Is it normal to have grad student instructors for upper division classes? I'm almost done with my undergraduate degree (B.S. in a physical science) and, looking back, a good portion of my upper division major classes have been taught by grad students. Is this normal?
I hope the grad students are getting some sort of additional stipend for full-on teaching these courses in addition to their research since the department seems to lack professors.
Possibly pertinent info:
This is a public research university in the United States.

US News and World Report has data from 697 ranked colleges. Of these:

544 schools (78%) reported that no graduate students were the primary instructor of any course. These include all except two "National Liberal Arts Colleges."
The 10 schools with the most graduate students as primary instructor were all large public research universities. These 10 schools reported that 18-26% of their TA appointments were as the "primary instructor."

Purdue University is listed as the school with the highest fraction of graduate students as instructors of record. Their policy allows the department to assign courses to graduate students; it makes no reference to upper division or lower division.
As another case study, the University of California reports that "as students progress through their academic careers and enroll in upper-division and graduate classes, they receive more consistent exposure to full-time permanent faculty and smaller classes." More specifically, the policy at Berkeley allows graduate students to teach lower-division classes, but they do so under the supervision of a faculty member, who remains instructor of record. However, Berkeley does allow graduate students to be the instructor of record for an upper-division course, though usually in exceptional circumstances only.
Summary: about 80% of universities do not allow graduate students to be the instructor at all. Of the other 20%, most are large public research universities. There is less data about the courses to which these graduate instructors are assigned, but Berkeley is an example of one school that will "formally" assigns graduate students to upper-division courses but not lower-division courses.


Is there a standard system example for writing papers which introduce a newly developed programming language? I am writing a paper that describes a programming language that I am developing as part of my PhD. I think that writing the paper using an example can greatly improve how the paper is understood. Is there a "standard" example that is used in programming languages paper? A set of examples?

The biggest problem that you face when writing a programming language paper is explaining why the world needs another programming language, rather than a library or an API or something of the sort.

I would thus recommend skipping "standard" examples such as "Hello world" and recursive factorials entirely, and instead going straight to a set of minimal examples that illustrate the value of the new language.  Only in the case of a particularly extreme langauge will the standard examples be necessary to help understand the scientific concepts.

Note that documenting a programming language is an entirely different matter.


Methods to track behind cheating on exams using on-line sites? In the current state of the technologies, what ways can be used to track clear yet directly untraceable cases of academic misconduct specific to students who post questions from and view answers to a take-home exam using on-line (homework) sites?

My case is specific to Chegg, although answers related to other on-line sites would be certainly welcomed.

As a background, I have a list (from Chegg) of the email address, log-in times, university affiliation, and question posted / viewed. I can clearly correlate the posting information to being from my exam during my exam. I can directly correlate some cases because the email address is ... surprisingly ... the student's university email address or their personal email address. What I do not have in some cases is a) a valid or directly representative email address and b) a full name.

I have a roster list of students and have diligently checked their email addresses against the offending emails to no avail. I will pass the roster and list to our administration to review against their (larger) database of email accounts. However, I can only imagine that an email address could be from a neighbor or friend or roommate or ... someone entirely untraceable in the full university email database.

After a formal inquiry to obtain the above list, I have an email response (from Chegg) that tells me that I will get no further help to obtain any other information. I will be glad to hear about other levers that can be pulled to get better information.

I can accept that at some point I may have to conceed that I am simply out of luck to track the real perpetrator. But, before I do so, I have to wonder whether this community has insights to additional resources to help solve my problem.

To help also focus the discussion further, I am not interested to learn about the ways to avoid this problem in the future. I have my own insights and plans to move forward on this front. I am also not interested to spend time trashing Chegg or equivalent sites, deserving or not.

Finally, in addition to the specific question on additional effective methods, I am also interested in one other broader aspect of the problem. I am curious to hear from like-minded individuals who have initiated or are aware of actions being initiated for a larger academic community stand against the problem. I make this latter statement especially as we faculty are all most likely to have to face yet another round of on-line teaching for the coming Fall 2020 semester.

In summary, I hope to hear about approaches that others have used that could also help me solve my problem to track perpetrators of cheating who, up to this point, are essentially directly untraceable.

I can think of a few approaches that might help. I'm not sure pursuing the sites used will be very fruitful. There are always disposable emails they can sign up with, Tor exit nodes they can route traffic through etc. It's a fool's errand.

Oral interviews post-exam

Ask a random sample of students for an interview after they've submitted their answers. With the script in front of both of you, ask them about their thought process when answering the question and evaluate the extent to which they can confidently discuss the subject.

Cons: time-consuming, potential for false positives from students who forget most of the content after taking the exam/have other assessments to study for/don't cope well with the interview pressure.

Issue unique questions per student

If you have control over the online assessment platform, you may be able to issue unique questions per student to catch out blatant copy-pasting of the question text in order to publicly outsource an answer. I am not proposing that a paper is manually put together per student; this seems like something you'd want to automate.

Cons: the ability to do this well will vary from subject to subject. It might be easy to generate lots of linear programming problems of comparable difficulty if your exam e.g. assesses the student's ability to apply the Simplex algorithm. But, explanation questions may be more difficult and lead to a perception that some students unfairly get 'easier' or 'harder' papers.


Will my Professor tolerate this medical excuse again? A week ago In one of my classes I e-mail the professor and asked to reschedule one of my quizzes due to illness generated from sleep deprivation, she accepted and was highly considerate of my situation.

Now, due to my intense anxiety & stress of exams (to perform excellently) and lack of sleep for the last couple of days as well, my body is failing me and now I woke up to dizziness (fainting/falling sensation), fluctuating irregular heartbeat, and confusing feeling I can't focus on my work at all...

I'm going to the clinic as soon as it opens in a few hours and the exam is also in a few hours, I feel as if the professor will not believe me but as I visit the clinic to be sure this is not serious, I will ask for a medical note. Will my professor tolerate this again? In our syllabus an exam can be re-scheduled for medical emergencies. I believe this is a medical concern.

I maxed out my body for school too much, trying to do well and sleeping less (creating "more time").

Bear with the writing, I'm not in a perfect mental state...

Go to the hospital NOW and worry about the exam and your professor later.

If you tell your professor that you had to go to the hospital, she'll most likely just accept that. Of course, you can ask for that note nevertheless, just in case.

Once you are ok again, think about how you can reduce stress and get enough sleep regularly. On the long run, constant lack of sleep and stress will hurt you (even more than it already did so far).


Email addresses for research assistants What is the best email address for a Research Assistant (RA)?

I noticed that most of the RAs and faculty in grad schools use their first name only as an email handle, like: firstname@school.edu.

However, as far as I know, the "perfect" professional email address would be like: FirstnameLastname@school.edu.

Only few of them used this format but with a dot in the middle, like: Firstname.Lastname@school.edu.

I think it might be neater to use the firstname-only form, but is it still professional to use it?

I'm sorry for this silly question, but I would appreciate your clarification.

Most schools don't actually give you a choice: they simply set you up with a standard email address following their standard format.  Often this is some combination of first name or first initial plus last name, though I've even seen some schools give everybody a horrible alphanumerical mess as their official email address.  Many places will then also give you an option of setting up one or more email aliases, which you can use to modify the default.

Personally, I think that anything that includes your last name (and nothing else bizarre) is pretty reasonable and professional.  For my own case, for example, one can reach me in various locations with various forms of "beal", "jbeal", "jakebeal", "jacobbeal", and "jacob.beal", all of which convey the same basic information of "yes, this is who you think it is."

It is worth noting that, in some circles, being the one to get your first name or your initials on a system can be considered particularly special.  This is because it can indicate that you had been a very early adopter and/or involved in the system's development.  That's unlikely to be the case for most RAs at a university, however.


Is it ethical to watch recreational videos or listen to music on a portable work computer bought from a research fund? I am in my first year of a PhD in France. My adviser recently bought a laptop for me using the research funds on which the computational codes for my research work are installed. According to my adviser, I can take the laptop to my residence when I leave the lab.

  Is it ethical to listen to music, watch videos or movies I love on the work computer?

I am well aware that discussing with my team would be the ideal thing to do but I am not sure how my supervisor would take it. I cannot reach a conclusion of whether listening to music and videos helps me constructively in my research. One favorable justification I found is that listening to music helps me concentrate better, but I am not sure it is always the case. The reason is that at times some movies feel very motivational, music feels soothing but on some other occasions I find myself investing hours in them when I am supposed to complete a task. This may be purely attributed to my personality but I am unable to reach an answer.

Unless explicitly stated in the rules that they should have told you when you got the computer, you can do whatever you want that doesn't damage the machine or compromises your work. Common sense applies:

Playing a CPU intensive game at the same time you are crunching some numbers you need soon will slow your computations, so it isn't good. Playing the same game while the CPU is doing nothing else, should be OK.
Storing music or films in the hard drive is fine, but if you are running out of space for your data, they should go away.
If the university is providing some backup service, make sure you are not bloating it with gigabytes of your personal stuff. A few megabytes of configuration files of your own programs and other small things shouldn't be an issue.

One can argue that just by using it you are wearing off the battery, and that is costing your university money; but on the other hand, a new battery costs around one salary day, and if that is what it takes to keep you happier and more productive (even a tiny bit) for years, I think it is well worth it.

If you were to need the help of IT support (in my university is as good as nonexistent anyway), they may delete your personal files and programs if they think they are related to the problem, as  Todd Wilcox pointed out in the comment. If they were less understanding, you may even be told off for installing software that they don't know (mind you, it may even be research related!).


Hypothesis incorrectly stated and submitted I realise after submitting my dissertation lask week that I posed my hypothesis in the wrong manner.

I posed them as

  I believe that X will not be a predictor of Y

I subsequently went on with results which returned a p value of 0.01 and subsequently said we reject the null throughout my discussion.

It may be simply viewed as a typo. Am I likely to suffer greatly as a result of this mistake. I proceed with my methods, data analysis and discussion as if the hypotheses are proposed in the correct manner and given the overall premise of the dissertation and explanation of the result, it's quite clear what I am trying to achieve I believe.

Am I likely to simply be failed as a result of this error?

Any help greatly appreciated as I'm very as this has been submitted  and I am extremely worried

I'm hoping to be correct here, but this depends partially on personalities. Since you found the error, you should immediately inform everyone involved of the error and offer to supply a corrected version immediately. If people are reasonable they should act reasonably, of course. If you wait, and someone else notices it, you will have difficulties.

Getting things absolutely backwards is likely more common than you imagine. Also, when you write and then proofread your own writing, you are very likely to see what you want to see, what you think you wrote, rather than what you actually wrote. Most reasonable people will recognize that, have a bit of a chuckle and move on.

But you have to be the one to raise the flag.

You raise this issue on a week end. If at all possible, don't wait for the new week to inform at least your professor.

One of the reasons that books have editors and journals assign reviewers is to catch these kind of errors of inattention. Sometimes an advisor will perform that function for a student, but not always.


Is there a programmatic way to check that a journal submission follows a style guideline? Does anyone know of a programmatic way to check if an article submission matches a style guideline?

Submitters are likely to be humanists, so LaTeX is not an option. Assume the journal has a Word template they have provided - is there some way to write macros in Word that will validate a document against a template? Or, does OJS (Open Journal Systems - https://pkp.sfu.ca/ojs/) provide this functionality?

EDIT: Motivation - my main interest is in automating as much of the journal submission workflow as possible to streamline a possible new project, thanks! I figured having an automatic screen for "did they follow the style guideline?" could save human time :)

Instead of automating the checks, minimize them by reducing the guidelines!

The critical pre-submission guidelines are only those where papers that don't follow them would have to be severely edited in a way that will change the referee's opinion about the paper. This is, e.g. a draft that is twice the permitted length, or one where the section organization used is not permitted. Many newer journals are pretty good about things like this (eLife, for instance: https://elifesciences.org/inside-elife/a43f95ca/elife-references-yes-we-take-any-format-no-we-re-not-rekeying)

If I took guidelines 100% seriously, I would be spending a great deal of time not just on margins and formatting (mostly fast once I have a template), but on things like:

Does this journal use Fig., fig., or Figure?
Should subfigure captions be a) or A) or a or A or a or A
Should journal names be abbreviated?
Are the acknowledgments I wrote allowed?
Are footnotes permitted, or is everything an endnote?
Do figure captions have titles?
Is it appropriate to use a comma in an axis label (1000 vs 1,000)?
Is it Eq. 1, Eqn. 1, Eq. (1), ...

Eventually these have to be settled by work from the authors and the editors - but I've found that even different copy editors at the same journal can have different interpretations!

I view rejecting a submission for issues like this as being a waste of everyone's time. With an automated system, it might be less of a waste of time - but I think it also encourages journals to be nitpicky at a stage when it is not important.


Usefulness of research paper(s) of another discipline for PhD Consider the scenario in which a PhD student of the subject X published good research papers on subject Y. X and Y are not related. Will those papers any way useful for getting PhD or any benefits related to PhD?

It is unlikely that any papers in a different field will be used in assessing a PhD. However, as long as the papers are not too time consuming to generate, then the process (doing research, writing it up for publication, responding to reviews) will be useful for doing the work necessary to get your PhD. And more papers will always help in applying for postdocs.

Depending on the subject, a PhD can be more about learning to do research than obtaining subject matter knowledge. If you are in such a discipline, then research practice is useful. But it is counterproductive if doing the extra papers reduces your capacity to do research in your own field (particularly time).


How much of advantage do US citizens have in American academia? How much of a disadvantage are you if you are not a US citizen, but want to enter American academia? It seems Americans have lots of advantages starting early, including NSF graduate fellowships, continuing with more access to DARPA money, US-citizen specific awards like PECASE, etc.

True, some federal funding of grad students in the US is restricted to US citizens or permanent residents, but, in mathematics, most funding of grad students is as TAs (=Teaching Assistant) supporting lower-division mathematics courses.

The question of admission itself is mostly separate from federal citizenship requirements. Let me explain some things an admission committee wonders about, statistically, at least, about international applications, that would not be  relevant to an applicant already in the US and with no visa issues:

It turns out that quite a few applicants simply want to get to the U.S., in effect as informal political asylum, while having limited genuine interest in mathematics, and this creates visa and other complications subsequently. I have no objection to fairly open immigration policies, but I do object to adding that noise to applications to our graduate program.
Given that the bulk of funding is as TAs, problems with understanding colloquial spoken English cannot be taken lightly. This isn't about "cultural bias", but about the practical features of teaching in this environment.
Minimum funding to generate visa papers: The INS (U.S. "Immigration and Naturalization Services", which controls visas of non-US-citizens) mandates that we promise a certain annual stipend for international grad students... ok... except that that number is several thousand dollars above what the university sets as the ceiling for standard stipends. Not hard to imagine the craziness and injustices this leads to.

Edit: Theoretically, the university is not necessarily responsible for guaranteeing the (needlessly high) minimum, but different universities develop policies designed to keep them at some safe distance from "trouble with INS", and sometimes/often such policies create complications not originally intended, and not due directly to INS. (Also, interpretations and policies drift over years, and I'm refering to a situation from a few years ago...)
International students demand resources for their spouses.  We have repeatedly "discovered" that an admitted international student is married, "needs" a visa for a spouse, the spouse needs admission to something, etc.  It is easy to understand the desperation that leads to such situations, but it also corrupts things, and absorbs resources.

Edit: Yes, if things are done right, as noted in comments, it "should not be" the university's responsibility to "take care of" other family members. Nevertheless, for reasons I do not fully understand, it somehow has come to this on many occasions. (The quasi-legal details are mercifully slipping from my mind! :)
Many other college-university cultures have a hugely different emphasis on problem-solving versus memorization, and relation of teacher and student. Thus, although in one sense the U.S. system generates the weakest outcomes in specialty, because of all the other required coursework for "breadth", the balance of demands, and the goals, of upper-division coursework is mostly compatible with what happens at the beginning of grad school. Further, the less structured parts of the rest of grad school have counterparts in some undergrad projects. In contrast, we have found that several other traditions give students not prototype whatsoever for coping with ambiguity, and this can lead to strange crises well into a PhD.

Edit: Again, not to be claiming that all non-USA programs are the same! For this last point, let's say "traditions which emphasize memorization and reciting, of definitions, theorems, proofs, and examples", as opposed to problem-solving as at least a component of activities. It seems that getting into the habit of thinking that there is a "sacred canon" of results, even perhaps including wording, is not a good habit of mind. Similarly, an exaggerated impulse of "following orders" or "compliance", while a habit highly rewarded in some situations, is not so much the desired methodology after the very beginning of grad school. Thus, success in a prior situation may be misleading to a student, when further adaptation and growth is needed.

Don't misunderstand me: I understand how such things come about. Nevertheless, with limited resources, not only money but energy to help people get in sync with their responsibilities as grad students, there is a serious question about how to allocate. Thus, we can capsulize this by saying that grad school applicants from within the US system are more "known quantities", are already fairly in-sync with the system, are cheaper dollar-wise, and are rarely or never under the threat of returning in disgrace to a genuinely dangerous situation in their home country if we stop renewing their TA!

A small further edit: At a university receiving state funds (although this amount is ever-decreasing), the state legislature, and the electorate, take the viewpoint that the university has an obligation to give priority to students from the region, if not literally the state, and to serve the state's and region's goals. This has become less idealistic during economic downturns, unsurprisingly.

Edit: in response to some comments... I certainly do not claim that "all" non-USA undergrad programs are the same, "bad", etc., nor that all in-USA programs are "good". At the same time, I didn't want to single out specific traditions for criticism. And, certainly, applicants in the U.S. can easily "achieve" weaknesses for no particular reason. As I understood the question, it was inquiring about the realities of the situation, advantages that in-USA applicants tend to have over not-in-USA, at least in the minds of admissions committees.

I do not think literal xenophobia or cultural bias plays any role in admissions, in case people wonder. If anything, the generally stronger undergrad prep of people from non-USA programs has created a "bias" against US students on that criterion alone. The practical and prosaic considerations mentioned above diminish post-PhD, so that "nationality" plays no detectable role in post-doc hiring or tenure-track hiring. (For that matter, examination of the faculty at most "research" universities shows a pretty international population!)


Is it dishonest to omit publications in one's CV? The question in the title is motivated by recent comments on other questions on this site that were surprising to me.  Is there a precisely defined and widely accepted standard for CVs that would mean omitting one or more publications in a CV is dishonest?

Please base your answer on one or more references and not merely personal opinion.

Note that this question is about honesty, not about whether it is a good idea to omit publications.

Since there are no rules on how a CV should look like it will be hard to find references. There are also different preferences from different people, so you cannot avoid "opinion based" answers here.

In my opinion it doesn't look good if you just leave out a publication and make it look like those are all of your publications.
However, it is very unusual to include all publications in a CV once the number gets bigger. How many to include is up to you, most of the time I've seen around 10. To make clear that those aren't all of your publication there's usually the phrase "selected publications" and in that case you want to include the "best" or most relevant publications or a very representative sample of the things you did. So the important part is to actually tell the reader that it is only a part of the full publication record, but if you do so it is definitely no problem.

The guidelines from columbia university say:

  Publications - These can also be listed on a separate page, or you may include a distinct section called “Selected Publications.”

This is also true for awards, fellowships and things like that. Once the list get's very long you might only list the most prestigious ones.


Is it possible to do a Master's in Mathematics after a Bachelor's in Economics? I'm currently an Economics student in my second year (only just begun).I've come to realise that I'd like to study mathematics further (pure or applied) since I'm missing the subject ever since school ended. The maths in undergraduate Economics isn't too challenging so I'm not enjoying it too much.

There isn't too much flexibility in my university (I live in India and the education system is rigid) so I do not have an option to double major in Economics and Maths. Also, I will have two allied courses of Linear Algebra and Calculus as an add-on.

I've been a fairly good student in maths so far and I've already started to take on some of the undergraduate maths courses on my own. Would a good maths GRE score be sufficient to apply to a good university in the US? Could I also apply for an M.S. in Statistics?

If I were in your position, I would look at the requirements for admission to Master's programs at universities you're interested in and see if you can find a way to fit their requirements into your required curriculum.

For instance, here are the admission requirements at my university. I'm not sure if it's top-10, but definitely top-20. At this school a degree in math is not required for admission to grad school. It looks like they require up to an analysis course and some abstract algebra. This is likely only a few classes beyond your required calc+linear algebra classes for your economics degree.


Why is salary inversion a problem? I've heard that salary inversion is a problem in academia, and it happens when Universities continue to hire new and highly qualified people at higher and higher salaries, but they don't increase the salaries of the existing faculty at the same rates.

I want to ask - why is this actually a problem?  Shouldn't salary be based on merit and qualifications, not how long you've sat at a particular desk?

I want to ask - why is this actually a problem? Shouldn't salary be based on merit and qualifications, not how long you've sat at a particular desk?

Yes, it should.  But consider what happens if I've been sitting at a desk for ten years, doing hard work and feeling good about it, and one day you arrive, take the next desk, and have a higher salary than I do.  It is hard for me not to feel:

1) That my employer is less than grateful for my ten years of desk sitting, given that I (apparently; as you point out this does not logically follow) am being paid less than if I hadn't done any of it whatsoever.

2) That maybe I have been sitting at the same desk for too long.  It gives me the idea to follow your lead and earn more money by starting fresh at a new desk.

Thus: although one can say that the market is the market and its invisible hand is totally amoral, in reality this amoral practice is bad for morale.  And if you as an employer do happen to value the person who has been sitting there for ten years as much as the new guy you would hire -- or even if replacing the 10 year veteran would be disruptive to the workforce -- then you may have some interest in pushing back against this market force.  The desk-sitters certainly will, and depending on their mobility, you may want to take their feelings into account.  (In a sufficiently bad economy, maybe you can persuade them that they're looking at it wrong, and that instead they should be very happy about having ten years of steady employment.)


Why do a second PhD in a "neighboring" discipline? I recently received an application for a PhD position from someone who is already working as a postdoctoral associate. The difference in fields is not particularly large—I work in chemical engineering and materials science, while the applicant originally is coming from computational chemistry.

So the obvious question is, why would someone want to pursue a second doctoral degree when they already have one in a related field, and why would I, as a potential advisor, want to consider someone interested in doing this? (For instance, it seems obvious that the person wouldn't likely need as much training as a novice, but there would also be the challenge of trying to find such a person gainful employment afterwards.)

I've seen two reasons for this.  The more worrisome reason is the one mentioned by EnergyNumbers in the comments, namely that some people just collect degrees.  In particular, they may feel having two Ph.D.s would look exceptionally impressive, or they may be unable to settle on a specialization, or they may feel that getting another degree is somehow preferable to getting a job.  These could be legitimate as personal desires, but they are counterproductive for a research career.

The other reason is to recover from a bad career start.  Sometimes someone's grades keep them from getting into a good graduate program, or they face other constraints (geographical or financial) in their choice of programs, or they fail to connect well with their advisor.  They may eventually get a Ph.D., but not under good circumstances, and a second Ph.D. might lead to a much better career.  The difference between this case and the first case is that these students don't actually want a second Ph.D. as such.  They regretfully feel they need one to achieve their career goals, but they wish it had been their first and only Ph.D.

Recovering from a bad start is a less worrisome reason to seek a second Ph.D., but of course the challenge is figuring out which students have moved beyond their past difficulties and which have not.


Reminder email about acceptance letter A professor at a university replied to my PhD application email with a yes. I asked him further about his current research projects but haven't heard from him for nearly 2 weeks now; should I write him a reminder email? if so, how can it be in the politest way possible?

I would recommend sending out that reminder email.  As a prof, I get about about 20-30 emails per day (of course, many people get plenty more than that).  After a week if I haven't responded to an email it is usually not because I am ignoring it. Rather, it has been drowned out by hundreds of more recent notes and messages.  Reminders are welcomed -- in fact they tell me that a potential PhD student is both organized and values knowing about my research.

Perhaps a simple response like  'I am following up on my email from May xx, 2016. I assume you are very busy but have you had an opportunity to consider my request?' with your previous email included within the email as a reply.


Changing major in the graduate school I am a second year under-graduate student studying mathematics, but also taking courses in economics. I am not sure at this stage if I will continue with maths in my graduate school. My option B is economics. Roughly, I would give 60-40 in favour of math as far as my graduate study is concerned. My question is this:
Which major should I focus for doing internship and research while I am doing my under-graduate  before applying to grad school?

Today your interests lie in economics, but what if by the time you finish, you're interested in computational biology? Good luck selling Economics → Computational Biology to a graduate school admissions committee. On the other hand, with Mathematics as your base degree, you can cross over to most disciplines including Economics (with your chances boosted by further by internships in that discipline).

The bottom line is: the narrower your major discipline, the fewer your opportunities.


How should I proceed if the author of a paper doesn't reply to my query? During my literature survey for a project, I came across papers that have very interesting results and have decent number of citations. Unfortunately, the sub-area I am exploring doesn’t have any standard datasets on which I can test my implementation of the algorithm presented in their paper. The authors have generated the dataset by applying certain actions on a subset of publicly available information, which are Flickr images in my case.

So I have contacted the authors, asking whether they can provide me with their code or the dataset that they used for their implementation so that I may use it to perform my experiments and possibly try to extend their approach. But almost a week has passed by, and I haven’t received any response from them. Does that mean that,

My method of asking was not correct? (In that case, what would be appropriate?)
They don’t have the code or dataset available? (The papers were published in 2004.)
They are not interested?

Would it be polite to remind them again?

The reason for no response may be anything from what Anthony Labarre mentions to what you list. I would wait at least a month before writing off an answer; I have received answers even later than that. It is possible they are working on improvements themselves as well. In short, if you do not get a reply, you can only try again and perhaps express your request differently.

You seem to have contacted all the authors. Sometimes the project leader may be too busy to pick up on questions and the younger team members may be more inclined to answer. They may, however, want to talk it over among themselves, and it is probably not a high priority.

So try again in a couple of weeks. If you do not get any response, then there is probably not much you can do. You probably need to think about what can do to improve the chances for a reply. The word "collaboration" is sometimes a good way to "sweeten the deal". Sometimes, just to give a perspective, I get requests for data that are of the sort, give me this or that, give me everything, and I often end up asking myself, why should I? having laboured to generate the data. I am not saying you must follow such an approach to be successful but considering why the other researchers would want to help you may also help towards solving the communication problem.


What is the best way to start learning a new course? For learning new courses by one's self is it better

to read the related text
to attend online courses such as from sites like Coursera
to combine the two
or use another method altogether

In general, which is the better strategy for a motivated adult learner who is entering a new area?

There is no best way to learn a new course, some people better learn by attending classes and some other better learn by reading textbooks and reviewing lecture-notes and pamphlets. I know a person from my bachelors degree who did not used to come to classes. He just used to photocopy the lecture notes and read them before the exams. On the other hand, I used to always attend the classes and did not miss even one minute of them.

In order to learning a new subject, these come to my mind; but I think you should examine each and choose the best way which suits your own learning habits.

Attend some physical classes. Take notes and do the assignments. However, if you are not registering for the course, talk to the lecturer before the class.
Register for open course-wares and do online learning.
Search for the best textbooks in that course and try to read them.
Ask a friend who can help you in that course to teach you one or two hours a week. One of my friends asked his friend to teach him an engineering software two to three hours a week and now he can easily work with that software.
Group study with friend(s) who are learning that course, try to solve each other problems. This may also help you better stick to the course. People sometimes get bored when they self-study a topic.

However, depending on the level of the course you may need to choose wisely. If you are aiming to learn a post-graduate course, you may also need to read some papers and discuss your problems with a professor specializing in that branch of science, while some of the under-graduate courses do not always require to talk to a professor. You can ask your questions from a teaching-assistant, a friend with high marks in that course or even discuss with a friend who is also learning that course, or even ask your questions in a related online question and answer website or forum.


PhD in finance/econ vs PhD in mathematics I earned a Bachelor's and Master's degree in finance. Eight years later I am thinking of going back to school to earn my PhD. I am currently a high school math teacher but I want to be a professor at the university level, so I want to do a PhD. The question is which one is easier and faster? Finance, economics, or mathematics? I know finance and economics require less math and more practical experience and knowledge of business and economics culture (which is something I am lacking). On the other hand, finance and economics are in-demand fields. Has anyone been through the same experience? Please advise, thank you.

These are incredibly differently fields, and I feel like you should gain a much firmer grasp of these differences before looking into PhD programs in any.  It's also meaningless to consider which field is "harder", since there will be huge variation between different programs within each field, and between how individuals experience those fields.

Finance and econ USE a lot of math, but they aren't math degrees.  Between these two I would at least recommend econ over finance unless you know for certain that you want to work/research in finance.  Most econ degrees include some work in finance, along side other econ areas, so you can cast a broader net while still learning what interests you.  In my experience the majority of econ PhD programs require no practical experience, and in fact skew very heavily toward theory.  Econ is also not necessarily related to business - there are a lot of specialties under the "economics" umbrella.

Also, I think it's great your goal is to teach (I also like teaching), but your PhD will be very heavily focused on doing original research.  Do you also enjoy this?  Consider that strongly before embarking on this path, because completing your degree will hinge almost entirely on you being able to sustain a multi-year research project, while practically not at all on your ability to teach.


Asking to reattempt research after having a hard time initially Last summer I asked a professor if I can try my hand at doing research under him in his field of interest. As I was an undergrad student (I still am) taking taking summer courses and working (I wasn't getting paid for the research) at the time and lacked a lot of skills/creativity to help in with what he was doing, I feel like I was not very successful in being very helpful with putting together a proper research report.

This being said, nearly whole year has passed since then, with no plans this summer for courses, I have an idea I want to do original research that could be helpful to his field (I didn't find anything written on the matter, so I'm not 100% sure if it's a good one, but I sincerely think it should be tried if it hasn't to verify its validity for his field) and I in turn gain from his guidance, and possibly get a paid position with him.

Is it possible for me to get back to doing research with him after having a rough time last year? If so, how should I ask for it? Should I simply write an email asking if its possible for me to try doing research with him again or compose a formal research proposal and give email it to him to demonstrate interest in the field? The risk in turn of that would be that I would waste my time writing a proposal that would never be read or  rejected where my efforts should be elsewhere.

This is somewhat field-dependent, but a lot of undergraduate research is not really for the professor's benefit. If they needed help, they'd enlist graduate students or postdocs. Working with undergrads is more about giving the student a learning experience.

You don't say anything about feedback from your professor, but in any case, I see nothing wrong with asking to work under him again. As for your idea, it's definitely a good idea to run it by your professor before investing much more effort, as he is in a better position to judge whether it's feasible, whether it's been done before, etc.


How do I write a good abstract? So, I'm sitting here, rewriting the abstract of a paper I hope to
submit shortly. The abstract I had was old and not very good.

I have on good authority that an abstract is an important part of a
paper, because it is often what makes a reader take a closer look at
the paper.

So, how does one write a good abstract that will make people take a
closer look? What are things to include and things not to include?
What are common mistakes, if any, to watch out for?

By way of example, here the abstract I am rewriting, in its current
form. This is for an applied statistics paper. Feel free to critique.

  We describe and implement a method to select a Bayesian model for
      a collection of DNA sequences. This method assumes the DNA
      sequences are generated from one of a particular class of
      distribution models. These models capture long range correlation
      structure among the sites of the DNA sequences. We choose a model
      from the class by using a simulated annealing search algorithm
      with a scoring function based on the prior predictive distribution
      corresponding to the model. We apply this method to model human
      and mouse Recombination Signal Sequences (RSS). We use the
      posterior predictive distribution corresponding to the model to
      predict which of a larger group of sequences are RSS in the
      context of a cross-validation setup.

ADDENDUM: Please comment on whether there is a standard abstract length limit in your field, unless it is journal specific.

EDIT: Here is a second attempt at an abstract. This is based partly on the discussion at How to Write an Abstract, which is a nice discussion of the main points to think about. The results teaser at the end is a little unorthodox, but it is intended as an inducement for people to look further at the paper. @F'x, do you have any feedback?

  Given a specialised set of DNA sequences, it is a biologically
  interesting problem to predict which members of a larger set of DNA
  sequences belongs to that set. In this paper we consider the
  particular example sets of Recombination Signal Sequences (RSS).

  Problems of this kind are commonly addressed in the biological
  literature. However, we approach this problem by selecting a
  Bayesian model for this specialised set. This is an approach that is
  rarely used in this context, but as we show, can give good results.

  We select our model from particular class of distribution models.
  These models capture long range correlation structure among the
  sites of the sequences. We choose a model from the class by using a
  simulated annealing search algorithm with a scoring function based
  on the prior predictive distribution of the model. We apply this
  method to model human and mouse Recombination Signal Sequences
  (RSS). We use the posterior predictive distribution of the model to
  predict which of a larger group of sequences are RSS in the context
  of a cross-validation setup. We summarize the results of the
  prediction in figure and tabular form, showing good results. Example
  statistic: out of 700,000 candidate sequences, 30 to 50 are actually
  RSS. The algorithm ranks these, in descending order, by how likely
  they are to be RSS. It ranks almost all the RSS (90\%+) in the top
  100.

One of the things that is too much overlooked is that an abstract is read by a more diverse crowd of people than the rest of your paper. So, you have three different goals:

Give a take-home message to people who aren't interested enough to read the full paper.
Convince the undecided to read it.
Make it easier for experts to find it.

Nowadays, #3 is not so much of a constraint: Google and other search engines allow for full-text searching. So, focus on #1 and #2: be concise, crystal-clear, attractive.

Enough with the generalities. I love to critique, so I'll give it a try: I'm not in your field, which I think makes me a good test reader for your abstract, but also makes it harder for me to actually suggest actual changes to it. Anyway…

I think it's decent: no catastrophe, but far from enthusiastic. My main comment would be that it lacks a clear statement of the broader issue you are trying to address. You could start with it, something like:

  The past few years have seen large advances in the statistical modeling of DNA sequences, mostly based on genetic algorithms. In this work, we explore the efficiency of an alternative and simpler route, and show how to efficiently choose within a class of distribution models.

I have filled in with semi-random keywords/buzzwords, just to give you a sense of how to achieve it. The idea is to put your findings in perspective:

Roughly, what subfield are you working in?
What is the main trend in this field?
How do you position your work with respect to this recent research?

Then trim down the rest of the text:

  We showcase a method to select a Bayesian model for a collection of DNA sequences, generated from a specifically chosen distribution model capture long range correlation structure. We choose the model based its prior predictive distribution. Applying this method to model human and mouse Recombination Signal Sequences (RSS), we predict which of a larger group of sequences are RSS in the context of a cross-validation setup.

(I hope I did not lose or betray some of the meaning, it's hard when you don't get the finer points of the text… but you get the idea anyway!)


Where and how to search for research problems? Where and how can an independent researcher search for research problems in a paticular field, assuming that a person doesn't have direct contacts with anyone knowleageable about that field?

And once you get a research problem , how can you get an idea about the approximate time that will be required to solve that problem? My area of interest is mathematics.

Reading recent papers is a good way to come up with ideas for things to work on.  For example, the last section of a paper often lists open problems and possible topics for future research, and they may also be scattered throughout the paper.  Of course the author or other readers may be working on them, but that difficulty is unavoidable for any problem that isn't communicated privately to you.  Some of the advantages of this approach are:

You have some evidence that the answer isn't already known.  Of course maybe the author just didn't know it, but at least you are getting an expert opinion (which is particularly helpful if you aren't an expert yourself).
Your work may be of interest to other readers of this paper.  This avoids the difficulty of making up a topic and then discovering that you are unable to interest anyone in it.
The published papers on the topic let you calibrate your level of knowledge.  If you can read them, then you probably know enough to work on extensions.  If you can't, then you need to learn more.
There's some reason to think progress may be possible.

By contrast, I would absolutely avoid working on famous problems.  They satisfy 1 to 3 nicely, but the "famous" requirement specifically filters out any reasonable likelihood of a full solution, and progress towards a solution may be very difficult.  Unless you are extraordinarily talented or lucky, choosing problems because of their fame is a big step in the direction of becoming a failure as a researcher or even a crackpot.  Even if you are extraordinarily talented, there's no harm in starting with a warm-up goal, and this avoids the difficulty that many people have trouble estimating their own abilities.

As for how long it will take to solve a research problem, this is unanswerable.  If you are really lucky, you might make important progress within a few weeks.  If you get stuck in a rut or are missing some background, you might work fruitlessly for years on a problem that's not actually all that difficult.  And of course problems vary enormously in their difficulty.  With enough experience, you might be able to estimate how difficult or time-consuming certain problems might be, so you could guess what might make an appropriate Ph.D. thesis problem, for example.  However, even experts are sometimes wrong, and developing this sort of feeling takes substantial research experience.  When you are starting out, I don't think there's any reliable way to guess these sorts of things.  This is one reason why Ph.D. advisors are important: they can offer feedback and advice based on intuitions the student is still developing.

If you are working on research without experience or expert guidance, you could use the following guidelines.  Don't give up too quickly: anything worth publishing is worth spending weeks beating your head against with no apparent progress.  (Of course I don't mean staring at a blank piece of paper, but trying ideas and discovering they don't work, looking at special cases and examples, studying background that may be relevant, etc.)  Once you have a solid background in the relevant mathematics, which could take a long time depending on the field, you should probably be getting somewhere over a period of months.  By "somewhere", I don't necessarily mean clear progress towards a solution, but you should be able to articulate an understanding of the problem you didn't have when you started, you should be coming up with tangential or spin-off ideas that may not solve the problem but could be interesting in their own right, etc.  The ultimate test of successful research isn't whether you accomplish your original goals, but rather whether you find something interesting along the way.  On the other hand, if months go by and you don't seem to be coming up with any interesting ideas or understanding, then this is probably not a fruitful research topic with your current level of background and experience.

Of course you shouldn't take any advice like the last paragraph too seriously.  Research is a highly personal topic, and many people have different research styles.  However, it may give you an idea of one reasonable approach.


How to cite an authorless report in JabRef/Bibtex I am learning to use bibtex/jabref in the context of a literature review and would like to cite the following report:

https://www.army.mil/e2/c/downloads/379567.pdf

The report is compiled by the USACE (US Army Corps of Engineers) and does not have an explicit author. In the summary report it is mentioned that this report was a result of "workshops" with a "panel" of experts. It includes the expert's names however no indication whether they actually contributed to writing the report itself.

The way I'm logging this into JabRef is as follows:

@TechReport{USACE2015,
  author      = {{US Army Corps of Engineers}},
  title       = {North Atlantic Coast Comprehensive: resilient adaptation to increasing risk physical depth damage function summary report},
  institution = {US Army Corps of Engineers},
  year        = {2015},
  file        = {:10A_PhysicalDepthDmgFxSummary_26Jan2015.pdf:PDF},
}

However, I'm a bit annoyed that I have to mention USACE in two distinct required fields, namely the author and the institution. Is there a more correct way to cite "authorless" technical reports using bibtex/jabref? Perhaps this is not a technical report at all?

Thanks in advance for your help

In the end, you should format your citations according to the style guide of the journal you are targeting. Which BibTeX fields you have to use depends on the bibliographystyle you are using in your document.

For example, IEEE journals require that the name of the institution appears after the title of the report, see here on page 7.

With the ieeetran bibliography style, the author field is printed before the title, while the institution field is not printed at all. In order to fit the journal style (institution name after title), one needs to remove the author field and enter the institution in the type field, which is a bit of a hack:

@TechReport{USACE2015,
    title       = {{North Atlantic Coast Comprehensive: resilient adaptation to increasing risk physical depth damage function summary report}},
    type      = {{US Army Corps of Engineers}},
    year        = {2015},
    file        = {:10A_PhysicalDepthDmgFxSummary_26Jan2015.pdf:PDF},
}

produces the desired output:

In short: check the citation style of your target journal and try to mimic that. If you also want JabRef to sort your references properly by institution, you will also need to include the institution in the institution field.


When citing web sources, should I give the URL within the text itself? When I am citing web sources, should I give the URL within the text itself?

Usually, web sources have an author and a publication/creation date as well. These you can add in-text, and then put Author, Year, Title, website and retrieval date in the reference list.


Google scholar search query issue Last year Economics of Governance (Springer) published an article that I co-authored  titled "Political determinants of fiscal transparency: a panel data empirical investigation".
Google Scholar  appears to find the article only if I search for the complete title or search for political determinants of fiscal transparency. If I enter any other combination of words of the title or the keywords  in the search query the article disappears from the results.
Even more strangely, Google Scholar does not find the article when I look for political determinants fiscal transparency. How is it possible that the word of makes such a difference for Google Scholar?

Thank you!

P.S. I wrote to the journal and they replied that this is how it is supposed to work, but this answer is not really convincing.

I don't think it disappears: it's probably just ranked low in the general query results.

If you restrict the search with keywords "political determinants fiscal transparency" to "results since 2017" (https://scholar.google.com/scholar?as_ylo=2017&q=political+determinants+fiscal+transparency&hl=en&as_sdt=0,33) (note the "as_ylo=2017" in the URL), your paper is ranked first.


Can human subjects be paid using a researcher's personal funds? I want to conduct some research and will apply for IRB approval. But, finding money to offer incentives for respondents may be difficult. I do not mind using personal funds as the total amount is very low.

Is there any ethical issue with using personal funds for research?
Do you know of any IRB related regulations that address the above issue?

If it matters I am at a US educational institution.

I've sat on an IRB panel and we've never been concerned about the source of funds for participant gift-cards.

p.s. Note I'm an ethnographic fieldworker and I often buy small gifts for the families and individuals who participate in my projects. It's too much of a pain to pay for these out of my research account (why do you want 15 stuffed bears and keychains?) so I buy them out of pocket.


How to make your small ideas into a research paper? I am a computer science PhD student who works mostly in theory. I always have multiple ideas in my mind about the research problem I am working on and also on problems which are related to my research. Many times my ideas were vague and not important, and many of my ideas have led to algorithms which are not in themselves enough for a paper. In the last couple of years I have given many ideas to my labmates and others. My colleagues say that some of these were very good, but so far, I have not able to find anything so big that it becomes a paper. Is there any way to makes my ideas more valuable and more significant?

Question: How to make your small ideas into research paper?

One of my biggest regrets is not writing down small ideas. There were waiting for a better time. But usually this time never came - either because I didn't find a good way to expand on the project, or because new projects left little time.

In There are no projects like side projects (which I wrote after finishing my PhD) there is:

  If you have a great project, do moonlight. Don’t wait for better times, because they won’t come. Maybe you overstate the need of money, institutional support or social confirmation?

Depending on the project, it can be a small draft (e.g. with a theorem and some comments on possible approaches to prove it) or a GitHub repository with code and some description.

Once something is shared, there are two main benefits:

you can talk about it / send it to your colleagues,
you made some progress, even if it is a small step, it's strictly more than nothing.

Occasionally, during the writing phase you may:

see how to expand on it,
or realise that actually you already did a lot,
or discover that it is flawed (e.g. your proof is trivial or incorrect).

Additionally, if a few years later someone ask you - you will be able to recover it quickly, along with its core content (formulae, citations, etc).
Sharing means that you can share it with your future self.

Is it just a one-page LaTeX note? Just write it.


What is the difference between a plenary and a keynote talk? I am organizing a workshop and have invited a featured speaker to give a synthetic talk. What is the appropriate term for this role?

They are both used (often interchangeably in my field). A plenary is a talk which does not have anything scheduled against it. A keynote is an invited talk in a conference or session.


Contacting potential PhD advisors while not knowing research topic? In December I will finish a Computational Sciences M.S. degree and plan to pursue a PhD in Applied and Computational mathematics afterwards. I have a B.Sc. in Applied and Computational Mathematics so have some exposure to the field but my Masters degree has largely been focused on Data Science and Machine Learning.

I would like to reach out to potential advisors over the summer whose research interests align with mine but I'm having trouble doing so in a meaningful way. Based on my coursework I would like to study math modeling/stochastic processes, numerical methods and efficient implementation of numerical methods, and/or PDEs, as these are the topics I found most interesting during my B.S. and M.S. degrees.

Most answers to this question indicate I should have a research question in mind before reaching out but beyond stating my interests as I've done in the previous paragraph I don't really have anything to go off. I've brought this up to a mathematics professor (who served as an advisor for my undergraduate math research) in the department of my current university and they agreed that mathematics papers are very difficult and time consuming to read unless you're up-to-date in that particular field and probably in that niche area of the field due to their density.

My question is how can I come even close to a research question prior to applying to PhD programs when it would take me an incredible amount of time to muscle through even a small fraction of the work by a single professor, let alone many professors from many universities? Do I need to fully understand the mathematics in their papers before I can completely determine the advisor is right for me? Is it OK to reach out with less information than an exact research question?

This is in the US, if that information is pertinent.

You definitely don't need super specific research topics to reach out to potential advisors to gauge their interest in working with you. When I first applied to PhD programs, I also got in touch with potential advisors beforehand to introduce myself and tell them about my research interests just to see if they would be responsive/be interested in working with me should I be admitted. At the time, I had a couple of ideas about what I wanted to do for my dissertation, but they weren't fully formed at all. Still, I did get some positive responses from several professors which encouraged me to apply to their programs.

At the end of the day, they know that most students don't join PhD programs with well-developed research agendas, that research interests change, and even that the person you wanted to work with originally might not be the person you end up working with in the end (the last scenario happened to me in some very ugly turn of events). Point being, I wouldn't worry too much about presenting developed research questions in the initial emails to faculty. If anything it's just a good way to get an idea of professors' availability and responsiveness, and to narrow down your list of schools to which you're applying.

And actually, given the above reference to my own negative experiences post-admission, I would add that it doesn't hurt to look for programs that have at least a couple of potential advisors to choose from.


The effect of a PhD on a student's interest in pursuing a career in academia Is there any research/study that looked at the effect of a PhD on students' interest in pursuing a career in academia?

E.g. it could survey students before entering the PhD program and survey the same students on their last year of their PhD program to analyze how interest in pursuing a career in academia has changed.

I am mostly interested in the field of computer science (machine learning / natural language processing), in the United States, but I am curious about other countries and other fields as well.

There have been some studies to examine whether graduate students’ career aspirations change over time.

Gemme & Gingras (2012) surveyed 294 students in the sciences (including health sciences) and engineering at French-speaking universities in Quebec regarding career preferences. Students were surveyed in 2003-2004, and again 2006. They were asked if they would like a career at a university (academia) or in a range of non-academic settings (industry, government, etc). They found that 30.3% of students desired a university position at both time points, and 17.3% of the sample initially expressed interest in a non-academic career, but wanted an academic career by 2006. Thus, a total of 48% expressed an interest in an academic career by 2006. In contrast, 43.9% expressed a consistent interest in non-academic work, and 6.8% shifted from academic to non-academic career interests. The authors noted that those students who shifted towards academia also were more likely to have received scholarships from their institution by 2006. They suggest this may have encouraged those students to consider pursuing a career in academia.

Another study surveyed 469 basic biomedical science graduate students at UCSF regarding career choices (Fuhrmann, Halme, O’Sullivan, & Lindstaedt, 2011). They compared responses of first year students to those in their second and third years, looking at overall trends for career preferences, rather than looking at the same sample at multiple time points. They noted that early in graduate training students indicated a marked drop in interest in pursuing research careers, but this applied to only specific types of careers. Interest in being a PI in academic (research/teaching) or “other” reseach careers (government, industry, or non-PI in academia) did not change significantly. However, there was a steep and significant drop in those students who wanted to be a PI at a research-intensive university: 41.7% of first year students, 35% of second year students, and only 25% of third year students. The authors note that at by their third year, the students would have spent 8-10 months in a single, research-intensive lab and would be better able to evaluate that career path. Congruent with this drop in interest in being a research-intensive PI, there was increased interest in non-research career paths related to science (e.g. business, law, writing, consulting, etc).

Fuhrmann and colleagues (2011) also found that when students were asked about their top career choice, 75% of second years wanted a research intensive PI position, while only 55.2% wanted it by their third year. Interest in PI positions with a mix of research/teaching had a smaller drop in that time (56.3% to 44.8%). Also, while women showed less interest in research-intensive PI positions initially compared to men (21.3% compared to 39.5%, respectively), both sexes lost interest in research-intensive PI positions at an equal rate as time progressed. Of those students who indicated a move away from a research-intensive PI track, 91% identified at least 1 negative perception or reason for the shift. This included negative beliefs about work-life balance, career competition, research funding, length of training, and salary for the research-intensive PI track. In contrast, only 24% of students identified at least 1 positive reason for picking a new career path, such as developing new interests or skills.

Golde & Dore (2001) conducted a survey of several thousand doctoral students across 12 disciplines. In one analysis of 4,114 students, they found 35.4% of respondents reported a decline in interest in becoming a university professor since starting their program, while 21.1% reported an increase in interest;  47.9% still listed it as a strong current interest. In contrast, 39.8% of students reported increased interest in conducting research in the private sector, 34.8% reported an increased interest in conducting research at non-profits or for the government, and 39.4% reported an increase in working independently as a consultant or writer. A subanalysis (n = 2505) found that concerns about the tenure and promotion process, academic job market, work load, needing to obtain research funding, and salary levels all decreased interest in pursuing an academic career. Individuals who enjoyed many aspects of university/graduate work were more likely to be interested. The authors also found many quality of life variables (such as work life balance) had an ambiguous influence on academic career aspirations.

The largest study I identified surveyed over 8,000 graduate students on 9 of 10 UC campuses (Mason, Goulden, & Frasch, 2009). The authors specifically asked about students’ interests in the “academic fast track,” defined as tenure track positions in research-intensive universities, not in academia more broadly. They observed a decreased interest in the fast track over time. While 45% of men and 39% of women reported they had been interested in  the fast track when they began their program, only 35% and 27%, respectively, expressed interest at the point of the survey (this corresponded to 1-7 years later in the program, depending on the student). As with the study by Furhmann and colleagues (2011), Mason and colleagues (2009) found little decline in the percent of students interested in academic careers that focused on teaching, and saw increases in students looking to careers in industry or government.

When Mason and colleagues (2009) restricted their analyses to STEM (science, technology, engineering, and mathematics) fields, they found an even sharper drop. While 40% of men and 31% of women in STEM initially intended to pursue a fast track career, this dropped to 28% and 20 % respectively at the point of the survey. Across disciplines, both men and women reported concerns related to having other life interests and negative experiences as a graduate student as reasons for moving away from the fast track. Women also mentioned concerns about having children, geographic re-location, and issues with their spouses/partners. Men moving away from the fast track also indicted that the career path was “too time consuming” and placed a greater emphasis/concern on compensation and promotion than women.

Overall, most of the studies found decreases in the percentage of students considering research-intensive academic careers, with less of a shift for those interested in teaching. All of the articles indicate that despite these shifts, and despite the fact that only half of individuals with PhDs are in tenure-track positions five years after graduation (Golde & Dore, 2001), few graduate programs educate students about opportunities outside academia. Most of the articles express two concerns: 1) that there are not enough academic positions for the students who want them, and 2) in some cases talented students who could make important contributions in research are leaving it entirely because of the quality of life issues inherent in the current academic structure.

Fuhrmann, C. N., Halme, D. G., O’Sullivan, P. S., & Lindstaedt, B.
(2011). Improving graduate education to support a branching career
pipeline: recommendations based on a survey of doctoral students in
the basic biomedical sciences. CBE-Life Sciences Education, 10(3),
239-249.
Gemme, B., & Gingras, Y. (2012). Academic careers for graduate
students: a strong attractor in a changed environment. Higher
Education, 63(6), 667-683.
Golde, C. M., & Dore, T. M. (2001). At Cross Purposes: What the
Experiences of Today's Doctoral Students Reveal about Doctoral
Education. http://www.phd-survey.org/report%20final.pdf
Mason, M. A., Goulden, M., & Frasch, K. (2009). Why graduate students
reject the fast track. Academe, 95(1), 11-16.


how to show I fit in with the department during faculty interview A commonly asked question during the faculty position interview is

  How do you fit in with the department?

It is quite similar to but different from

  How do you fit in with the position?

I want to seek some specific suggestions as to answering this question, assuming the position to interview for has a 50-50 teaching and research appointment. What is the hiring committee really looking for from a candidate from this question?

In my previous interviews, I would answer this question by focusing on how I would collaborate with other faculty in the department. It seemed that the answer did not hit the point well.

The other answer has good advice. But for a job with a research component, I'd interpret it at least partly as "How does your research synergise with our own?".

Point out things that you could work together with existing staff on; areas in which you could help each other out, and things where the combination of your skills/knowledge and their skills/knowledge might combine to produce something bigger than the sum of its parts.

Similarly, but less clearly, on the teaching side you might be able to identify how you fill a gap in their existing areas of expertise.


Should honorifics be included in acknowledgments? In the “acknowledgments” section at the end of a paper, I currently have a sentence saying:

  The authors thank Bob Johnson for discussions and insightful comments

However, I wonder: Bob’s full name is Robert Z. Johnson, he is a professor and a fellow of the Royal Society. Should his title be used? What about post-nominal letters? And middle initial? Finally, is it “Bob” or “Robert”?

I initially went with “Bob Johnson”, because that’s how everyone calls him (heck, his academic webpage is title “Bob Johnson”!). But is that appropriate?

In general, when in doubt, you should use the name of the author as it appears professionally. If the person in question publishes under a particular name, you should use that. The only exception is if you and the author have an existing relationship outside of this, in which case it is appropriate to use a less formal name.

You do not normally need to include honorifics ("Prof.", "Dr.", etc.) unless specifically asked to do so.

This leads to the final point: if you have any questions about how someone should be credited in an acknowledgment, ask. They'll generally be more than cooperative.


GRE Scores for Graduate programs in Chemistry I am planning to apply to PhD programs in Chemistry this year so I took the GRE last week and my scores were the following:

Verbal - 161 - 88%

Quantitative - 154 - 55%

(I still don't have the scores for the Analytical Writing section)

As a foreign student, I am not familiarized with the American admission system for graduates and I'm not sure if this score is sufficient (alongside with my CV, which is not that extraordinary) to guarantee acceptance in north american graduate programs.
In your experience, should I retake the GRE and try to obtain a better score or this should be sufficient? Please note that I am not aiming super top institutions like Harvard, MIT, etc, a decent normal PhD program will be a huge improvement for my career already, since I come from a country with substandard research in chemistry.
I looked up in some institutions and it seems that the quantitative score is acceptable (verbal seems to be ok for chemistry programs), however, some institutions put the lower limit to 155 on the GRE, which is higher than my score.

Thanks in advance

PS.: I would like to retake the GRE, but I'll only do so if I judge it is extremely necessary, since it costs $215 and where I live it is a great amount of money

While I am not in Chemistry, I can tell you from a US perspective that there is no such thing as a GRE score that guarantees acceptance. As long as you are above any "minimum score" (which most places are moving away from, but many admission committees reportedly still privately use), it guarantees your GRE won't alone prevent your file from being considered, but that's really it. Even a 99% on all sections is no guarantee of admission to anywhere I've ever heard of.

In general, higher scores are of course better, but the general advice is that retaking the GRE should only be done if you have a good reason to think you would do very differently. I retook the GRE once after a month of brushing up on some of the algrebra and geometry I had not touched in years, and it still only changed my quantitative score by just a few points. The only reason it wasn't a total waste of money is I didn't have to wonder, "gee, if I had just taken it again, would I have done much better?" I got the answer, which was no, I would not in fact do better without a lot more work that I didn't actually want to do :)

The GRE overall is pretty good at test-retest reliability, and if you prepared reasonably well (went through a practice test or two, were familiar with the type of questions being asked, were not very rusty on the topics covered) it takes considerable effort to do substantially differently. However, you can test this out for free in an afternoon by looking up the ETS free POWERPREP software (from the makers of the GRE), which allows you to simulate a full test at normal testing conditions, in the comfort of your home. If you follow the test protocol on your own (no outside source, only blank paper and a pencil, timed breaks with no food, etc) and the estimated score is not wildly different than what you got on test day, then you just have to decide if it is worth spending many hours of intense preparation to try to get better at taking the test.

Regardless of what you end up choosing, don't let a focus on the GRE score prevent you from making the rest of your application as good as you can. A well written statement (edited and reviewed multiple times) and working with your letter writers to get good letters (delivered on time) is essential, while extra points on a test are just "nice to have".


Should startup-company founder accept peer-review requests? I'm a relatively recent graduate putting together a startup company, and I've just received an offer to peer-review a paper for a reputable journal.

As with prior questions on SE.Academia, e.g.

As a Ph.D. student, should I spend time reviewing papers?,
Personal repercussions for those who actively ignore review requests?,
What benefits are there to peer reviewing papers?,
I have been invited to peer review a manuscript for a reputable journal. This is my first so I have a few questions to the more experienced,

I'm basically asking if it's important that I accept.  The difference in this question is that I'm not currently employed by a university, nor do I foresee that changing any time soon, so I'm less concerned with something like a faculty activity report and more concerned with maintaining professional connections.

While I believe that it's virtuous to help build the human knowledge pool, my primary concern is a bit more pragmatic in if I should be focused on building professional connections.  Realistically speaking I'm not currently too concerned with publishing papers, though I may yet need to when it's time to explain to the world exactly what technology my startup's based on.

However, a time sink's still a time sink, and I can't afford too many distractions right now.  I'm also unsure about precisely what I'd be committing to, making it harder to judge how much of a distraction it might be.

Other considerations:

The peer-review request came in on a private email address, so I assume that someone I know well recommended me.  It's a pretty safe bet that both the editor and the person who recommended me are aware of my professional situation and considered it when making the recommendation/request.
The editor has strong connections in a field where I have a patent for a technology that may yield significant royalties if it's adopted.  As a complex technology that I don't currently have time to personally champion, having other experts in the field recommend it could be the difference between investors using it vs. going with a less-effective-but-simpler solution advocated by competitors.

Questions:

Should a startup-company founder be concerned with maintaining a reputation as a peer-reviewer?
In general, is serving as a peer-reviewer important to researchers outside of academia?
Are there potential benefits/hazards to accepting/rejecting a peer-review request?

Context information (if relevant):

The peer-review subject matter isn't closely related to the startup company's core technology, though one expected basket of consumers of the company's core technology is likely to care about the peer-review subject matter.
I haven't officially done a peer-review before, though I've provided unofficially commentary upon request for those that were serving as peer-reviewers.

I think you should not take up this review, based on three reasons:

(1) No longer 'peer' : As an entrepreneur, you engage with technology, products and marketability more than academic research, thus defeating the purpose of peer-review, where researchers review papers in their field of work, and ideally get reviewed equally when they publish. The exception to this is if the journal is a technology journal focusing on product development and you have been asked to review from that perspective.

(2) Resource availability : To review a paper well, you will need access to related work in the field for judging correctness/suitability, possibly you will need to follow up some references, and more importantly, you will need to have time and current knowledge of published literature in the field. Unless the start-up is research-oriented, I think these resources may not be very accessible.

(3) Potential conflict of interest : This may not apply to the present case, but if this repeats, you may find yourself in situations where your review could be biased (eg. the paper reports flaws in a competitor's technology), and this would be an avoidable ethical burden.

Finally, the overbearing message I get from the question is that you are considering review because it could potentially benefit your product (through professional network etc). IMO, this is neither good (review should have a share-and-grow ethos) nor particularly effective- your network can be built better through more conventional routes, and also publishing a paper may not reach a big chunk of your target consumers (unless you are targeting the research community, in which case many of these points are invalid.).


Applying same graduation school with my girlfriend, is it ethical? Me and my girlfriend are currently studying in the same university and same department. We both applied couple of master's degree programs abroad.

One of the programs is in both our interest and she already got accepted for that program. I want to apply for the same program too but there is no question regarding your spouse / girlfriend etc. Should I notify the program about this in my application?

At best, that information would be (should be) irrelevant to them. You want to be accepted on your own merits in any case and a fair evaluation will, hopefully, assure that.

But you also need to be flexible in planning your own future. For example, if there are other universities nearby, it would be good to also make application there. Then, if you don't get accepted into the same place, your relationship can still survive.

But a relationship can, if strong, still survive a couple of years of separation.

But give it your best shot. Riding someone else's coattails isn't a strong point in your favor and people are unlikely to change relative rankings of candidates for such reasons.

And yes, it is ethical to apply to the same programs, if that is your real concern.

Let me give you a worst case scenario, though it would be unethical and I doubt that any university would follow it. But suppose you tell them and they decide not to accept you and think that she, then, won't come either. They then rescind her offer and offer it to someone else. Some programs, notably in medicine, need to fill every slot since it is expensive to run a program partially populated.

I seriously doubt this could happen, but I recommend against inserting wild cards into the decision process.


Which LaTeX template to pick if I want to write a paper that will stay unpublished? I'd like to choose a scientific paper template in LaTeX for a Deep Learning (Artificial Intelligence) project. I think the AAAI or the IEEE are interesting, however, I'd like to know under which terms I can use their templates if my work is not intended for publication. (I am also open to suggestions of other templates or styles to consider.)

Virtually all templates available as packages on the Comprehensive TeX Archive Network (CTAN) are free to use, regardless of whether or not they're intended for submission to the journal for which they were created. If there are any specific restrictions for their use, they would be indicated somewhere in the package information.


Does mathematical modeling expert need to know subject of interdisciplinary field I'm working in chemical engineering field and being mathematician and programmer I'm conducting simulations and mathematical modeling problem solving. Basically the battery processes simulations. There is a problem with my chemists colleagues who can't really help in theory regarding the processes inside of batteries that needs to be modeled. The question is, is it my responsibility to learn chemistry, electrochemistry etc in order to be able to build the math model of the processes by myself not relying on chemists? What is the general practice in this case in the world? Or do people in other research groups and/or universities hire mathematician-chemists specialists for this purpose?

I'm kind of being blamed for making slow progress and I feel like I don't fit into this group as not knowing chemistry field. Publications though can be made using commercial software but ideally I want to be able to build the model of the battery without relying on other software that does it for you, as it is introducing some restrictions.

Yes, absolutely - up to a point.

You do not need to be the world's greatest expert on the subject matter, but you will need to know enough to participate in the research process and make suggestions. If your role as an applied mathematician is "Subject matter expert gives you equations to solve, you apply numerical method," that is consulting, not modeling or research. Here are some things where more subject matter knowledge improves the life of both applied mathematicians and their collaborators:

With more chemistry knowledge you can know

What approximations could be appropriate? What information do your collaborators really need?
Do the results you have make sense? Being able to determine simple checks and analytic test cases yourself will save a lot of time.
If you can answer this question, what is the likely next question?
If the suggestion your collaborators gave you has a typo - this is obvious with more subject knowledge, but can be missed.
When you report on your work (papers, talks) - you can be specific about why it is important

I have worked in many math/[other science] collaborations, and a very common failure mode is that a math student takes a couple of weeks, and comes back with a single result, with an error that could have been caught. Or that the student mis-interprets a suggestion, and it takes a long time to clear up. Or that the student spends a month optimizing a calculation that is only relevant to an order of magnitude.  All of this happens a lot, and the best solution is more subject knowledge!

I do not know if this is happening with you. But if you are modeling a chemistry process, you will either need to learn a good amount of the chemistry, or you will need a more well-defined problem at the beginning. And as a modeler, you presumably know that defining the problem well is half of the battle!


Should I quote an anonymous review on my CV? I received an anonymous review from a journal.  This journal does not hide the identities of authors from reviewers.  Should I quote the reviewer's remark that the work is "impressive" on my CV?

Does the answer change if the manuscript is not accepted?

No, and no.  You should not try to hype up yourself with subjective comments in your CV or elsewhere in job applications, say.  Other people don't do this, and if you do you will seem strange and possibly desparate.  Your CV should be a list of objective information.  It is the job of your references to compare your work to other people's and say how impressive it is.

About the most you can do is keep the review in a personal "feel good" folder and look back at it when you need self-encouragement.


Sending email to editor about reason of rejection of paper My manuscript was rejected without sending to peer review, of course the editor whom I sent my paper is an expert in my field and he rejected it without any report. (it took one and half month with editor). Just the editor in chief told that the editor can not recommend this paper to publish in Journal X. Now I want to ask about sending another email to Editor to ask about his suggestions on my paper. For example, if he recommend another journal or some advice on improving my paper? Can I send such direct email to him?

  The editor who rejected my paper is the editor of several journals, what about  sending my paper to him through other journals' which he is on editorial board as well?

It is not the editor’s job to give you advice on how to improve your paper or where to submit it. And it is the editor’s job to do lots of other things, which usually keep them pretty busy. So at the very least, you’d be imposing on them by contacting them with such questions. You might get lucky and get some advice, but be aware that you are essentially betting on the editor being willing to spend time answering a question from a random person they don’t know and whose question they have no professional obligation to answer. If you lose the bet (or even potentially if you win the bet) it might not leave the best impression.

As a general rule, it makes more sense to discuss these sorts of questions with your adviser, a mentor, or a colleague you are on friendly terms with.


How to specify Russian (foreign) authors in the bibliography of an article in English? I am writing an article for publication in English. It is necessary to include references to articles written by Russian authors in Russian language. Do I have to translate their names and titles of their articles to English or I can leave their names and titles of the articles in original language (Russian)?

In the Russian-language articles, I see that foreign authors (their names and titles of the articles) are not translated into Russian and left in the original language (English). However, I have not seen articles in English, where Russian/foreign authors and articles were referenced in the original language (i.e. Russian).

For now, I am following IEEE citation style.

The purpose of a bibliographic reference, from your reader's standpoint, is to be able to find the original work. So, if I have any hope of finding it, you must provide the original title, as it appears on the publication.

Now, since I don't speak Russian (and unless your paper is on Russian literature, most of your expected audience won't either), your reference is, for me, just a bunch of weird symbols. So, I will benefit if you provide a translation of the title and authors. That would, at least, give me the topic of the article, and if the authors have any international projection, I'll get a rough idea of their work. The different citation styles dictate how to exactly refer to the original and translated title. This is an example for APA style:

  Piaget, J. (1966). La psychologie de l’enfant [The psychology of
      the child]. Paris, France: Presses Universitaires de France.

In your case, you would also transcribe the name of the author, and possibly translate the name of the journal, so I know what kind of work is it (it wouldn't be the same a paper published in a Psychology journal than in a pure Maths one).

Note that sometimes, work is published in national venues, and later translated into English. Having the names of the authors and translated title can help me find it, if it were published after your paper.


Papers about raising awareness within a domain about open source technologies Part of my (computer science) research work involves a relatively new open source technology that I feel has been overlooked by the research community. It would seem valuable to raise the awareness of this technology to the research community in a few different subdomains of the field. Would a paper that describes the open source technology, explains how it works, and demonstrates its utility with some original, albeit simple, work be ethical? While I have contributed to the open source technology, I am by no means a primary creator, and would intend to include some of the main contributors and original creator as secondary authors.

The question Should I write a paper on open-source software I've built? is related, but this question is asking about open source software that I only contributed to, as one of hundreds of contributors.

Just to get the ball rolling.
I do not see any major problem if you:

Significant contributed to the software;
Enables new research challenges to be addressed or address research challenges significantly better (e.g., faster, easier, simpler).


Graduate school holding a diversity committee meeting. What do I need to do to be effective? I created an affinity group for first generation students at my graduate school. Shortly after, the school's diversity committee emailed the student government informing us that there would be a diversity committee panel on the issue of being first gen. It seems like sort of an informal lunch between students and faculty.

I am thinking there are two possible reasons the school is holding this meeting. 1) they are trying to look good by taking initiative before students start making noise or 2) the school cares deeply about the issue. I'm inclined to believe its reason 1 because the school took zero steps to contact me or any of my first gen acquaintances to see what help or resources we needed, and is only having a "first" discussion about this issue right after I created the group even though this is by no means a new issue.

At any rate, not sure the motives for the meeting matter except insofar as me knowing how much I can get the school to change its approach to this issue. In other words how much do they actually care about what I have to say and how do I hold them accountable to act on any requests I make? I have a bunch of (very actionable and easy) ideas - e.g. distributing surveys that ask students questions such as "are you first gen" and then having the school appropriately match an advisor/mentor - but is this the forum to express those ideas? If so, at what level of detail? What I really care about is just getting the school to do the things I want it to do. How much work is done in that meeting, and how do I follow up "behind the scenes"? How much leverage do I have as a student? Any advice appreciated!

We (and indeed you) clearly cannot read the minds of those who have called the meeting, however I'd like to suggest a third option:

Someone in the administration is definitely trying to do something, but because universities are large and cumbersome organizations, change takes time.

The best things you can do are heading to that meeting prepared (which you appear to have done) and with an open mind. Assume the intent is to have a productive meeting, but also one that likely won't have a great deal of actionable things come out of it. First meetings rarely do. Express that you've got ideas, and are willing to work with them. And find out who the people are who you should be talking to - if the meeting does seem like it's sincere, try to finish by identifying at the very least who you should talk to about "next steps".

In terms of leverage? Almost none. But that doesn't mean you can't get things done - university administrators are human beings, and they too care about things. For example, at my university, the problems of first generation students are taken quite seriously and we still struggle at times.


What to do with negative research outcomes (results) of PhD research experiment? It is an expectation that the PhD would make an original contribution and/or advance knowledge  in a given field. I understand this is a universal assumption for this level of study across all universities.

One of my friend's research experiment has not produced a single positive result. This was a science experiment, so it is easy to quantify whether the result is positive or negative.

[It is a bit different in the social sciences, where the outcome (result) would be that either the null hypothesis is supported or rejected (with some analysis on the effect size to make the analysis meaningful in a given context). In other words, the data analysis either supports or does not support the proposition that is being investigated.]

My question is: What should a student do if none of the research outcomes or results are positive?

Simply writing that nothing new was found does not add or advance knowledge other than to just confirm the status quo (which I guess is a form of contribution, but there has to be more than this at this level of research!).

Your friend might not want to hear this, but there's nothing you can do except for start over - with a different experiment. Research fails - and should  ! If there isn't a risk of failure, you're not out on the cutting edge doing research.

But in most failures, there's a grain of something to build on ("from the ashes of disaster come the roses of success"). Maybe the student is too demoralized right now to see it, but almost always there's some clue in the failure that leads to a different research question worth asking.