and you cannot do that without mastering the minute details of it, which you will only learn by reading in depth the papers published (and attending conferences, asking questions, etc.).
However, though becoming an expert is what gets you your PhD, if you want to continue further in research (whether academic or R&D), you will need to be able to show a quick understanding of new problems, to make connections between concepts in various areas of research, and propose creative solutions to the problems you have identified. This requires a casual knowledge of a large variety of fields, which will be only acquired by reading a large breadth of topics.

The best ever comment about "breadth vs depth" I have heard!

I agree with Henry about breadth vs. depth.  You'll ultimately be judged on depth, so that has to be your first priority.  However, breadth is quite valuable too.  Many breakthroughs have come by applying standard techniques from one area to a new area.
The $.02 I want to add is that not all reading is created equal.  Particularly when you're learning a new topic, well-written exposition is invaluable (in large part because it's so rare).  As you progress, you'll develop a better intuition for what's worth reading.  But when you're early in your career, I strongly encourage you to ask your adviser (or more senior students) which papers and books you should be reading.  Personally, I've slogged through many manuscripts mired in myopia before encountering enlightened, engaging exposition.  ...and that has made all the difference.

+1 for "slogging through manuscripts mired in myopia". Beginners often tend to think that when they don't understand, it must that they just aren't smart  enough, when the reality is that poorly written papers are hard to read no matter how smart you are!

I personally think one should read both for breadth and depth. Read all sort of literature around your field, and a few outside your field. One can only come up with good ideas having a good general knowledge of science. When it comes to your own research area papers should be read carefully and critically to understand what is being done, how it is being done and if the interpretations and methods made and used really show that. As my PhD supervisor used to say, read atleast two papers a day even if you are busy with experiments. This gets easier as you go. For me in the first year it used to take a lot to time to read a paper. Towards the end, I was looking at the abstract, results/figures...if needed methods, and where confused check the discussion quickly to see how the authors explained their results. After a while, you rarely need to read the intro in your own field unless you want a refresher.

I have some points to mention on this:
1. Read papers relevant to your research:
I mean, you will know slowly which conferences publish results in your interest area and which do you find relevant, so choose papers from top conferences or journals because those set the benchmarks
2. Datasets in paper convey a lot
Browse in the result section of the papers and you will notice that the datasets on which the algorithm are tested should suit your requirement. For example: I am working on outdoor dataset and I see a paper showing results from indoor dataset, there are 99% chances of me dropping that paper.
3. Use abstract as filter
I think this point is self explanatory
4. Scheduling your reading - difficult
There may be a week where you will end up reading a lot of papers and there will be times where reading even one paper will not be possible.

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How to make a concise dissertation defence in Math department
I am a math student in the US preparing for an hour long defense of my thesis. I am assuming that the dissertation committee has already read my dissertation by the time of my defense.

Any advice for preparing and giving the dissertation defense talk?
What balance between presenting subtle parts of the proofs and a clear big picture of the results obtained should I aim for?
Would it be more interesting for the committee to hear me explaining more technical parts of the proofs rather then the statements of the main theorems obtained?

What did your advisor suggest?  (You did ask your advisor, didn't you?)

I don't think there is a meaningful answer to this question that is helpful across all US math departments and advisors.  There is too much variation in what is expected.  You need to find out what your committee expects.
That said, if I had to answer without knowing your department or committee, I would recommend trying to focus on the big picture but making sure that you spend most of your time explaining your contribution to that big picture.  That's certainly what you'll want to do for job talks, which you're presumably also giving around now.
If the committee is concerned about your understanding of the technical parts of the proofs, they can ask you in the closed exam following the public talk.

Two general suggestions which can overrule anything else I'm going to say: Ask your advisor about the talk! Practice your presentation with your advisor, if that's alright with them.
In general, you should not give details of proofs unless pressed; even then, you should be careful. Proving real theorems in talks (even thesis defenses) is hard, time consuming, and generally of minimal benefit to the audience. You should instead try to give the general picture of what you've done.
Some other good resources for communicating math are:
http://www.ams.org/notices/200709/tx070901136p.pdf
http://www.ams.org/profession/leaders/workshops/gcoll.pdf
Neither are specifically about the thesis defense, but are general resources for math talks. I've found them both helpful at various points.

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What do I do if there is an error in a figure in my not-yet-defended thesis?
I submitted my thesis some days ago and I am currently preparing for viva. I have found an error in a figure which is a summary of four other figures. In fact, by means of Excel I did calculations and made a mistake regarding some numbers. I have prepared a new file with  corrections but I confess that I am scared.
Anyone has experienced such a situation or may give any tip about what to do.
Thank you very much indeed

Contact your advisor immediately. Include the corrections.

If the discussion in the viva turns to the error, be honest about it.  If it turns to that figure, I would raise it proactively.  I'd also have a corrected copy with me if possible.

In the U.S. (and in engineering) it is common for the thesis committee to ask you to make certain corrections to your thesis before it is published (this usually happens at the end of your defence meeting). An error of the sort you mentioned should not matter at all, unless it has a significant impact on the fundamental contributions of your thesis.

Speak to your mentor. However, I see no reason to panic or to be scared, if the mistake doesn't influence some fundamental contribution of the thesis. I've found numerous cases where mistakes in the thesis were found years after the defense, even with considerable impact (one extreme case de facto made ~60% of the thesis obsolete aka wrong).
If the thesis is completely printed/prepared/submitted, I guess that the mentor won't insist on repeating the process.
Again, this is not a great issue, if the error is not of fundamental nature (e.g you base your thesis that the actual value of pi is 4, but somehow you miraculously discover now that you were of by 0.8584).

I sent corrections to my mentor and I am waiting for his advice. It influences two numbers (mean values of four other values) the figure infuences also some wording in the text explaiing the figure but not some fundamental contribution of the thesis

Don't keep it a secret, but don't walk in and start apologising for it either.
If the discussion in the viva turns to the error, be honest about it.  If it turns to that figure, I would raise it proactively.  I'd also have a corrected copy with me if possible.  There's no such thing as a perfect thesis (including when the final copy goes to the library) so make use of the opportunity to fix everything you can.  I found nonsense (but not as bad as misleading) wording in my readthrough the night before the viva, as well as typos, none changing the outcome but some in equations.   I brought a list and mentioned it at the end of the viva, and they were accepted as needing fixing with the corrections.

Thank you very much guys. It is encouraging. I will take into account what you wrote all of you. My mentor said bear it in mind if you are asked say you identified the mistake.

In fact, before finding these issues, I decided to bring a file of corrections with me to the viva. Thank you guys again


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How can I find journals in my field that do not charge author fees?
I am in search of an algorithms journal that does not ask for publication fee.
It doesn't matter to me whether it is an open-access journal or not.
I have been searching for journals with no publication charges journal for a while, but have been unable to find any such journals.
Are there any resources that can help me locate journals that do not charge authors publication fees?

Could you specify if your algorithm paper is applied to a particular discipline e.g. molecular biology  or is it pure theory / a mathematically-focused algorithm paper?

What do you mean? Most journals (algorithms included) have no publication fee if your article will only be available through subscription. E.g., look on ACM, Springer journals and you will find many.

@Alexandros not such a silly question IMO. Many subscription access journals still levy 'page charges' to authors, particularly for long papers e.g. IEEE fees http://www.ieee.org/advertisement/2012vpcopc.pdf

@rmounce I never used the term silly. I do not know about IEEE but ACM and Springer journal I know of are free.

@Alexandros this 2013 survey seems to indicate that 55% of Springer journals levy 'colour' charges for colour figures: http://www.researchinfonet.org/wp-content/uploads/2013/09/Wellcome-survey-of-colour-and-page-charges-v-02.pdf ACM not included in that survey

@DUDE_MXP: I have substantially edited the title and content of the question, because in its original form, it is asking for a list of journals, which is considered an off-topic "shopping" question. Asking for how to find no-fee journals in general, however, would be on topic.

Can you please specify field more precisely?  Most computer science journals do not charge fees, so I am guessing you mean some other field?

My algorithm is a bit graph theory based.To be precise its based on maze generation algorithm.

I don't know what subject maze generation falls under, but most graph theory journals I know do not charge author fees.

Legitimate algorithms journals do not charge publication fees. Those IEEE page charges are voluntary, which in this context is a synonym for fictional. (Some legit journals do charge fees for open access, but that's not the same thing.)

You can use websites like DOAJ.org to search for relevant open access journals that do not charge publication fees.
This example query for 'algorithms' reveals 40 such cost-free open access journals, some of which may be relevant to you, depending on what exact type of algorithm you're publishing.
Using this I found The Electronic Journal of Combinatorics & Image Processing Online (IPOL Journal) which both look like reputable, high-quality, publication-fee-free, algorithm-relevant journals to me.
Some of the information is a little out of date though, for instance the journal Algorithms (MDPI) I think now charges 300 CHF (Swiss Francs) although initially it was free, in the past.

Many MDPI journals were no-fee for their first couple of years and have since begun charging - I think this is now standard practice for them.

It's standard practice for most new open access journals - it makes sense IMO: new journals by definition don't have a journal impact factor (you need at least 2 years of citation data), so to some authors it is more of a 'risk' to publish there, so they need to do something to entice authors in the first couple of years before they get an impact factor.

As far as I can tell, MDPI Algorithms is a write-only journal.

Harsh. Google Scholar seems to think differently: https://scholar.google.com/scholar?q=Delaunay%20Meshing%20of%20Piecewise%20Smooth%20Complexes%20without%20Expensive%20Predicates (although, perhaps all the 29 citations didn't read the paper?) ;)

Thanks the DOAJ link helped me and I found a free journal for my paper.


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What is the rationale for having references before the article text?
Certain journals (for example, Optics Express) place the references of papers in between the abstract and the article text:

How common is this practice? Is there some specific design reason for this? As a casual reader, I find it disconcerting and annoying, and tend to see it as another barrier between googling the paper and actually getting at the text. However, I understand there may be other workflows for which this is useful. Can anyone point out possible justifications for this practice?

Maybe the example given is not the best one: since it's an erratum and it quotes only the article needing amendment, it might look like an exception and not a rule for the journal (while looking at the journal one indeed finds articles with a long list of references right after the abstract).

I have found a better example; please see the updated question.

Usually the article consists of three or four parts. Introduction, main text which is your original research and conclusion. There may also be an appendix.
So, usually most of the citations are from the introduction because you mainly talk about the prevosly done researches there. So, when I read the introduction I have to constantly go back and forth to check the citations, but if the citations are located nearby the introduction, it is more convenient.
I read sceintific articles a lot and I can say that this is not a common practice in physics. I can't talk about the articles in other fields.

especially considering the article uses the "number system/stye" for citations. It's hard to keep track or who author "13" or "23", etc. is.

@SoilSciGuy That is standard practice in large stretches os physics (and definitely in atomic, molecular and optical physics), and references are almost uniformly at the end (which is not to say that there isn't typically a lot of shuffling over between introduction and references).

It's standard practice in a lot of journals, that doesn't mean it's the best practice.


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Are there any papers where two (or more) authors share the same full name?
Because of reasons, I'm curious to know whether there are any simple examples of papers where two or more authors have the same full name.
(To be clear: I am only interested in exact full-name matches. Instances where authors share a last name are so common as to be completely unremarkable, and instances where the first name matches but additional initials distinguish the authors are also reasonably common and not what I'm looking for.)
Does anybody have any particular pointers that can be useful?

A bit in jest, but see Another article that makes bibliometric analysis a bit harder in the 2015 SIGBOVIK proceedings.

I would imagine there's a parent-child pair out there with the same name that have collaborated.

I'm on a paper with two other Peter Green's, however we do have different middle names and we put our middle initials on the paper.

an obvious situation where this could happen is a wife and husband team where the wife has taken the name of the husband, and both spouses share a the same first name, for instance Mrs Michele Spaghetti (from a country where Michele is feminine) married to Mr. Michele Spaghetti (of Italian ancestry).

@PeterGreen Which one is you though

Here's an example with co-first authors who share the same name:

Fan Zhang, Fan Zhang, Liyu Huang, Dan Zeng, Casiana Vera Cruz, Zhikang Li & Yongli Zhou "Comparative proteomic analysis reveals novel insights into the interaction between rice and Xanthomonas oryzae pv. oryzae." BMC Plant Biology 20, 563 (2020).

From physics I can only think of papers where two authors share initials and last name, but don't have the full name written out. An example, though perhaps not so simple, is this one from the ATLAS collaboration. Its author list includes two instances each of "A. Gabrielli", "B. Li", "M. Liu", "B. Martin", "C. Meyer", "J. Meyer", "T. Mueller", "C. Schmitt", "S. Tanaka", "P. D. Thompson", "C. Wang", "H. Wang", "J. Wang", "H. Yang", "J. Yu", and "S. Zimmermann".

Looks like the journal website can't handle that example either. For all authors clicking on the name shows the affiliation, except for the second Fan Zhang.

"Fan Zhang and Fan Zhang contributed equally to this work." That's ironic

Well, at least the handling of the citation_author meta tags on the html source looks more rational than in the example linked in the question. But the problem with the affiliation popup is really quite something.

Isn't this why Knuth tries to credit authors in their native script? A lot of information is lost in pinyin.

Q: Is there a paper where two authors have the same name? A: Here is a paper with 16 pairs of authors having the same names.

@Stef The ATLAS paper is kinda cheating but it gives us a nice estimation of how frequently we expect it to occur, both in names written in English and those with a native Latin spelling.

Interestingly, it is possible that Fan Zhang and Fan Zhang don't, in fact, share the same name. That is, the tones (not represented in the paper author list) may not be the same (and tones are as important as vowels in Chinese). Furthermore, even if the tones were also the same, the characters may also be different. An interesting situation, nonetheless!

Why does the ATLAS paper abstract quote the amount of data in inverse femtobytes???

@user253751 That is rather off-topic, but the unit used in the paper is inverse femtobarn.

@2ndQuantized Yes this is the annoying thing with being a Chinese academic, they may actually have different names but they look the same when rendered into the Latin alphabet.

A recent example:
Otto , P., & Otto , P. (2022). Impact of Academic Authorship Characteristics on Article Citations. REVSTAT-Statistical Journal, 20(4), 427–447. https://doi.org/10.57805/revstat.v20i4.382
which includes the following footnote:

To the best of our knowledge, this is the first published article where both authors share their surname and given name, while working at the same university. Thus, the two authors are largely indistinguishable, which highlights the importance of individual author identifiers like ORCID.

ORCID always makes me laugh.  "What's your orc id?" "Me Grimtooth!"

They do not share the full name, only the last name:
Michalis Faloutsos, Petros Faloutsos, Christos Faloutsos: On power-law relationships of the Internet topology ACM SIGCOMM Computer Communication Review, Volume 29, Issue 4O,ct. 1999, pp 251–262
What is also amusing, is that the "Faloutsos" last name is extremely rare in Greece. According to this they are siblings.

This was pretty clear from the original phrasing of the question, but I've made it as explicit as I can in an edit. I am not interested in anything other than full-name matching.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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What is a fair metric for assessing the citation impact of journals across disciplines?
To quote Thomsons "a journal's Impact factor
is calculated by dividing the number of current year citations to the source items published in that journal during the previous two years."
I assume that disciplines vary in

average number of citations per paper: Disciplines with fewer citations per paper will appear to have less impact.
citation half-life: Longer half-lives means under-estimation of impact relative to journals with shorter half-lives. The Wikipedia article on impact factors summarises a study that found that "the percentage of total citations occurring in the first two years after publication varies highly among disciplines from 1-3 percent in the mathematical and physical sciences to 5-8 percent in the biological sciences." (Nierop, 2009).

Google Scholar uses the five year h-index. See this listing of top ranked journals with various psychology related keywords in their title. The five year h-index indicates the number of papers with an equivalent number of citations. E.g., a value of 20 indicates that 20 articles published in the last five years have received 20 or more citations.
However, while the h-index might reduce the issue of different citation half-lives, it does not resolve it. And it does not address the issue of differential citation patterns across disciplines.
Question
What index provides both a reliable and unbiased assessment of the citation based impact of a journal when comparisons are being performed across disciplines?
Reference

Erjen van Nierop (2009). "Why do statistics journals have low impact factors?". Statistica Neerlandica 63 (1): 52–62. doi:10.1111/j.1467-9574.2008.00408.x.

Defining field boundaries is an extremely difficult problem. There is some cool work on this via statistical properties of random walks that makes nice definitions, here. I recommend taking a look around that site. A naive metric might be: classify journals into their fields and then assign each a ranking p, meaning the journal is in top p% of its field by some standard metric (say eigenfactor, since I link to that site). Note that the website claims eigenfactor and article influence scores adjust for different fields, already.

@ArtemKaznatcheev That sounds reasonable, and I imagine that discipline specific rankings is a common approach. My first thought was simply to think of a metric that is less influenced by citation half-life and discipline specific citation patterns.

The reason why I asked it here is because this is a real issue in some psychology departments where research output is being compared with academic output from other fields such as the biomedical sciences. But I'd be happy for the question to be migrated to academia.SE if it was preferred.

I raised the question of migration in chat.

There is no such index.
Publication and citation standards vary significantly between disciplines and even sub-disciplines.  Without direct, deep knowledge of the standards in each community, it is simply impossible to compare impact of a journal in field X with the impact of a journal in field Y.  (Eigenfactor's extraordinary claims to the contrary require extraordinary evidence, which they don't provide.)
Moreover, it's not clear why you should even try.  Any judgement about the relative importance of Journal of X versus Journal of Y necessarily requires a prior judgement about the relative importance of field X versus field Y.  HC SVNT DRACONES.

And Thompson's is evil, to boot :)

More importantly, at least within computer science, they're simply incompetent.

Here's an informal and difficult to quantify one: does it suffice to have a publication in this journal to get tenured? Or, in a reverse manner, can one get tenured without having published in a journal like this?

In economics, you will get tenure in most reasonable places for publications in Quarterly Journal of Economics, American Economic Review, or Econometrica.
In statistics, these would be Annals of Statistics, Annals of Applied Statistics, Journal of the Royal Statistical Society Series B Methodology, the Journal of the American Statistical Association, and Biometrika.

These are the fields I am closely familiar with. I can imagine that in most natural sciences, a publication in Nature or Science would boost your chances quite a bit. Finance has a system of ranking journals, with a handful of highly coveted journals designated as A-journals.
Getting reliable statistics along these lines is of course impossible. If Google were a little smarter, it would scan the CVs of those tenured and untenured (those who moved to another university after the typical period of 5-6-7 years), grab their publication records, and see what the journals are that those who got tenured had published in that untenured hadn't.

The CWTS SNIP factor controls for differing disciplinary citation rates and speed. See http://www.journalindicators.com It is also shown in Scopus

Moving from comments:
Defining field boundaries is an extremely difficult problem. Once those boundaries are defined (or as they are being defined) it is also difficult to judge which field individual journals belong to. The technical problem is one of "community-structure" or cluster detection, and is a big problem in computer science.
For the specific case of journals and citations, there is some cool work on this via statistical properties of random walks that makes nice definitions using the map-equation. However, these statistical methods sometimes produce oddities (I think I remember seeing Phys. Rev. Letters being classified as Chemistry based on the stats). It also doesn't always produce the fine-gaining desired, for instance eigenfactor seems to not have a Cognitive Science category, just a general Psychology category.
In general, I recommend taking a look around eigenfactor. Note that the website claims eigenfactor and article influence scores adjust for different fields, already (take a look at point #4). I am not sure how accurate this claim is, but I personally find their metric more reliable (not to mention more accessible!) than ISI Web of Science. I also think their approach is more developed than the freshly-pressed Google Scholar journal rankings based on h5-index. However, I would love to see eigenfactor and Google Scholar together.
If you are unsatisfied with eigenfactor's rankings, then a naive metric might be: classify journals into their fields and then assign each a ranking p, meaning the journal is in top p% of its field by some standard metric.  This should give you a rough idea.


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What are some proven methods for keeping track of research and programming in a lab notebook format?
Are there any proven methods for keeping a computer science lab notebook? What sort of stuff goes into a CS lab notebook, and what notation is used? Are there any reliable ELN methods, preferably integrating with Git?
I want to keep a lab notebook that helps me keep a running narrative of my research as well as my programming, in the same way you would keep a natural science lab notebook. It would be handy to be able to write down what I'm doing as I'm programming, then when a bug mysteriously appears in the software, I can see the last time the software was working, see where the bug started, then read the running narrative in between to help figure out what is causing it.

What's a “lab notebook”? I'm unfamiliar with this terminology. Is this something you write as a researcher? Or as a student? Is that lab as in doing experiments, or as in a class? Is this something you need to do for some legal purpose (say, related to patent claims), or for a teaching institution, or for yourself?

I think this is a duplicate of http://academia.stackexchange.com/questions/1085/software-app-for-electronic-research-notebooks.  Several interesting answers over there.

I don't do CS specifically, but I do research in computational/mathematical Epidemiology, which similarly lacks a clear format for a "lab notebook" and where the electronic tools designed for something like chemistry and biology doesn't necessarily suit.
Honestly, I've tried a number of systems. A private Wiki, a variety of other electronic systems, etc. And when it came down to it, I went back to a traditional paper notebook.
I've ended up with a bound paper notebook where I keep notes about experiments and simulations being run, thoughts on direction for the project, and the occasional printout of results taped inside. This has, for me, worked far better than electronic systems.

I might have to try this out. I really hate taping things in to a notebook, so I'll probably just keep anything printed in a separate folder and refer to them as Attachment 1, etc. I like to keep track of my thought process, and I was considering using Wordpress with a live blogging plugin, but I think I'll have to resort to paper. I like drawing diagrams, and Wordpress doesn't really support that.

@NickAnderegg TiddlyWiki may be interesting for both of you. It resides in a single HTML file and can easily be carried around on a thumbdrive (or hosted on a webserver, obviously). Thus, it may be closer to a classic notebook than a hosted Wiki.

I use Jupyter Notebooks as my (almost) all-in-one research environment. Jupyter is a locally-hosted webapp designed primarily to write code in a convenient interface in a web browser. The native language is Python, but there are kernels for many languages. Jupyter also supports writing text with Markdown and MathJax inline with your code. The notebooks can be exported as HTML, PDFs, slides, Markdown, .rst, or .tex files (especially useful when drafting papers for journals/conferences that provide a .cls file!). And because a notebook is stored as a plaintext JSON file, it's VCS friendly.
The next iteration, JupyterLab, is currently in the late-stages of development and is in beta.

Are you affiliated with JupyterLab?

I'm not affiliated.

In contrast, back in my postdoc we referred to the Jupyter Notebook we were using as "The Wall of Madness", and I found it wholey unsuited for lab notebook use.


text:
Is it okay to inquire about the status of a paper when the online submission system shows no update three months after submission?
Around 3 months ago, I submitted a paper to a Math journal for peer-review through an online submission system. The same day I received in my e-mail an acknowledgement saying that the paper had been received.
The problem is that the status of the paper on the online submission system is still "Manuscript submitted".
I think this means that the paper has not even been assigned to an editor yet (once this happens, the editor will probably start looking for reviewers), but as far as I know submissions are assigned to editors within a few weeks (I once submitted a paper to another journal and when an editor had been assigned, the status on the online system changed to "Editor handles" or something like that).
I know that publishing is a long and complicated process and I'm not trying to rush anybody, but sometimes manuscripts are lost in the system. In fact, this has once happened to me, so I just want to make sure that the paper was not lost in the system.
Should I send an e-mail asking for the status of the paper?
PS: I think that, at the time of submitting the paper, I didn't choose any specific editor to submit the paper to.

A short, polite email asking about the status of the paper would not be remiss after three months without any additional information.

Yes, you should send a quick email asking for an update. In my experience, every electronic manuscript submission/tracking system seem to be different. Even different journals from the same publisher who use a unified log in system, seem to have tweaks to what you see. I think that after one month at any one stage, with the exception of "under review", it is perfectly reasonable, and probably desirable, to ask for a status update. The time a manuscript spend "under review" is often very field, and even journal, specific and can range from days to 12+ months.
When sending an email, be polite and explain that you just want to make sure that you do not need to do anything at the current stage. In all likelihood the status update will be no more informative than "we are working on it" (i.e., they haven't lost it), but that is informative.

♦ I already sent an email (through the journal's online system) several days ago. How long should I wait for a response? Is it possible that things go slower during this time of the year?

When this has happened to me, I was told to look at the journal's list of editors that are in the appropriate subject area, and contact them directly to see if they would accept the task.  You may have to do something similar.  Ask Nicely!  It will also help if you have colleagues in common so that you can strike up some conversation and get them interested.


text:
How can I fill in the missing background I need to be a good computer scientist?
I'm doing my PhD in computer science and I'm almost finished (maybe one more year).
In fact, my background was engineering, but in my MS study I got interested in CS and took courses about "pattern recognition", and "distributed AI" and "robotics" so I tried to continue that line in my PhD which is about machine learning and data analysis.
I tried to become an expert (in a PhD level) in my topic of PhD as I read a lot of papers and got focused on one topic, but here and there I see my colleagues (same discipline) are talking about methods and principles that I'm not familiar with.
The fact is that I want to continue in this field as a post-doc and even more, but I feel a gap in my background, and I'm wondering how I can fill it? For example, should I take online courses related to commonly taught materials in a bachelor/master CS program?

"For example, should I take online courses related to commonly taught materials in a bachelor/master CS program?" : IMHO, that will be a waste of time, you'll subscribe, think about it for a week, and then forget about it because you'll be busy doing other (more critical) stuff. To me, the best way to learn at your level is to teach: if you can find a post-doc with some teaching duties, that would fill some gaps and corresponds to your job description, so that you'll have an insensitive to do it, and to do it properly. And don't worry, you'll learn faster than your students when the time come.

Let me give you a bit of perspective, as well as assurance. In mathematics it is no longer possible for a person to be knowledgeable in every aspect of the art. That possibility ended early in the 20th century. Henry Poincaré is often given as the last person who was conversant in everything mathematical. Math has evolved a huge amount in the interim. So, even Poincaré didn't know all that we now know about math.
In any field there is a tendency toward specialization. Research normally requires this. Over time, being a good researcher requires less and less of the whole both because of expansion of the field and the much narrower needs of a specific sub-topic.
While it is good to know a lot and your goal of expanding your horizon is a good one, don't be discouraged if others, especially other researchers have different ideas, use different tools, and especially utilize different thought patterns than you do.
Probably the most effective means, given that you like your research area and that it rewards you properly, is to branch out from there, studying similar things that you don't explicitly need, but which might come in handy. But rather than focus on the details of the related topics, pay special attention to whether the thought patterns you hear discussed are somewhat different from your own. Think Different was a slogan (Apple Inc.), but it has a deeper meaning.
Another way to expand your horizon from a given base is to study something very different from what you know, but to which your current knowledge might be applied.
I don't know of CS has reached the stage yet where you can't know everything, but I suspect we are pretty close to the boundary. You can have a broad education, and you should. You can also go deep into one or a few areas, as you also should. But there is (likely) too much there to do it all.
Modern Academia tends to reward specialists, as I'm sure you are already aware.

"I don't know of CS has reached the stage yet where you can't know everything" I can guarantee you it did, a pretty long time ago already.

@Clément, If I were naming names, I'd think Edsger Dijkstra may have been the last "polymath" in CS, though Don Knuth might be another candidate (Don is alive, of course, hence my hedging the bet). Quantum Computing made the game harder, of course.

In 1970, when I was a young programmer, I thought that if I studied hard enough I could learn a substantial proportion of what there is to know about computing. Almost 50 years later, I am still learning, but the field has grown far faster than I can study it. Even in the narrow field of programming languages, I have programmed in a smaller percentage of them than in the 1970's.

@PatriciaShanahan, it would be good to think that our learning is superlinear (in some sense), but the expansion of the field is exponential. Even if the exponent is small... But synergy implies we have a bigger and bigger playground. Growing faster than anyone can explore.


text:
How do professors view a student who has good grades but always asks stupid questions?
I am planning to apply for Ph.D programs in mathematics in U.S. and I am thinking about which professors I should ask for letters of recommendations from. In order to get "strong" letters, I need to determine which professors believe I am really strong.
Although I do well in most of my classes (I am always trying to get full marks on my homework and tests), I do have a "bad" habit of asking stupid questions frequently. I can see from the faces of professors that they dislike students who always ask stupid questions, while welcoming students to ask questions. (If you are a professor and don't think this is the case, please let me know...I really appreciate it.) I feel like professors prefer the questions that will make them have a better understanding about the subject instead of the questions that they will waste their time on.
What do professors think of a student who has good grades but always asks stupid questions? Should I expect good letters from them? Or I should go to the professors whom I seldom ask questions (but still do well in their classes)?

"There is no stupid question but stupid answer"... this dear Einstein. What do you think is a stupid question ?

@GautierC I define a stupid question to be a question which is so easy that a professor believes that a student should think of it by himself instead of asking it.

then why are you asking those questions ?

@GautierC Because I don't think those question are easy to me when I ask (but turn out to be easy when they answer).

then first of all, you have to work on yourself. Everytime you want to ask for something, think a bit about it, write it down and go to see the teacher after the course to ask him about what you REALLY are not able to answer. I know it is not the question, but it is still the main problem ^^

is it wrong in US to ask for recommandation letters, even if you think the teacher don't think high of yourself ? I mean, you just have to show the most relevant ...

I am not really sure whether the professor thinks high of myself or not. Because if I were the professor, I wouldn't care about the stupid questions asked by the student and I would measure him mainly by his ranking of grades in my class. I believe that a student is still excellent in this case even if he asks stupid questions.

Human relations are as much important as notation is some cases, especially for recommandation letter. Anyway, why do you not try to ask him what he thinks about you ? It's not like it is impossible to speak with this teacher, right ?

If there are students like that, I would certainly start questioning my assessment techniques. If a student clearly demonstrates a complete lack of understanding when asking a question and still does well in my exams, then there is a problem with my exams.

@GautierC  This will be another questions that I want to post here...Can I first ask how the professors think of me before I ask them to write letters? Letters of recommendation are supposed to be confidential, right?

@BurakUlgut or a problem with the student ^^

@TiWen in your position, I would go to see the teacher, explaining to him the problem clearly, and that's all. I mean, it's a teacher, it's not a random guy, more than giving you courses, his task is also to help you if needed. Being honest is the best way to deal with a problem, especially since it's because you think about your future and about yourself.

@BurakUlgut I don't think that is always the case. In most times, a student who ask stupid questions still does better than most of the students in your class. Because at least this student dare to ask and is able to ask. If a student can't digest at least half of the materials covered in your class, he may not ask any questions.

and "half of the materials" is just conservatively estimated. If I don't get 80% of the materials covered in class, I dare not to ask either.

Are you turning in the recommendation letters or do they submit them on your behalf? If you are the proxy just request several professors write letters and only submit those that you find flattering.

Only the professor? We hated people in faculty that just made up stupid questions to lick the boots of the professors, and there were quite a few of them. A few professors also clearly did not like it.

They will think you are dilligent and stupid.

I have great tolerance for "stupid questions" by students and try always to answer them (if possible) by taking an unexpected angle, even for simple ones. The reason for that is that, as a student, I was able to sometimes ask a series of seemingly stupid questions of things taken for granted but never properly explained/looked into in detail (this may quite have annoyed the profs and TAs), and then, at certain moments produce a one-liner when the prof and everybody else was stuck and stumped. This clearly confused people. So, if your questions really help you to better understand, go ahead.

Students often have a highly distorted perception of themselves with respect to the feelings of their professors.  The differential in both power and experience between student and professor is just so large that it's quite common for a student to confuse the very distinct attributes of professorial attitude, personal affection, and intellectual respect.
As such, I would suggest that you really don't know what your professors think of you until you ask them.  Maybe you are reading them correctly, but maybe not: many professors are quite pleased to have a student who carefully advocates to improve their understanding of material, even if they might wish to be getting on with the lecture in the moment.
My recommendation is to tell the professor you're thinking of applying to Ph.D. programs and ask them something like:

Do you think that you would be able to write a strong letter of recommendation for me?

The "strong" is important here, because that's what will get you the honest opinion of whether the professor thinks well of you or not, and you don't want letters that are not strong.

Oh ...I have just posted the question in yellow on this community~~~but in a more sensitive way...

@TiWen I'll put a linked answer there.

Although I do well in most of my classes, I do have a "bad" habit of asking stupid questions frequently. I can see from the faces of professors that they dislike students who always ask stupid questions while welcome students to ask questions. (If you are a professor and don't think this is the case, please let me know...)

I don't think you've provided enough context to really say for sure whether or not your questions would be annoying.
Here's what I'd need to know: Does the professor (or the syllabus) say that you should read from the textbook before you come to class? If so, do you read the book as assigned?
If you constantly interrupted me with a barrage of questions that indicated you hadn't done the assigned reading, then my face might also show some of the consternation that you claim you see.

How do professors think of a student who has good grades but always asks stupid questions?

Generally speaking, I like it when students ask questions. It shows me they are engaged. It shows me they are interested in learning the material. It helps provide feedback when I haven't explained something clearly. Quite often, the one student who is brave enough to speak up is asking for help that other students probably need and appreciate.
That said, though, there are times where there can be too much of a good thing. If one student's questions are so frequent and incessant that it becomes distracting for everyone, that might be viewed negatively. But that's perhaps more of a timing issue than a "stupid question" issue.

None of my professors write in syllabuses that students should read from the textbook before they come to class(we may be required to read the book after class).

Ti Wen, your professors may omit the point that students should read before coming to class because they consider it painfully and completely obvious. :)

Don't forget off-topic questions.  I suppose these are less likely in math but I remember in economics classes that dealt with things that were remotely political there'd be one person that wanted to turn the class into a debate on the topic du jour

J.R.: regarding your last two paragraphs,  and also @Dean MacGregor's comment,  the following was written by a student in my linear algebra class: "The instructor was very kind and patient with students who spoke out of turn and reacted heroically to questions that were asked which I found to be far off topic and confusing".

First, even if your questions annoy your prof intensely, you may still get a good letter. And conversely even if your questions demonstrate your commitment to learning and are actually welcomed, you may not get a good letter. So the only way to know is to ask,

Do you think you can recommend me strongly for [whatever] in a letter?

And if they say no, don't push them because they are telling you it wouldn't be a positive letter.
Now let's tackle those questions. There are so many reasons why I might make a small face when a student asks a question. Imagine we're doing non-university level material and I say "there are five vowel letters in English: A, E, I, O, and U." Up pops your hand and you ask:

isn't that 4? No, it's not 4, you've interrupted me for no reason. It's 5, right? Now where were we?
What about A? I said A, that was the first one. Oh, sorry, wasn't listening
What's a vowel? Either that's what this whole lecture is about or it's in the material I asked you to read before class, or I just covered that on the three previous slides but you were zoned out, or in some other way, a person who doesn't know what a vowel is shouldn't be trying to find out by interrupting a list of them to ask
In [some other language] there are 7! Fascinating, but not interruption-worthy. Thanks for sharing.
What about Y? I was just breathing in to explain Y, it's a little more complicated. On this one my annoyance is just that you've broken my rhythm, it's actually a fine question and leads to my next point, so I need to relax and keep going

Think about the questions you tend to ask. Are they the first kind, where you are correcting or contradicting the prof, pointing out an error, when there is no error, you made a mistake? Try not to do that. Are they overly broad, or do they show that you came to class unprepared? Try not to do that. Are you just randomly sharing your thoughts in the middle of someone else's sentence? That's not a question. Do that only in discussion parts of class. But do keep in mind, there are questions that irritate me that don't make me feel less about you or not recommend you. They are just a little annoying.
But if your question is none of these, it's you genuinely seeking clarification on something you just heard and don't understand, then you're probably doing fine. You can always approach the prof after class and ask if your questions are ok. That will clear things up for you, I'm sure.

"Are you only counting letters as vowels for some reason?"

While jakebeal's answer is good for what you should do, I'll expound on your titular question:

How do professors think of a student who has good grades but always asks stupid questions?

First, there are a couple of things you could mean here by "always": you're incessantly asking stupid questions or all (or most) of your questions seem stupid to you in retrospect.  J.R.'s answer addresses the first, so I'll ignore that aspect except to say you can always ask your professor outside of class if you are asking too many questions during class.
Second, I ask "stupid questions" all the time also, though I don't vocalize most of them, just some of them.  I think this is normal for researchers, and it's especially common when you're thinking about something for the first time, or thinking about something in a new way, and trying to understand something in a short amount of time, such as a class meeting.  So just because a question has an easy answer, doesn't mean it's sounds stupid to a teacher.  It may or may not, so as the other answers say, you may not be able to accurately judge a professor's opinion of your questions without explicit feedback.
However, if these questions are coming long after you should have learned the material (e.g., asking something that's obvious from high school algebra in an advanced math class, or only now asking something that was crucial for understanding what's been going on in the class for the past 10 weeks), then probably the professor will think you don't understand the material as well as you should.
Finally, I get lots of students in math classes who ask lots of questions that make me think they don't understand what's going on very well at all, but then surprise me by doing great on the assignments and exams.  This sort of thing seems to be what you're concerned about (though may in fact not be the case).  Without knowing anything more specific, generally my impression is they're good students, though probably they didn't have a strong background coming in and/or they're not exceedingly quick.  By not quick, I don't mean they're not smart or that they wouldn't do well in grad school--you can be quick and smart or slow and smart.  While thinking quickly can be impressive, I view thinking deeply as more important.
I would generally be able to write a good letter of recommendation for such a person, though I would probably be writing different things from "Ti has one of the fastest minds I've ever known."

I think this is a good answer, although any student reading this answer should know that faculty will vary. I am someone who didn't have a strong background and typically asked a lot of questions in class. I once had a faculty member become upset with me for asking too many questions. I then went and asked each of my other professors (current and past) how they felt about my questions. Other professors said that they liked my questions, and a few noted that I was one of their favorite students because of my questions.

Speaking as a professor, the problem may not be "stupid" questions but rather "too many" questions.  When a student interrupts the flow of a lecture, it can be annoying, but when it is the same student that interrupts the flow on a frequent basis, the dismay you recognize is not because the question is stupid but rather that others do not appreciate the frequent interruptions.
So yes, ask frankly if a strong letter would be proffered.
And, perhaps, take some of your questions to office hours.
another old cranky lecturer

Agreed. I had a student like this who was always derailing the lectures but was very good. If he had asked me for a letter I would probably have said "I would rather not write you a letter, but if you can't find anyone else, let me know." Of course, I would not have written anything negative if I did end up having to write one.

I find that if a student is brave enough to ask a question, there will likely be at least one other student that has the same query but lacks the confidence to ask.  Also, I am not perfect, sometimes I may assume some prior knowledge that not all students have.  In these instances, I always welcome questions.
If I am at a part of the lecture where I feel a question will be distracting, I simply acknowledge the raised hand and make it clear I will come back to it (I make sure I do come back to it).
The only times I would ever find questions irritating would be if the student had missed a previous session and failed to catch up (my notes are always posted on the VLE so there is never any excuse for this) or if they regularly failed to complete their prescribed reading.  I would hope that a student would deal with any queries arising from their preparation before they turned up for class but there are always some who will leave it until the last minute!
If asked to provide a letter of recommendation, I consider academic performance, potential, attendance and punctuality, in that order.  These are all things that a student can control.  I would never base a reference on a student's personality. That would be unfair.
Incidentally, I asked tons of questions as an undergraduate and one of my lecturers gave me a job!

You are right! There's a saying A sensible question is half knowledge.

Stupid questions exist. There is the occasional student who often asks those. Do not be that student!
Fortunately, most questions are not stupid. For most questions, a significant, silent fraction of the class appreciates that the question is asked. (How do I know? Several ways. For instance, when a question requires a long answer, before answering, I will sometimes gauge interest by inviting all interested students to show their hands. More often than not, several students will want the answer.)
Also, if the instructor is teaching the course in question for the first time, sometimes students who are not otherwise troublemakers ask questions merely to slow the lecture down, because the lecturer is inadvertently covering too much material too fast. Such questions represent valuable feedback.
Occasionally you get a student who just probably isn't smart enough to pass the course. If you politely invite that student to bring his or her questions to office, that student usually gets the hint and stops stalling the lecture with questions his or her classmates don't care about. The student may or may not then come to office, but that's for the student to decide.
The problem student is the student who asks questions because he or she likes attention. Every class of a certain size seems to have one or two of those. The classmates usually don't like that student, either.
All you can do with a student who asks questions because he or she likes attention is (a) call on other students first whenever possible, (b) give the troublemaker curt answers and smoothly move on with the lecture without inviting further discussion, or (c) in the final need, affect for a while not to see the student's raised hand. Unfortunately, most such students won't take the hint. Oh, well.
Fortunately, most questions asked are worth answering, or at least worth respectful deferral, in my experience. Moreover, depending on an instructor's lecturing style, good questions (which often arise) really help a lecture to move along. Some of the best questions come when the instructor has briefly glanced upon some point the instructor thinks is obvious but the students don't. The instructor needs to know that.

It is simple. Tell the professor you're thinking of applying to Ph.D and ask his opinion. If he suggesting Yes, you can go for and ask.

Welcome to SE! This is the beginning of a good answer, but you could elaborate a lot more, especially since you don't address the core question the OP is asking. Just asking the Prof may be good advice, but it is so general as to be unhelpful in this specific circumstance. It is rather like someone asking "How do I drive on ice?" and you answer "Very carefully. Be sure not to have accidents." Try expanding this answer to meet the specific question that is being asked, and this will likely get and upvote.


text:
Why do most mathematics PhD programs in United States have rigid qualifying and breadth requirements for every PhD student?
For a long time, I have been confused about the purpose of having (roughly the same) qualifying requirements and breadth (course) requirements for every mathematics PhD student (in many, if not most, mathematics departments in the United States).
I have heard that European math gradaute programs do not have such rigid requirements. In U.S., although many math PhD students only start with an undergraduate degree and they do need a lot of training in graduate math, there are still a lot of students who come with a master's degree and that is the reason I add the qualification "everyone". I am just asking why we need (roughly) the same requirements for everybody in a program instead of having tailored requirements for individual PhD students, especially for those who have already taken graduate level classes, had their confirmed interests, and even written a Master level thesis in mathematics.

In response to the comments below, I will add some parts that I deleted earlier back.
An example: many programs, such as Stanford and Ohio State, require students to take qualifying examinations in real analysis and algebra. (Some programs require exams only in real analysis and algebra, despite having strong research groups in other fields like topology.) Why are real analysis and algebra both necessary for everybody? Does an algebraic geometer have to be so familar with abstract measure theory? Does a topologist have to be so skilled in using the "Big Three" theorems of Banach spaces? Does an applied mathematician have to know Galois theory so well? Moreover, the real research is quite different from exams. The statements of problems are usually not well formulated, there is no specific time limits, the techniques can be convoluted, etc.
Taking courses are not the only way of learning things in a PhD program. PhD students can learn a subject on their own or through student reading seminars. I am not denying the necessity of taking classes for certain students (especially those who come with undergraduate degrees)---that is why I added the qualification "everyone".

This seems more of a rant than a question - perhaps you could focus it down? One counter-argument would be to ask how many incoming graduate students really know their area of interest if they have not been broadly exposed to the field?

@JonCuster Sure, just removed most part of it

@JonCuster My focus is the last sentence.

"studies have shown that the best training for any activity is that activity"... Sometimes general studies do not apply to mathematics... And by sometimes, I mean most of the times...Also, while students think about this as taking unnecessary courses, when you become researcher you'll be happy that you did.. And this is not just for background information, often an idea from one area can be succesfully used in another...

To answer your last question, in North America many students get the first glimpse at true research in mathematics during  their PhD. Also, they often discover that the area they thought will be interesting may not be the best fit for them...

@NickS I am not asking why a graduate student have to learn things from different fields...I am just asking why math departments in U.S. have such rigid course requirement.  A PhD student doesn't have to take classes to learn things.(from the parts I removed...)

@NickS I agree that "in North America many students get the first glimpse at true research in mathematics during their PhD". But at the same time, there are also many students who have already had lots of glimpses of true research before they enter PhD programs.

(1) an incoming PhD student is not yet a PhD student ready for quality individual learning. (2) Glimpses of research areas are not a good foundation for future work or for picking a focus area.

@JonCuster In respond to (1), I feel like I have to add the things I deleted back. My point is that incoming PhD students who have already had their interest don't have the freedom of taking courses of their interest.  Does an algebra geometer have to be so familiar with abstract measure theory? Does a topologist have to be so skilled in using "Big Three" theorems of Banach spaces? Does an applied mathematician have to know Galois theory so well?

@JonCuster For (2), that was just a response to NickS's comments. But Again, I am not questioning the necessity of learning different things. I am questioning the restrictions on course choosing for the incoming PhD students.

Yes, I believe there are some things every mathematician should have seen no matter the field. And IMO measure theory is one of them. The second, and bigger issue IMO is that an incoming PhD student who already had their interest settled only saw a small glimpse of that field. When the incoming student starts doing research in that field it suddenly looks different, and you'd be surprise how many want to change field at that moment. Also, I know many students who were settled on a field until they took some courses in a different field and found it fascinating...

At the end of the day, by seeing a variety of courses a student can make an informed decision, and most of the time that is better than just picking and sticking blindly with the first choice you made, a choice made even before truly knowing what research in that field is.

@NickS Yes, students need to a variety of courses, but some incoming PhD students have already seen a variety of courses in their undergraduate/masters program. Why do such students have to repeat?

@NickS I agree that measure theory is one of those things  every mathematician should have seen no matter the field, but I don't believe everyone has to be strong enough in measure theory to pass a challenging exam in it. I am more inclined to believe everyone has to be strong enough to pass a test in cohomology... That is why I only question the "requirement".

One should keep in mind that in Europe, most PhD students already have a master's in mathematics. In my MSc+BSc I had courses on all the topics that you mention. You might be able to skip one or two of them, but certainly not much more. And I think that I followed a fairly typical program for European standards. So I don't think the situation in the US and Europe is so different in the end.

@PieterNaaijkens In U.S., many incoming PhD students also have a master's in mathematics. The situation is different when it comes to those in U.S. who have to repeat things that are unnecessary for them...

"Repeat" is a rather incorrect word here.  The truth is that graduate programs in the US are of hugely varying standard, and a student who earned an A in the Commutative Algebra course I occasionally teach in the graduate program here almost certainly does not know Commutative Algebra well enough by the standards of U. Wisconsin, never mind MIT.

@AlexanderWoo Yes he may be not and when he gets accepted into PhD programs in U. Wisconsin or MIT, it would be better for him to take comm. algebra again if he is interest in algebraic geometry but he shouldn't have to if he is interested in applied math. But what if a student in your school is already strong enough in comm, algebra to meet the standard of, say Oregon State (no offend to anyone...), does s/he have to take comm algebra again when he go to the PhD program there?

@NoOne: Your PhD is not just your dissertation, and it is granted by the university and the department based on your knowledge of mathematics as well as your research.  (For one thing, a PhD should certify that you are qualified to teach every undergraduate course with a reasonable amount of time for preparation.)  It is entirely reasonable for the Wisconsin faculty to decide that every one of their PhD graduates should know algebra up to their standards, no matter what their research area is.

@AlexanderWoo Finally someone tries to address the core of the issue---taking classes and passing exams are not just means to help one do good research and get a math PhD, but also the part of goals of a math PhD. Still,  i don't see to the development of graduate students, why it is reasonable for the Wisconsin faculty to decide that every one of their PhD graduates should know algebra up to their standards

@NoOne - because it is reasonable for Wisconsin faculty to agree that the standards at my university are inadequate - (in other words, to implicitly believe as a group that my university should not be granting the degrees that it does)

@NoOne: "Does an applied mathematician have to know Galois theory so well?" The theory of error-correcting codes makes use of Galois theory (e.g. BCH codes), so even some electrical engineers need to know Galois theory. This is precisely the idea behind breadth requirements - that certain fundamental ideas (Galois theory contains some fundamental idea) are sometimes applicable far from the contexts in which they arose.

So far as I've seen, in the U.S., the "required" aspects of most graduate programs are really very minimalistic and elementary. So it misrepresents the situation to refer to them as "rigid", since they're so minimal. And it misrepresents the situation as well to refer to those minimal bits as somehow clearly un-necessary for ... "non-specialists"?
I'd claim that these basic requirements are just to inculcate awareness of the various basics. Again, minimalist.
Further, some places have choices or other requirements. E.g., many require some complex analysis. We in MN do, as well as some algebraic topology and differential geometry... although students who want to style themselves as "applied" can manage to avoid quite a bit of this, by doing some "math modelling" and "numerical analysis" coursework.
True, in principle people can mostly learn whatever they want outside of formal courses, and I myself prefer that, because the artificial schedule of MWF classes, etc., does not fit my personal rhythms. But for some people, manifestly, this is easier than self-study. Also, I've been told by many grad students that it's easier to avoid self-deception/delusion in a more formal classroom setting. Ok.
And, of course, some people arrive in grad school being vastly over-confident of their own scholarship, as well as the significance of their "prior research experience". To make decisions based on very-incomplete information would be unfortunate.
And, of course, classes and exams are a very artificial, caricatured version of mathematics... which I think is best appreciated as not being really a "school subject" at all. Nevertheless, it is not easy to construct viable alternatives. Here in MN, anyone who really has already picked up a bit of competence in the basic subjects can "test out", by exam, and not have to take those courses. Many do. We do not try to test "mastery", but only "(at least) minimal competence".
Yes, having been involved in grad studies throughout my 35+ years here, I have often seen students complain about the alleged burdens of our "requirements". Mostly, that convinces me that they're sufficiently unaware so that they appreciate neither the minimality of our requirements, nor the universal utility of the ideas. :) But I understand that "requirements" are often viewed as "obstacles to getting on with research". I do claim that this represents a substantive misunderstanding of what's going on...
EDIT: as for a further question implied by, or suggested by, comments... although of course inertia plays some role, faculty in every grad program "require" things of grad students (regardless of eventual "specialty") after substantial consideration. Not whimsically. Indeed, exercise of such judgement is very important. The possible fact that entering grad students (arguably in possession of very little information about what they'll need later, etc.) may disagree or resent "requirements" will not deter faculty from requiring what they consider wise, based on decades of experience.
A related question, that of requiring reading competence in French and/or German, etc., has a substantially different answer. E.g., while I do still encourage my own students to learn at least French (to read Sem. Bourb. at least), and maybe German (Hecke, Siegel, ...), our program as a whole is ever-reducing these requirements, since, indeed, a greater and greater fraction of contemporary mathematics is written in English. Not all!!! I have always regretted that I didn't learn enough Russian to be able to read mathematics in it... Sure, one can "get by" being substantially illiterate, but it is quite often useful to be less illiterate! :)  This applies also to awareness of the rest of mathematics, outside one's tiny bailiwick ... however large it may seem in a myopic view! :)

Although included in your answer, perhaps it's worth being more explicit. At many (probably a strong "most") non-top U.S. universities, MANY more students are accepted into the program than will finish, and the Ph.D. exams are used to weed out students from the Ph.D. program for the/a Masters program. From this 9 June 2016 answer of mine: When I passed the qualifying exams, there were 15 people taking the exams and only 5 passed, (continued)

and this does not include several other people who decided to bail and not take the exams after some experience in graduate level courses (and the realization they would likely not pass the exams). Of those 10 who did not pass the exams (given in August), I think 2 (out of 4 or 5 who decided to make a second attempt) passed during a follow-up exam the following January, so a total of about 7 out of 15 students for "one year" wound up being admitted to candidacy.

Do you think that reducing language requirements is a good thing or is it "lowering standards"?

@Mehta, I think reducing language requirements is probably a correct re-prioritization of "requirements". Some things are more important than others.

Speaking too broadly, the US and Europe have different models for PhD programs in mathematics. This reflects partly that they have different models for undergraduate programs in mathematics, and so students entering PhD programs do so with different backgrounds. It also reflects partly a difference in philosophy. (Speaking of "Europe" as a unified whole is obviously ridiculous, but for this discussion it is probably not as ridiculous as it would be in some contexts, provided one's notion of Europe does not include Britain where the educational system possibly has more elements in common with the US.)
In Europe an undergraduate studying mathematics typically takes only courses in mathematics, perhaps with some complementary courses in physics, computer science or statistics (closely related fields). In the US an undergraduate studying mathematics might take well less than half her courses in mathematics (My case is a bit extreme, but it makes the point - I got an undergraduate math degree from a US university taking 14 of 38 courses in mathematics, and 2 or 3 more in related fields, depending what one considers related; this is simply incomprehensible in most European systems). One consequence of this is that when a student finishes a European undergraduate program in math that student has typically seen a lot more math than a student who finishes a US undergraduate program in math (the situation is really far more complicated - the far greater flexibility of US undergraduate programs means that it is possible for a student to take a great deal of math, even at the graduate level, as an undergraduate, and so the distributions of courses realized are very different for US and European students - (again speaking too broady) in any given European system every student takes essentially the same classes, whereas in the US two students with math degrees can have taken radically different sets of classes). The content that the typical US student hasn't seen as an undergraduate is frontloaded onto the doctoral program in the form of the classical year long first year doctoral sequences in analysis, algebra, and something else - note that many US doctoral programs allow an incoming student with more preparation to take qualifying exams early and skip some or all of the required courses - such contingent mechanisms handle the better prepared entering students coming from Europe or Asia or from a US program that allowed the student to advance rapidly. In practice the administrative flexibility of US doctoral programs means that "requirements" are adjusted or waved as is necessary to accomodate individual circumstances (I know a successful researcher who never passed one of his qualifying exams). In Europe, "requirements" usually really are requirements in the absolutist sense of the word, and the only way to endow a doctoral program with a similar flexibility is to minimize the formal requirements.
The preceding should all be qualified by the observation that roughly half the PhDs in the mathematical sciences awarded in the US are awarded to students who hail from outside the US, probably a greater percentage at research oriented institutions. So in fact the typical student entering a US graduate program in math comes from Asia or Europe, and was prepared in a non-US system. Although this is less true in Europe, it is increasingly true in Europe as well. Nonetheless, the institutional expectations implicit in how graduate programs are structured are typically based more on the corresponding expectations implicit in how local undergraduate programs are structured than they are on the nature and origin of the students entering such programs (the latter takes longer to assimilate administratively). (Institutional expectations need not be set by mathematicians, nor even by academics; often they are imposed politically or administratively by people with little understanding of what they entail except in a formal, procedural sense.)
The other reason is philosophical. This is a personal impression, probably quite debatable, but my impression is that European doctoral programs tend to be more focused, the idea being that students immediately start research, and develop deeply in one particular area, while US doctoral programs tend to be less rushed, and have as an explicit goal that the student acquire a certain breadth, the idea being that really novel ideas often come at the interfaces between well established research areas, or by applying ideas from one context in another. For example, in Europe a student typically chooses an advisor before entering a program (i.e. enters to work with a specific person), whereas in the US the choice is typically made in the first year or two of the doctoral program. The US structure allows the student to explore her interests for a while before focusing; the European model obligates focus before the interests are developed. In Europe a student is formally expected to finish a doctorate in 3 or 4 years. In the US, 5 years is normal and 6 is fine. (If we are talking about star students at "top" places all the expectations change, but I am focusing on the "typical" students who will mostly become ordinary mediocre researchers.)
To some extent the differences in the structure of doctoral programs also reflect differences in the expectations for the postdoctoral phase. A "postdoc" in mathematics in the US means a temporary teaching position during which the person is expected to develop an autonomous research program. A recent graduate needs to be prepared for that level of autonomy. In Europe a "postdoc" in mathematics typically means a position directly connected to a particular research group, in which the student is expected to participate, sometimes in a still directed manner. A student entering such a position needs less autonomy a priori than a student entering a US style postdoctoral position, but may need considerable area specific knowledge to function usefully in an established research group.
Surely some of the characterizations in the preceding are wrong in the details, or when compared with any specific context (particularly vis-a-vis Europe), but what I think is valid is the claim that the structure of doctoral programs reflects ideas about the preparation of students entering such programs and the needs of students exiting such programs, and that such expectations are different in the US and Europe, even if it is hard to articulate exactly how without oversimplifying (the discussion includes dozens of national systems ...) Also the intellectual traditions in the US and Europe are different, and this effects the structure of doctoral programs. Again, a properly nuanced examination of what the differences are would require a book, but the general feature that seems most salient is the relative importance of breadth versus depth. Maybe also the differences reflect different ideas about the importance of developing exceptional talent and training/forming ordinary talent. To me the US seems more oriented towards the former, Europe more oriented towards the latter (this does not claim that one is more succesful than the other in either regard; intention does not imply results).

11 of 32 in mathematics, plus 3 in CS for me.  I previously taught a place where students were not allowed to count more than 13 (of 36) from any single subject towards graduation.

In the US tradition, a PhD does not simply indicate research ability; it also indicates a basic ability to teach all undergraduate courses (except perhaps upper-level electives) in the field.  There are many colleges in the US with only 2 or 3 math professors (in fact collectively these places hire more math PhDs into permanent positions than research universities), and a small department relies on all its members being able to teach all its courses.  (If the algebraist in the department has a heart attack, the applied mathematician will have to cover the undergraduate algebra course.)
In particular, this is why algebra and analysis are almost always part of the requirements; these courses are central to the undergraduate curriculum almost everywhere.