text:
How to encourage students to work more carefully and independently?
One of the challenges of advising students is working with students whose "quality control" expectations do not agree with that of the advisor. When such mismatches occur, how do we encourage them to provide "better" quality work. Note that it's not the number of hours being worked the concern, but rather issues like returning a marked-up manuscript with half of the important suggestions left unaddressed, or leaning too much on the advisor or more senior members of the group for help.

I recently heard a nice angle on this problem. Computer Scientist Olivier Danvy, adapting Buddhist philosophy, stated in a recent presentation that you need to inhale before you can exhale. What he meant was that in order for grad students to produce quality work, they need to see, read, ingest and understand quality work. Perhaps you can show them the difference between the high quality work and low quality papers you've also come across. Try to get the student to gauge where his/her work fits in relation to these other papers.
You'll also need to explain that even though poor quality work can actually be published, that this is not a good thing. Not good for science. And not good for building a career.
Many iterations of corrections is important. It takes time. But for every comment that is ignored, you need to find out why it is ignored, and make the student aware that it takes your time and is annoying if you need to make the same comment over and over again. Maybe the student does not understand the comment. Maybe the student is overwhelmed by the vast amount of comments. Maybe that just missed it accidentally. Maybe they just disagree.

In my experience, teaching "quality control" in larger-scale projects is part of the enterprise, since (in mathematics, for example) standard coursework provides no inkling of this. That is, it is not typically the case that sloppy or flawed homework or exams are returned with detailed comments, for iterated corrections, to be repeated until the thing is acceptable. Rather, as we know, schoolwork is presented to students as a high-volume stream of disconnected small tasks, most of which truly do not merit "perfecting", but, rather, treating as a bulk-processing problem.
So the methodology and style of iterative improvement and "perfecting" a larger, months-or-years-long project is arguably a novelty to the student. The "solution" seems to be to just keep iterating the corrections, perhaps making the auxiliary point of the inefficiency of your making the same point several times.
I've had the opposite problem a few times, as well, namely, exaggerated attention to over-perfecting an initial fragment, effectively avoiding addressing the sequel and larger project.
Thus, I think that imparting a functional sense of editing and quality control is part of the task of the mentor/advisor/supervisor, although, yes, energy allocated to this takes away from the more literal scientific/intellectual tasks. Advisor's firm repetition of the standards is essentially the only constructive response, I think, since, for example, it seems infeasible to hope that novices can sufficiently critique each other, as they usually share the same inexperience.

I would cite one of the maxims in industry. Your people is only as good as your structure. A good structure designed to give incentives to current students and guidance to new ones can do wonders for any organization.
You can streamline a lot if you have a whole system in place where you have older students mentoring fresh students (one to one basis) and a more structured way of presenting the job. This way the students (and the adviser) won't be running to catch deadlines and will be way more efficient.

I do not think that giving too much liberty to someone that essentially has never worked or known the stress of self imposed deadlines is necessary a good thing. It looks more as a two edged sword.


text:
Coordination of a class taught by different professors: how to be fair to the students?
A typical freshman course on calculus has about 100 students every semester. If the course is divided into 4 sections, each has 25 students and a different instructor.
Because the instructors are different, a student's grade may strongly depend on who teaches his section. Lazy students could ask other students before registering who is the easiest grader or who makes easy or straightforward exams, etc. This is unfair to the rest of the students.
Has anybody else faced the same problem? And how did you overcome it?
What criteria one should impose to guarantee that a student's grade will only weakly depend on who is teaching him?.
We are thinking about giving all sections the same midterms and finals. But, obviously this is not enough

The more common problem across multiple sections of large courses taught by several different instructors is content drift, not grade drift. At my institution we have three General Chemistry I sections, and the distributions of A's, B's, and C's among the students does not change significantly depending on which of 6 people teach the course. The distribution of D's and F's does vary, however. What is more troublesome to us is that some instructors will cover 8 chapters, some will cover 9, some 10, and so on.
However, if you really want to minimize grade drift, then do the following, which will also eliminate content drift:

Coordinate the class - same textbook, same syllabus, identical assignments, exams, everything.
Agree ahead of time on a common grading policy, including cutoffs, curves, grade disputes, etc., and enforce it uniformly.
Teach from a common outline so that all sections get the same material in the same week.
Meet frequently, including at least one meeting before the class starts.
Grade equitably, which may seem difficult, unless you do the following: Professor A grades all of Assignment 1 across all 4 sections. Professor B grades all of assignment 2, etc. Better yet, hire one person (a grad student maybe) to do all of the grading for all 4 sections.

Just to add to the last suggestion: when I’ve been involved with such courses, we’ve usually split it up more finely, like “On Assignment 1, Prof A grades Q1 for all students, Prof B grades Q2 for all students…” and so on.  This both helps further with evening out the grades, and means that the professors’ workload is more evenly distributed, since “Prof A grades all of assignment 1 for all students” would be a lot of work in a big class.

So far, I think this is better to make a rotation of the instructor. Split the course in 4 parts, each part being taught by one instructor to all the students. Each part is graded independently by each instructor as well.
If this is not doable (slots in parallel, with a strong sequencing of the content), then the only thing I can think of this making as much grading as possible in common (same exam, etc.) and normalize the grades for the rest.
Another possible way of action is to randomly select the correspondence between groups and exams. Let me be clearer: let say that two instructors A and B give a lecture in common. We toss a coin to decide whether group A (or B) have exam by instructor A. In that way, students cannot have a strategy that maximizes their grades.

Doing each part being taught by one instructor to all the students is not always possible since each student may need to learn sec-1 before sec-2 and sec-3

I suspect the intent is that if you have three instructors over three sections in a fifteen week course, the first instructor teaches all three sections for the first five weeks at the same time and then the second instructor takes over, and so on.

The above is the common situation in Israel. Most of the freshmen-year classes are given by multiple lecturers, while the students are tested by the same midterm and final. They also have exactly the same homeworks and due dates, etc.
Officially, the sections are "identical". One professor is declared as the leading-professor and s/he sets the syllabus and takes any course-wide decision. Since all the professors are aware that the homeworks/exams are the same, they all teach the same material, more or less, giving the same emphasis on different subjects, more or less, etc. It should be the case that if one student misses one lecture at some week, s/he can go the the other lecture and be able to continue from the same point.
Of course, this is not perfect, and the different sections are not "identical", but rather, "close enough". In addition, an important thing is that students can choose which section to go. Indeed, sometimes "good lecturers" gets a class full of students (with people sitting on the floor, etc) while "bad" lecturers teach a half-empty class. Most of the times, there are no "bad" lecturers (there are better and worse, but many time the difference is not substantial enough that students change their schedule), so most of the classes are 70-90% full on avg.
Exams are graded by the same team, regardless of the section you are enrolled to. In general, the sections are "technical" partition, and any decision is course-wide and not section-wide. All students from all sections should be treated the same in the most objective way.

You already said, "we are thinking about making same midterms and same finals exams to all sections, but obviously this is not enough".
In effect it is not possible on a practical level to be equal since it all depends on how each professor teach and interact with his/her students. Each professor has specific personality and way of doing things which different people/student would like and dislike. However, somehow the following principles are followed by each professor, each class may be comparably good:

Professors are dedicated to student's welfare
There is a good understanding among the professors and they are in agreement with what should be taught (This may include regular meetings among themselves)

Apart from this each professor has his/her natural way of teaching that he/she is most confident about and this cannot be separated.

I disagree. If the common background and the exams are designed before the beginning of the lecture, then it is perfectly doable to share most of the material. Instructors can share exams without giving the exact same lectures.

I was trying to hint at something deeper beyond setting common exams/examiners/section-instructors. I said there will be some difference always unless there is some randomness and each teacher teach a given section to all the students.

I did upvote most of the other answers... but would like to add something:
Yes, it is important to try to be "fair". Sometimes, "uniformity" is the best approximation we can systematically arrange to "fairness". Other answers discussed how to implement this (common mid-terms, common finals).
However, there is sometimes a danger that "uniformity" seems to demand that everyone's situation be degraded to that which can be guaranteed for everyone else. That is, while it is highly desirable to guard against bad effects of poor teaching, it is surely also desirable to not prevent benefits of good teaching, as a side effect of administrative design.
Similarly, while the impulse is understandable, keeping instructors "secret" so that students cannot seek out desirable instructors (desirable for both wholesome and less-than-wholesome reasons) is a bit perverse, in my opinion. Namely, it always seems to me that "equality" achieved by suppressing information is not at all moving to maximize social welfare, but only "equalizing" it in a rather poor, default sense.
Students "voting with their feet" is a "problem" at all levels, and some of that is reasonable, some unreasonable, of course. But the "solution" of having instructors be "secret" amounts to merely ignoring genuine issues in the situation by trying to obliterate information.

Should all information about the teachers be revealed?  For example, should the age, gender, political affiliation, and race of each teacher be explicitly mentioned?

In the U.S., unless an individual voluntarily and deliberately discloses such things as @Joel mentions, they are "private" information. The actual chain of events would be that giving the instructor's full name would lead to their university web-page, and, yes, also, google-able information.

thanks for the clarification.  I agree with you that "uniformity" is sometimes not desirable.

Aside from having common exams, one possibility is to withhold the names of the instructors until the registration is finished.  In that way, students cannot choose sections based on who is teaching it.
Another possibility is to have all, say, 4 teachers teach all 4 sections:  Teacher "W" teaches section "A" during the first quarter, then teaches section "B" during the second quarter, ..., while teacher "X" teaches section "B" during the first quarter, etc.
I do not completely agree with your statement that "the student grade in any section will strongly depend on who is teaching it."  I think that the teacher is a factor, but not the main factor.  An extremely lazy and weak student will fail no matter who the teacher is; An extremely hardworking and strong student will pass no matter who the teacher is.  I believe the student is the main factor.

I think it's more the interaction between the teacher and the student with a productive interaction being only one of four outcomes. A motivated student can leave a class with a "bitter A", turned off from a topic he is talented in, because the professor and he didn't click. Not that you can click with every student (or faculty member).

For any individual student, the choice of instructor can have a profound effect on their grade, their mastery of the material, and their enthusiasm for the topic.

I've had different experiences. Sometimes entire batches of students have different experiences.
My control systems Professor was lenient at grading, keeping the class average at a C. He taught well, but however had to shift to another university before the second semester of that year. The other half of my batch had to deal with the second professor whose mean grade was a D.
I think a Professor, in this way could perhaps make a large impact on a student's grades. Perhaps even on the grades of an entire batch of students.

source: Stack Exchange

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

sort: votes


text:
How many years of industrial experience needed for a Mechatronics Professor?
I have a Bachelor's in EE and Master's in Automation and 3 years experience, including 2 in software development. For me, an academic career would be both more meaningful and professionally rewarding in its flexibility.
I think someone with experience but without formal qualifications could teach a subject such as mechatronics well.
How I might switch to teaching mechatronics? Should I stay in industry for a few more years and then move straight to being a full-time professor? Or, should I work part-time for a lest prestigious college and try to progress incrementally?
Note:
I don't want to obtain a PhD. Rather, I'd prefer to obtain the necessary qualifications while part-time, while working as a professor.

As far as I know, you cannot be professor unless you hold a PhD in the first place.

@seteropere I think it's location dependent. At some places, PhD is not an absolute requirement to teach at a college. Full professorship would require PhD. However, I believe the qualification varies differently and 3 years is far far away from it. It's also far away from a senior technical position in industry.

There are quite a few professors without PhDs. And there are a lot of professors without industrial experience. And once you've got a professorship, why would you then want to study for a PhD?

@EnergyNumbers I personally know people(more than one case) who taught at colleges as assistant professors holding only masters. But it's very hard to get promotion(e.g. to become associate professor), so they had to get PhD to get to higher positions.

I dis-agree with scaaahu here. There are people who teach with just a Masters degree. Typically they are termed as 'Lecturers' then. A Professor, has to have a Ph D.

@Naresh Again, I think it's location dependent. Different systems, different locations and different titles have different meanings. I don't disagree with your statement.

There is a full Wiki page

In the US, it's very common for faculty---including tenured full professors---at two year colleges not to have a PhD.  The same is true at plenty of four year career-focused institutions.

Your answer seems to confuse teaching with being a professor. One can teach without being a professor. Furthermore, and although this may be controversial, I think the quintessential characteristic of a professor is a scholar who can carry on substantial independent research.
Neither teaching skill nor experience in industry counterbalance the lack of that.

It's not clear where you're writing from, and where you'd like to become a professor. But it's almost certainly true that, given your chosen field (mechatronics), you will need to be working at a four-year university with an engineering program.
In the US and Western Europe, this means that you will almost certainly be required to have a PhD before you begin your position as a professor; this isn't really negotiable. You would not be permitted to complete the PhD while being a full-time professor. In other parts of the world, this is not a strict requirement.
However, if what you want is to teach, the rules are substantially different. In such cases, having a PhD is not required, and you can instead work part time as a lecturer or adjunct professor (or other equivalent title, depending on location). You would be expected to teach a certain number of courses per semester, but otherwise you would not be responsible for teaching students, nor would you have many of the other commitments required of full-time faculty.

In many fields, and at many schools, even lecturers and adjuncts are strongly preferred to have a Ph.D.

@NateEldredge: This is true, but it is certainly not required. In many cases, particularly for design courses and other "practical" courses, having a PE (practicing engineer) degree might be more useful than the PhD. But especially at top universities, yes, a PhD might still be expected.

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


text:
How is the training for MS students different from MBA students?

How do universities train MBA students differently from MS students? Statistics and quantitative reasoning are vital parts of the MBA as analytics have become crucial to many aspects of business. In what sense does an MS in Statistics have better training in analytics than an MBA?
How do MBAs add value to a person's career? Is their broad (if not deep) command of statistics, economics, finance, etc., the reason behind their astronomical salaries? Is there specific training that makes them 'managerial'? If yes, what is that training?

Broad question. Can you focus it to something like "How does an MBA in X (say Finance) differ from an M.S. in X (say Quantitative Finance)? Perhaps focussing will reduce the answer to searching through a few course catalogues.

MBA, as its name indicates, is training in specific business management skills. An MS is training in another set of skills They differ in the same way that an MS In EE differs from a MS in Anthropology.

What do you mean by analytics? Your question stem hints that it is a field in statistics, but I have seen it used in the context of machine learning and decision support. I don't want to seem like I'm picking at semantics, just want to understand better part of your comparison.

An important aspect of MBA training that I haven't seen mentioned in other answers is networking.  A huge amount of getting things done is business is about networking, and while you will not find a formal course on networking in an MBA program, most good MBA programs will have a lot of networking events and opportunities connected with them.  MBA students are also often grouped together in cohorts or otherwise treated much more as a unified group of students than MS students.  All of this adds up to a very important extra-curricular curriculum component that is often a key differentiator for success or failure after business school, and is largely neglected in the more technically focused track of MS students.

Exactly, from what I gathered the purpose of MBAs is almost exclusively networking. The content is irrelevant. Prestigious, expensive MBAs attract influential people from companies successful enough to afford the tuition. Very useful to have in your address book if you plan to climb the management ladder.

I believe an accurate rephrasing of your question is Why do some MBAs make so much?
The upper decile of MBA holders surely earns more than the upper decile of those with a MSc, although that is my educated guess. I, similarly, wager that the average MSc earns more than the average MBA.
I am not sure that an MBA derives its value from content, nor am I alone in that view point (See what a graduate and the FT say.) An MBA allows for powerful networking, especially if you already have a powerful network to barter with.
As to the content, A Master's in Business Administration (MBA) differs from a Master's in Science (MSc) in many ways:

Curriculum: MBA classes do not cover technical subjects in as much depth as MS classes. An MBA student may take one class in financial economics that involves no calculus or concepts from analysis. An MSc student would take at least two courses, involving higher math and perhaps computer simulations.
Point in career: An MBA is a terminal degree in business, inasmuch as degrees matter in business. An MSc can be a terminal degree in engineering and computer science. In math or economics the terminal degree is a PhD and one picks up an MSc along the way.
Point in career (2): An MBA is supposed to be acquired after substantial work experience. Many pursue an MSc shortly after undergraduate education.

CS has phd as a terminal degree I think.

I didn't say MSc is the only terminal degree in Computer Science. Edited to make that clearer.

From an educational stand point, a Master's degree in a specific discipline is not much different from an MBA degree, in that they both are Master level degrees. There is one caveat, though: Master of Science (MS) degrees are, naturally, focused on scientific research, whereas MBA degree is not. The consequences of that distinction can be found in different structures of academic programs as well as the typical requirement of a thesis or a similar major project for an MS degree. Those Master's degrees that do not use the MS designation are usually referred to as professional Master's degrees.
Now on to the core of your question. In it, stating the importance of analytics for business, you are making an IMHO unfounded implied assumption that analytics is vital for business. There is a big difference between being important and being vital. While analytics and data-driven decision making are, certainly, important (more or less, depending on the industry, business type, size and other factors), I would argue that, in general and by the very definition, having excellent business knowledge, skills and experience is more vital to a business than the same traits in analytics department. In other words, MBA holders potentially have a more significant and immediate effect on a business' performance and other results, than data-focused professionals. The former are "closer to the bottom line", so to speak.
For a case in point, an anecdotal example of Warren Buffett and his Berkshire Hathaway company. It is a well-known fact that computers and computer-based data analytics are almost unused by the company and, especially its boss, Mr. Buffett. However, that fact clearly does not preclude the company from achieving its tremendous growth and overall business success. Therefore, that is done by applying mostly general and specific business knowledge, skills and experience, rather than modern analytical approaches and tools.
Note that above, when referring to knowledge, skills and experience, I used the word "excellent", which, usually (but often unfairly!), implies that MBA graduates are top business schools. Therefore, their higher salaries on average, when compared with similar graduates from analytics, data science or similar MS programs. If we would consider graduates from both MS and MBA programs from programs beyond top tier, the latter not only will not have "astronomical salaries", but, AFAIK, their salaries (especially, starting) will be lower than narrow technical professionals, graduated from MS programs.
As for specific training for MBA graduates that "makes them managerial", certainly MBA programs offer specialized managerial courses (i.e., strategy, marketing, accounting), which are simply typically not offered in MS programs (or, are optional and significantly more limited). Hence, naturally, more significance of MBA graduates to business results and, thus, higher financial compensation (again, I imply here only top tier MBA graduates and their starting salaries).

This largely depends on the type of MS degree.  An MBA is geared towards business. A graduate from a MBA program should be able to immediately apply his or her learning to a business environment.
MS degrees differ. Some are geared towards research, requiring a student to write a thesis.  Others require only classwork. A MS degree in statistics that emphasizes research may not discuss how to use statistics to make business decisions. That MS degree may, instead, teach the mathematical underpinnings of statistical tests or how to analyze data. Data, here, means not just stocks or inventory, but time series in general.
One can leverage a quantitative research background into a lucrative career in business. I have known a few physicists who are well paid for their knowledge of mathematics, programming, or thinking processes, all valuable in a business setting but not taught in MBA classes.

authors: سلطان الشمري


text:
What should I do if I submitted more recommendations than needed for an academic job application?
I am applying for academic jobs in mathematics. Most of the jobs I am applying for ask for three or four letters of recommendation.
At the beginning of my application process, I spoke with an academic who has served on multiple search committees, but who is not a mathematician, who told me that it is ok to send more letters of recommendation than asked for. As a consequence, I obtained five letters of recommendation, and used all five for almost all the jobs to which I have thus far applied.
Recently, I read some things on this site (here and here) that made me wonder if this decision was an error.
In addition, I reflected more on my choices of recommender, which had really been made with the first job application in mind, and wondered if some of the letters really didn't make sense for some of the applications. (I am applying to a variety of positions.)
The question is what to do now. Almost all the applications are through the website mathjobs.org, which allows me to change them at any time. But in many cases the departments are supposedly already processing applications. I wouldn't want to just make a change to an application that is already being reviewed, without comment. The options I have thought of:
1) Don't worry about it. Leave everything as is. Let the departments contact me if something needs to be changed.
2) Contact the search committees and ask them if I should withdraw a letter or two.

What is your advice for me? Are there other options I should consider? What is your recommended option?

I am particularly interested in hearing from people who have served on search committees, or who have gone through the search process and received feedback from departments about issues related to the above. So if you fit either of these descriptions, please say so.

2) Contact the search committees and ask them if I should withdraw a letter or two. I doubt you would get a response to such an inquiry, as the search committee is too busy.

What's done is done.  Five is no big faux pas, just more than needed.  I would move on and, perhaps in the future, trim the number you submit.


text:
Grid lines on graphs
I'm told there are conventions in scientific papers around graphs. I'm publish material for a general audience based on a the findings of a scientific paper (unpublished) and having a disagreement with the author of the paper about how graphs must be presented.
I'm specifically asking here about the conventions for scientific publishing. I'm very aware that conventions outside scientific papers for graphs are much more open, I take my visual data cues mostly from Edward Tufte's books.
I'm being told that displaying horizontal grid lines implies a greater accuracy in modelling data and therefore should be absent in the case of this carbon sequestration modelling since it's not the results of measurements? (I would have thought significant figures on axis, axis spacing and fundamentally the caption explaining the data source and assumptions were more relevant to that)
I'm told that titles are a no go, captions only. (I've found a Uni spec online for science papers saying titles are mandatory). I'm told titles are rare in journals.
Is there any right or wrong to these matters of convention or is it just opinion?

I'm pretty sure grid lines are unacceptable by APA standard, which is typical in social science journals (APA = American Psychological Association). I know this is irrelevant to your field; just giving a datapoint.

In my field, journals and conferences that publish usually specify details such as usage of captions/titles and the placement of captions/titles in relation to a figure. (Graphs being regarded as figures.) I've never heard of any standards regarding grid lines.

displaying horizontal grid lines implies a greater accuracy in modelling data — what is this i don't even

If displaying horizontal grid lines implies a greater accuracy in modelling data is true then you should suggest removing the y axis as well.

On grid lines
It depends on the points you would like to make with the graph. If you're just going to show an upward or downward trend, then the grid lines are probably redundant. If you'd need refer back to a certain point of a curve, and knowing the vertical position of that point would be crucial, then grid lines can help. It's not about the graphs (or I may go so far to say even within publication culture,) it's about the points you are trying to get across. If the grid lines will get people there with less puzzling or work, then yes to grid lines. In all other occasions, then no.

I'm being told that displaying horizontal grid lines implies a greater
  accuracy in modelling data and therefore should be absent in the case
  of this carbon sequestration modelling since it's not the results of
  measurements

This is perhaps the oddest graphical rule I have heard in the last 12 months. For most grid lines are just an extension of the tick mark on the axes. As long as you provide the tick marks on the y-axis, anyone can draw horizontal grid lines.
It is, however, not advisable to provide tick marks or grid lines finer than what your instrument or model can discern. For example, if your measurement or prediction is in the unit of meter. Then, at most I'd just put grid lines at 0.5 m increment. I wouldn't go so far to put 0.01 or even 0.1 m increments. That would imply some precision that I never had. I believe your partner author's concern may be more related to this problem. In that case, you two need to talk and make sure at least the tick marks make sense.
On caption vs. title

I'm told that titles are a no go, captions only. (I've found a Uni
  spec online for science papers saying titles are mandatory). I'm told
  titles are rare in journals.

Yes, they are rare in my field (biomedical.) We use captions (located below the graph) most of the time. The caption usually starts with something like this:
Figure 1. The [would have been title]
[texts explaining the graph.]
If you have a title in the illustration, it only serves to duplicate information, making it redundant ink.
Though, depending on fields and journals, the rule may differ. Check with the journal's guideline and other published work in that journal for clues.

Thanks for these comments. This is a publication for a general audience so I'm going with my instincts and design trends at large towards simplification. Was interested about the standardisation around these ideas in academic publishing.

There is no right or wrong when it comes to grid lines. There may be conventions varying between disciplines. The basic question of whether to use such lines or not, is if they add something useful to the reader to better understand the data displayed. A go figure should communicate as many thought as possible to the reader without to much effort. If you want to get some ideas of thinking about graphics, try to locate the book The visual display of quantitative information by Edward Tufte. There are many constructive thoughts about displaying information there worth considering.
In the end you need to look at how others publish similar data and figure out if a "standard" has developed. It may not be the best way to display data but since many are familiar with the format it becomes an efficient communication. Otherwise you should try to display the data as clearly as possible, lines or not.

See Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis? for some discussion on this at the stats site.

As I mentioned in the OP I do take many cues from Edward Tuffe on visual communication of information. Have several of his books including the one you cite. I'm confident from a comms point of view of what I'm arguing for but I was curious if this claim about implied accuracy of modeling in scientific papers had any basis in fact.

@Andy W I should have mentioned that by using faint gridlines I am able to remove the solid vertical-axis which makes for less chart-junk in my view. There's an obvious trend towards this presentation of column/line/scatter graphs in the design world. It's cleaner in my view and lets the data take centre stage, not the apparatus of the axis and ticks. Removing that left hand vertical element seems to remove some kind of hierarchical impediment to 'seeing' the data for me, however subtle this may be.

Regards audience you hit the nail on the head. That's the nub of the disagreement. For me and the publisher it is the general public, for the author it would be academics (since the paper remains unpublished at this time).


text:
My final transcript print is bad (Low quality print)
I'm interested in studying abroad (US,UK), but my transcript got very low quality print and my college is not ready to issue new transcript, does bad quality print affect my abroad study chances? what should I do, Please guide me. {I'm from India}

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


text:
What is the best action if a reviewer did not read my work (survey paper) but still advised me to cite at least ten of his works?
So I'm talking about a survey paper on a very specific topic. This reviewer suggested that I include hyperparameter training and a comparison with the SOTA model, indicating that he did not read my work because it was a survey paper with no model proposed. However, he suggested at least 10 of his works, some of which are not even relevant. I appreciate the recommendation to make my work more complete, but I believe it should be based on the topic relevance and significance of the recommended work rather than his personal recommendation. What would be the best action? I know citing 2-3 more papers to avoid trouble is fine, but not 10 papers from 1 person with some of them off-topic.

Just do it and add an acknowledgment thanking a referee for pointing out refs 1,2,3,4,5,6,7,8,9 and 10. without using them in the paper. People will understand.

Response to reviewers comments

This reviewer suggested that I include hyperparameter training and a comparison with the SOTA model, indicating that he did not read my work because it.  aAs a survey paper with no model proposed, this does not seem appropriate.

However, he They also suggested at least 10 additionsl of his works, some of which are not even
relevant. I appreciate the recommendation to make my work more
complete, but I believe it should be based on the topic relevance and
significance of the recommended work rather than his personal recommendation. I also noted that the recommended work all share an author in common.  We apologise for missing the work of this author and have included the subset of these references we feel relevant to the work.

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


text:
How to write the introduction of a stapler PhD thesis?
It looks like I am going to have three articles (if all goes well) that are dealing with quite different fields (psycho-accoustics, time-series analysis and machine learning). I do not have a single clue on how to start writting the introduction for a thesis that should tie these together ...
Do you have tips for writting introduction when dealing with "sandwitch"/"stapler" thesis ?

If you have an advisor, this question would be best suited for them because it is very field specific.

Does this answer your question? Differences between the introduction of a sandwich thesis and a paper

As a comment says, this really is a question for your supervisor. I'm assuming it was your supervisor's idea to put your three papers together in the thesis, so perhaps they have suggestions on how to make them fit together.
If it was your initiative, check with your supervisor first. They might propose to leave out one of the papers so that the resulting thesis will be more homogeneous (at the expense of being shorter).
My thesis included only some of the publications I did during my PhD, those that were part of a larger project. In the meantime I did other things which have not been included in there.
Otherwise try to find a common denominator to all the papers, no matter how far it is, write a section about it, and then specialize to the topics at hand. I suppose there has to be a common denominator of some sort, but I'm not an expert in your specific field.


text:
What are some good practices when asking people to share their PowerPoint presentations from a conference/talk?
In many cases, they (understandably) don't reply (or they are uncomfortable with sharing them, as they often contain unpublished material). Also, I do this often, and don't want to be known as "the person who asks people for their powerpoint slides".
At the same time though, it's simply far easier to remember the content of a presentation/talk when you actually have access to the stuff inside (and I do discover that I learn faster from talks than from any other source). Most of the time, I request the slides as a reference for learning the material (since I'm still new to the area, and they are quite helpful for that).
(though I do wonder - what are the underlying circumstances when most people ask for them?)

Most people ask for them for the same reason you do; to simplify note-taking, and to help remember some idea for future research.

Whatever you do, just don't call them a PowerPoint unless you know they were actually prepared in PowerPoint :-P (I am among the minority of people who get a little offended when someone asks about my PowerPoint - because I prepare my slides in LaTeX)

In my experience, conference organizers often ask speakers to provide their slides, to be posted (after the talk) on the conference web site, so everyone can access them.  (And @DavidZ will be glad to learn that, instead of calling them "PowerPoint slides" regardless of how they were actually produced, we usually call them "Beamer slides" regardless of how they were actually produced.)

There is no magic when it comes to asking for presentations. And there is nothing wrong with being the person who asks people for their powerpoint slides (at least you show that are interested and it may result in them being cited; and it shows that they got their job done - though a presentation they interested others in their idea).
If they don't reply (or don't sent it), it is usually of one of the following reasons:

They are busy and missed your mail,
It would take their time to find the presentation and send it (it may be big so it is not just sending an e-mail),
They would prefer not to make it public as:

It is not polished enough for anything but a presentation,
It may contain things that they would prefer not to share publicly (e.g. plots form other papers, preliminary data which may later proven to be wrong or incomplete, pictures or video they don't have right to share further, etc).

If they don't want to share it - they have right to it. However, usually they have nothing against (and actually are happy to do so) as long as you make it quick and easy for them.
So it is a good idea to ask for slides just after their talk - they may have it on their computer (so you can copy it to your stick) or on a stick (o you can copy it to your computer) or send it right away.
Also:

Send your e-mail at most a week after the presentation,
Be short and concise (what exactly you want, what do you want with do with it),
If there is no response try writing the same e-mail a week later.

Be very clear on what you want it for, particularly whether you will be sharing it further (even within your group, with your advisor, &c).
I often post edited version of the slides online, but I always want to have control on the contents that are public, semi-public, or private.

I don't mind sharing my presentations, but I make sure that I provide a read-only format only (PDF) with slides scaled to e.g.  1/4 of actual size, in raster format (so that more than one would fit on a page). When people ask for presentations, I expect they'd like to have the material handy for further reading and reference, not to present it or use it themselves.
One would go even further and restrict the PDF printing, copying and modifications, although these can be circumvented with a reasonable effort if you know what you are doing. I don't do this as a matter of principle, but I can see how some people might want to have such kind of control.

You can ask for a redacted version of the slides, without new data. Do note that this will require extra work from the presenter; in my experience, I've found that most people do not go this route. I would follow the route you're currently taking; ask for them, and if they don't wish to share, then drop the subject.

If you're uncomfortable asking the authors of the slides, for some event types, another idea could be to ask the organizers of the conference, seminar, talk, etc., if they could make the slides available to everyone on the event website. Depending on their attitude, the organizers may think that this is a good idea and do it on your behalf, and there may have a higher chance of getting a response from the presenter.


text:
What are the 25th-75th percentile ranges for the annual income for graduate students in the United States?
And are there differences in average income between different types of fields, or different types of schools?
The diagram below says that the average income for grad students is $17k/year. But I'm more commonly seeing incomes in the range of $25k-$30k/year on PhysicsGRE.com...

Okay just changed it

In jest, 25th percentile - poor, 75th percentile - not quite as poor (but still poor by most standards!)

Note that those statistics are often averaged over all possible disciplines, and therefore, since there is such a wide disparity of stipend levels between different schools, and between different disciplines at the same school.
At the institution I attended for graduate school, engineers had stipends approximately 30% higher than the science majors (chemistry, physics, math, etc.). Similarly, at the undergraduate institution I attended, a similar disparity existed between science and humanities graduate students.
It is also important to ask if master's students, who often don't receive a salary at all, are included in that average. (And, since humanities students tend to stay longer, they may skew the statistics even more than one might expect.)
(One final note to directly address the question: competitive national fellowships in the US currently pay between $30,000 and $35,000 per year as a stipend. I would estimate, then, that most stipends are significantly below that amount. I'd say something in the range of $20,000-$30,000 would be appropriate in STEM fields, and probably $15,000-$20,000 for full-time humanities PhD's.)

It only makes sense. Companies aren't willing to pay all that much for historians to finish their phds like they are physicists. How could what they have to offer have any bearing on today's scientific research anyway?

I would suggest the major driver of differential graduate student incomes is the varying sources of funding for different research areas. Some funding agencies list the amounts they fund explicitly on their website (e.g., NIH NRSA grants gives $22,032 for graduate students, other numbers available on their site), while others require the amounts to be listed by the PI in the grant proposal (e.g., Air Force Office of Scientific Research grants do not list any specific amounts in the call for proposals).
Given the large number of funding sources across all disciplines, finding actual numbers (beyond anecdotal reporting) would be a major undertaking. If you do research and publish the numbers, though, I'd love to see them.

Here is a nice list of PhD stipends in different fields and schools in the USA:

Brown University – 20500 (Political Sciences in 2013), 27500 (BioMed:Biology in 2013)
California Institute of Technology – 21321 (Physics in 2014)
Carnegie Mellon University – 27600 (Biological Sciences in 2013)
Columbia University – 32447 (GSAS in 2014), 31666 (GSAS in 2012)
Cornell University – 36080 (Life Sciences/Physical Sciences/Engineering in 2014), 31293 (Humanities/Art in 2014), 30533
  (Policy in 2013), 30533 (BMCB in 2013), 29067 (MBG in 2010)
Duke University - 28773 (Arts & Sciences in 2014), 29420 (Medical Sciences & Nursing in 2014), 27850 (Engineering in 2014), 27934 (Arts
  & Sciences in 2013), 28700 (Medical Sciences & Nursing in 2013), 26910
  (Engineering in 2013)
Emory University – 28000 (BCDB in 2014), 28000 (Biomed in 2013)
Harvard University – 32232 (SEAS in 2014), 31284 (SEAS in 2012), 32616 (OEB, GSAS in 2012), 26795 (GSAS in 2009),
John Hopkins University – 29218 (Biochemistry, biophysics, cellular, developmental, molecular biology in 2014), 22000 (Political
  Science in 2014), 25000 (Biostats in 2014), 21000 (Political Science
  in 2013), 20000 (Biostats in 2013)
MIT – 31968 (Engineering PhD RA in 2014), 30888 (Science/English PhD RA in 2012), 30630 (Chemistry PhD RA in 2012), 28236 (English MS
  RA in 2012), 31644 (Science/English TA in 2012), 28524 (English TA in
  2012)
Northwestern University – 22992 (average in 2014), 22428 (avg. in 2013), 28000 (IBIS in 2013), 21936 (Physics and Astronomy in 2013)
Princeton University – 32000 (Molecular Biology in 2014), 26784 (Humanities in 2011), 27504 (Natural sciences in 2011)
Rice University – 26000 (Chemistry in 2014), 24720 (Chemistry in 2013), 28000 (Biochemistry and Cell Biology in 2013)
Stanford University – 36500 (PhD with SGF in 2014), 29500 (Neuroscience in 2010), 29250 (GSBS in 2010)
University of California Berkeley – 31000 (Molecular and Cell biology in 2014), 30500 (PMB in 2013), 30000 (MCB in 2013)
University of California Los Angeles (UCLA) – 26000 (Ecology and Evolutionary Bio in 2014)
University of California San Francisco (UCSF) – 31000 (Neuroscience in 2014)
University of Chicago – 29000 + $500 relocation fee (BSD in 2014), 28500 (BSD in 2013), 28000 (Humanities in 2013), 28000 (BSD in 2012)
University of Notre Dame – 25333 (Biological Sciences in 2013)
University of Pennsylvania – 30500 (Biochemistry and Biophysics in 2014), 27615 (Art & Sciences in 2013)
University of South Carolina – 17500 – 22000 without tuition
Vanderbilt University – 25500 (CPB in 2011), 25500 (Biomedical Sciences in 2009), 20000 (Chemistry in 2005)
Washington University at St. Louis – 28500 (DBBS in 2014), 28500 (Biology/Biomed Sciences in 2013)
Yale University – 32500 (BBS in 2014), 31700 (BBS in 2012), 35700 (BBS with NIH or NSF funding in 2012)

Nice list. Do you know if this includes tuition? For the University of South Carolina it specifies without it.

@Davidmh I'm pretty sure it does in most schools. All grad  schools I know a friend from in the us includes tuition (otherwise you'll be in serious deficit).

There's a comparison with other jobs that uses current data on simply hired. You might be able to drill down to what you would be looking at specifically using their search.

I think I would be surprised if it were $30000$. I, for one, get much closer to $20000$, though it is pretty easy to do a little here or there and get a little extra if I wanted to.
On the other hand, I do work all day, and I don't spend any of it ever. So in a sense, I get too much.
In addition, my school is very willing to fund our grads visits to conferences, so that's not (always or completely, but rather sometimes and/or partially) out-of-pocket.


text:
Does a informative and clear professor webpage often increase the number of prospective PhD students who apply to work for the professor?
Here is what I would call a particularly informative and clear professor webpage.
Of course, it's not the most important thing (for me, personally, I mostly discovered professors through asking current professors whom to contact). But a lot of PhD students do discover professors to contact through Internet searches, which may be especially relevant for PhD students who might not have as many connections (especially international ones). And maybe a strong professor webpage could also increase the "fit" of the applicants who do decide to contact the professor.

Hm, a lot of information, or good web design. I'll have to admit that "strong" is subjective, but the webpage I linked is unambiguously strong.

Hmm. I'm not sure how your linked webpage can be called "strong" either (how would you define "strong" there?). As for the one I linked, http://www.atmos.washington.edu/~dargan/research.html contains individual pages for all of his research subareas (if you click the hyperlinks).

okay I updated it

I still wonder why you call this page informative or well designed or fitting to the applicants. Maybe you can elaborate on what design choices you found that good.


text:
Is it a good or bad idea to list declined fellowships (for a PhD program) on one's curriculum vitae?
E.g. as shown in the example here.

2011—Centennial Fellowship, Princeton University Department of Atmospheric and Oceanic Sciences  (accepted)
2011—Top Student Award, University of Washington (declined)
2011—Program in Climate Change Fellowship, University of Washington (declined)
2011—Faculty Fellowship, Columbia University (declined)
2011—Charney Prize, Massachusetts Institute of Technology (declined)
2011—Regents Fellowship, Scripps Institution of Oceanography (declined)
2011—Chair’s Award, Stanford University Department of Earth and Environmental System Science (declined)

What do you mean by declined - it means that u were offered the fellowships, but u declined them for reasons unstated?

One reason is if you want to go to another institution. If Berkeley offers you a fellowship to be a PhD student and you choose to go to MIT, then obviously you have to decline the Berkeley fellowship.

Yeah - by declined I mean that multiple universities gave the PhD applicant a fellowship, and the PhD applicant had to decline some of them.

I am not sure we will have a definitive answer to this question. But as a committee member, I will not count such a list at the advantage of the candidate.

I have seen this commonly used with NDSEG and NSF awards

I'd like to see this question focus on EITHER grad fellowships that were declined OR other fellowships (more like grants/awards).

Aeismail makes an important point about location: what's standard in Germany differs from what's standard in the US.
In a US context, I would strongly recommend against listing things like declined graduate fellowships.  It will look strange, and even beyond that it can work to your disadvantage: everybody will already assume you declined several attractive offers, so giving an explicit list will do nothing but focus attention on what isn't on the list.  (If the list of declined offers is short, readers will be disappointed, and if it's long, they'll spend more time speculating about what's missing than being impressed.)
The only time I'd recommend highlighting this sort of information is if for some reason you had to turn down a vastly more prestigious offer than the one you accepted.  For example, maybe you were offered a tenure-track job at a top department, but ended up working as an adjunct in the middle of nowhere so you could take care of a relative.  You should then make sure everybody knows this the next time you are able to apply for jobs.  However, you should be very careful when doing this, because if the prestige difference isn't absolutely universally acknowledged, then you run the risk of offending people who feel you are unfairly denigrating a perfectly fine career path.

Bad idea. If you turn down an award (or an acceptance to a univeristy, etc), you don't get to reap the benefits of that award.
No one cares about the universities you could have gone to or the fellowship programs you could have worked for, they care about what you have actually done and that is all you should include on your CV/resume. Period.
To me, the resume linked to above reads "I had no one else edit or evaluate my resume before I posted it online."

Yes, exactly this.

As I mentioned in a comment below, I got exactly the reverse advice from several career professionals. There's no single right answer here. Some people may take offense, but others might find the information useful.

I'd argue that the including declined fellowships would not offer enough of a boost/benefit to offset the risk of your CV coming across as "padded"

@Amy I agree - especially since "padded" is I think the most charitable negative reaction that comes from this. There are several others that are worse. And not printable in polite company.

Like so many other matters, your location plays a role in what is considered accepted or not.
In the US, I would limit listing "declined" awards to national, competitive fellowships which had to be declined because of the fact that you're not allowed to accept multiple fellowships. However, the awarding of multiple such fellowships shows that you are a "hot commodity," and therefore does confer some benefit to you. (As an example of other countries' practices, here in Germany, it is expected that you would list offers of faculty positions that you have declined, for exactly the same reason.)
However, I would agree with Amy and Ben that in the present case, those awards should not have been listed on a CV. On the other hand, "DOD Fellow" and "Offered NSF fellowship" would be a different scenario.

As a USian academic, I completely disagree about DOD and NSF.  If you decline the fellowship, you decline the award.  (Choose wisely.)

I am also a US-born academic, and was offered a number of fellowships. I listed that fact on my CV at the advice of several recruiting professionals. So there's no "absolute" correct answer here. I'm just trying to argue that there's a big difference between a national award that everybody's heard of and a minor departmental- or school-level award.

@aeismail - recruiting professionals for academia or industry? In the US? I assume that you're currently employed, so keeping these things on your CV worked for you - can I ask what kind of work you are doing now? I'm trying to imagine a scenario where listing these things would work to your advantage.

@Amy: I had a staff position with the DOE, and the inclusion of the fellowships was recommended by the recruiter. Now, I'm working as a professor in Germany.

I agree with this answer and disagree with Anonymous Mathematician and Amy.  If you were awarded a highly competitive national fellowship, but had to turn it down in order to take another highly competitive fellowship (or for some other reason), the fact that you won both awards is meaningful.  BUT: the drek listed in the original question is not an example of the sorts of things that should be listed as declined.  We're really talking about things like the NSF or similar stature here.

I also agree with this answer because hiring committees want to know about your potential earning ability, and the fact that you were awarded a million dollars worth of grants tells them you did have that ability, even if you declined the award for whatever reason.

@aesmail what about listing declined fellowships for a Master's application in Germany? Would that also be seen favourably?

I don't think there's one answer to this question.  Different people react to different things on CVs in different ways.  The level of listing above is silly; if the candidate in the example had asked me for my advice I would have told them not to (they're basically listing jobs they were offered; interpreted generously, it looks like CV padding), but occasionally it can make sense to list a prestigious fellowship you declined due to circumstances.


text:
How do professors usually discover "new" research interests?
Usually, a person starts out with a single primary research interest as a PhD student (closely related to that of their adviser). But then as they move on through the assistant professor stage, they discover new research interests. Do they usually discover new research interests through collaborations/discussions with professors at their university or through conferences? Or do they discover new research interests through what can earn them funding (through the NSF and other agencies?) Especially since any topic could theoretically be interesting to them? (even though they might find some topics more interesting than other topics).
Is it more often that they do what they're most interested in, or that they do what they obtain funding for (and find what they get funding for interesting enough)? And can they sometimes even discover new interests through their PhD students?

I'd be surprised if there is a "usual" method.  (How do people meet new friends?)

I'd think all of the above, as well as just plain old "thinking" :)

The more difficult question is how to narrow down the innumerable interesting areas and decide which to focus on.

Here are a couple ways that have resulted in new research directions for me:

Reading a paper/book. Reading a paper/book can inspire new ideas. You may find that a paper is missing something, or you think of a way of doing it better, or perhaps you can combine the ideas in the paper with something you already know.
Obtaining funding. Often funding is obtained in groups, generally to inspire collaboration and cross-fertilization of ideas. As a result, one is forced to venture into new territory.
Talking to a colleague (generally from other institute). Simply chatting with someone at a conference or when visiting another institute can inspire new research directions. They may have a problem; you may have the tools to solve it.
Changing job. This brings you in contact with new people who have new problems. Collaborations my result from conversations in the coffee room, or by going to research discussions within the new department.
Following up on something a student has done. You may set a masters student, for example, to look at something you find interesting, but have not yet had time to explore in depth. The student may come up with something useful, but then leaves.

Some points (I'm no Prof., but which research topics might "pay out" (wether citations, patents, fame) is imo rather a matter of common scientific sense):

It may be cheap/easy/time-efficient based on the current lab hardware and expertise/knowledge of the group to "dive" into a additional topic. So I would call this economical reasoning to exploit new research opportunities similar to funding reasons.
Publish or perish. Which research fields are trendy or will become trendy and offer high chance to publish and get citations. Visualisation tools for the publishing landscape or search engines might show you which fields are currently booming more or less. Or browse research forums like mathoverflow,citeulike,... to see what top and experienced scientist are currently interested in.
Finding a niche, which needs of course a really good overview on and understanding of a research field. Contrary to bullet point 2, focusing on a topic/problem no one else is interested in or thinks of may give you the chance to become a trendsetter, find a new effect/phenomenon and may earn you a lot "automatic" citations.
simply working off the list of unsolved problems :) (Wikipedia knows also, what we don't know!), http://www.openproblems.net/, science's magazine 125 big questions

As much as possible, I'd encourage you to pick research topics because they really interest you, rather than because you think you can get funding for them.  It's so much easier to bring passion and energy to your research when you love it (rather than when you've chosen it to get funding).  That said here are a few more ways to develop new research interests.

Hear a talk at a conference.
Write a paper with a colleague who knows a different area than you.
Chase references for a paper you are reading or writing.  I often learn quite a bit the first time that I write the introduction of a paper in an area that's new to me.

Dave Clarke's answer is excellent, and just to add a few more:

Changing funding opportunities. The unfortunate reality is that grants are often hard to come by, and it's not unusual for professors to tailor specific grant applications to the nature of the grants being offered. Depending on how unpopular the professor's previous research interests are at the time, this may result in a shift in research topics.
Single topic diverges into multiple topics. To describe this via example, at one time our lab was working on a neural imaging project related to human decision making. This research led to investigating decision-making on a larger scale, as we tried to create computational models. This led to a few papers detailing the applications of ontologies to behavioral and cognitive neuroscience. Simply by following the various aspects of the project we were able to investigate a wide range of topics.


text:
What is the average number of articles written per author in a year and has it increased recently?
Did anyone research the data about scientific productivity? I.e. what is the average number of articles written by author in a year in a specific field? I am  referring to average, since this is maybe easiest to calculate, but it would be interesting to see the distribution data too. This of course depends on how many journals we want to scope, but I would be interested to see any data on say most popular fields like physics, mathematics, chemistry, biology, psychology, economics, etc (order is of no importance).

can you rephrase/edit your question to define why one would want to study such numbers? in other words, give us a context of the question

It's been my impression that quantity is not as critical as quality.  A paper in a reputable journal is worth more than several papers in their less reputable counterparts.

A single number "papers per author per year" is nearly meaningless.  Publication and coauthorship rates vary radically from one field to another — in many subfields of physics, people publish tons of papers with lots of coauthors; in many subfields of economics, people publish few papers, with few or no coauthors.

I need to settle argument with a colleague. I know that quantity is not quality.

@JeffE, yes different fields have varying number per articles written. So this is why in my question I mentioned that I want to see the data for different fields.

@Paul do you have data to back that up? My recollection of Byrnes's study in psychology (http://pss.sagepub.com/content/18/4/283.short) is that that is not the case for tenure review in the US.

@ElCid I think knowing these numbers for your field can be valuable to both new people in the field and the tenure review process for calibration purposes.

@DanielE.Shub: Paul's contention is certainly consistent with my experience in computer science, on both sides of hiring, promotion, and tenure decisions.  (We tend to refer to "Publications in less reputable counterparts" as "non-publications".)

Number of articles written? Or number of articles in which someone shows up as one of many authors? Because those are two very different things.

A comprehensive review was conducted in the field of psychology in relation to publication rates and tenure. There is also a "fun" review related to beer consumption and publication in the field of avian evolutionary biology.

doi: 10.1111/j.1467-9280.2007.01889.x
Psychological Science April 2007 vol. 18 no. 4 283-286

From the psychology study: It is possible that the only way to fully understand this finding will be to conduct detailed case studies in which chairpersons or colleagues of the candidates for tenure are interviewed about the cases. — Really?  Ya think?

@DavidKetcheson the link works for me. Added the doi at the end.

Thanks; it works for me now too.  Maybe just a server hiccup.

Though this is not a direct answer to your query, this hopes to lead to more productive / useful answers.
See this Wiki article, on Hirsch index or h-index that is a widely accepted measure of

both the productivity and impact of the published work of a scientist
  or scholar.

The article also points to other indices on scientific/academic productivity and impact in the field.
The SAO/NASA Astrophysics Data System Abstract Service provides a gateway to the online Astronomy and Physics literature. A new interface of the ADS provides a tool to visualize the author and paper networks; that tells you how productive the author is, quantified with the number of collaborators and papers produced.
For e.g. see this link; a search for the name Narlikar. On top of the resulting page, next to Top 200 results, you can see a drop-down menu, on which you can select various options like Author Network, Paper Network etc to get various  visualizations.
You can contact the project personnels here http://adsabs.harvard.edu/ . They do conduct studies on bibliographic data, which is one of the measure of academic productivity.

Update :
Here is a link to the articles that cite J. E. Hirsch's article - An index to quantify an individual's scientific research output- on h-index via Google Scholar http://scholar.google.com/scholar?q=link:http%3A%2F%2Fwww.pnas.org%2Fcontent%2F102%2F46%2F16569.abstract

Widely used perhaps, but widely accepted?  Really?

There is a scope for another question, then :-)

I have added a question http://academia.stackexchange.com/questions/3131/which-is-the-most-accepted-measure-for-an-individuals-academic-productivity

In the subject that I am familiar with, political science, there is a recent study using bibliometric data on patterns of publishing in journals - maybe this could be interesting (and might be similar to already mentioned work in other comments): https://www.cambridge.org/core/journals/ps-political-science-and-politics/article/patterns-of-publishing-in-political-science-journals-an-overview-of-our-profession-using-bibliographic-data-and-a-coauthorship-network/D946105595ED2C314AE2A04138F7A5B6
As the authors state:

We drew on more than 67,000 papers published from 1990 to 2013 in one
  of today’s 96 core journals. The network consists of more than 40,000
  authors located worldwide.

and regarding your question, they find this:

Whereas some are highly productive in terms of publications, the
  majority of authors published only a single paper, which suggests
  significant turnover in the community.

You should have a closer look at it (although polsci is not in your list of mentioned disciplines)

Papers on coauthorship networks such as Newman (2004) have the descriptive statistics on publication rates in different fields, e.g. publications per person per year.


text:
How do I find research groups that do work in field X?
What is the best way to go about to find a certain research group that do work in a specific field (e.g research groups doing empirical brain investigations but working from a dualistic perspective, or research groups doing eyetracker work on infants)?

Um... did you try Google?  (In computer science, it would unthinkable for a faculty member not to have a web page advertising their group's research results, usually with pointers to local copies of their papers.  This is a comment instead of an answer because it looks like you're in a different field.)

Well, I find reasonable numbers for research groups focusing on eyetracking by using Google and search operators. Of course, you should add some redundant similar terms (eye-movement, baby,...)
Notice, there are some patterns:

american universities nearly always have edu (actually it's even a domain) in their URL, german univ. uni. So using inurl:edu in google filters out a lot. Non-university institutes like german Max-Planck often have URL patterns too.
further add  -filetype:pdf -filetype:doc -filetype:ppt -filetype:ps to filter out more useless results
add 2010..2012 to be sure the site/group is still active and the topic on their agenda.
add research | forschung (latter being german translation, but afaik nowadays most natural sciences groups in Germany have a english (& german) page)

Some research branches also have a online directory, there exist also internet directories like dmoz (not sure if this stuff is up to date, probably some dead links):
http://www.eurosys.org/directory/
http://www.dmoz.org/Science/Biology/Neurobiology/Research_Groups_and_Centers/
http://www.ida.liu.se/ext/etai/actions/colloq/groups.html
At least I can say that most research groups in Germany will have a english home page and short summary/research topics/open positions on it. So there should be no general problem to find them by some "serious" googling. But don't use too specific keywords, "eyetracking on infants", "dualistic view" is too special imho, use keywords being specific rather to the topic than the exact methodology. They are probably mainly interested in how the visual recognition system adapts and learns over time, this is the bigger thematic picture. You attract master and phd students not by naming a special experimental method, so you will not find these type of keywords often on a group page, where they often try to put in a minimum of time.

I've nearly forgotten that you actually can make more specific Google searches than just typing in a bunch of words and hope for the best. This is some great advice, thanks!

@Speldosa googling is a art & science but imho the most important soft skill for todays students with growing redundancy of online contents, but there is light at the end of the tunnel http://www.googleguide.com/advanced_operators_reference.html Take a look at the websites of some research groups, there are more patterns, like often links named jobs, positions, people, staff. Include this in your search. Know the operators AND use them.

Thanks for the link! To learn to google things more efficiently will be my project this week!

Note: Just using the "uni" designation would exclude most of the TH's and TU's in Germany—which are the schools most likely to be doing the kinds of research the original poster might be interested in! Also, if there's no English-language version of the homepage, you'd need to search for the German-language equivalents of the search terms.

@aeismail Correct. To my knowlegde TH/U's focus more on applied sciences and engineering, less on fundamental research (here imo the case) in medicine/biology/neuroscience. But I mainly tried to show OP some patterns he should look for in the countries of his choice, I cannot cover all countries here. Helmholtz, Max Planck, Leibniz... also have different URL patterns. The group websites are in natural sciences afaik mostly english, as one of the main purposes of this sites is to attract master/phd students. For humanities in Germany I would rather use german search keywords.

I would try the following, in order of "how useful the results will be":

If you know someone - anyone - in the field, have them recommend names of labs/professors to you. The names they give you will likely be people relevant to their research who have done solid research, and have really established their names in the field. This is the best approach.
If you have access to journal articles, find a good journal in field X and look for a recent paper. There are two ways to do this one:

Look in a couple of articles that seem interesting and see which authors are cited most often in the "introduction" section. Chances are, those authors have completed some recent seminal work, which all these other papers are using as their research springboard.
Look for a review paper, and see who is cited often. This isn't such a good method, as I've found that many review articles will be fairly biased towards themselves/their collaborators, but it still can be useful.

Go to any big-name university's department web page for field X and browse the faculty listings. This is a total crapshoot; you'll find lots of labs, but there's no surefire way to tell quality of lab from their department web page.

+1 for "see which authors are cited most often in the "introduction" section"

Frequently formalized groups of academics hold conferences, or if it is a more specialized field they are frequently part of a broader conference but have special panels/proceedings/meetings within the larger conference.
So possibilities of finding such groups are;

Looking at the CV of authors in the field to see if they are members of such organizations or have presentations at said conferences.
Looking for conferences in the broader field, and seeing if they host research on specific topics.

Other possibilities include email list-serves and forums. I'm not sure if I have any better advice to find such groups than besides doing regular internet searches though.

One way is to ask ChatGPT. Although its answers are sometimes inaccurate, it can help a lot when your question is a bit vague. It understands what research groups are and it understands the connections between fields.
With ChatGPT you don't need to list all the different synonyms for your field or their subfields and it can choose to ignore some parts of your query if they're too specific.
For my own search I've seen that out of the 10 suggestions it gave me,
2 were misnamed or didn't exist, but the rest all seemed to be very good suggestions.
Of course this is just a tool and you should never rely on it too heavily, but it can help you find research groups which are a good fit for what you're looking for. Also note that ChatGPT was trained on data from 2020, so it might be outdated.
Here's an example output of ChatGPT for your query:

Can you list several research groups doing empirical brain investigations but working from a dualistic perspective, or research groups doing eye-tracker work on infants.
I can certainly provide you with some examples of research groups that fit your criteria:

The Center for Mind, Brain, and Culture at Emory University: This interdisciplinary research center includes researchers who work from a dualistic perspective to investigate the relationship between the mind and brain. They use a variety of empirical methods, including neuroimaging and behavioral experiments.

The Human Neuroscience Laboratory at the University of Utah: This lab conducts research on the neural mechanisms underlying human perception and cognition. They use both behavioral and neuroimaging techniques, including fMRI and EEG, to investigate these mechanisms from a dualistic perspective.

The Center for Cognitive Science at the University of Minnesota: This interdisciplinary center includes researchers from psychology, philosophy, linguistics, and neuroscience. Some of their work investigates the mind-body problem from a dualistic perspective, using neuroimaging and behavioral experiments.

The Infant Cognition Center at the University of British Columbia: This research group investigates the cognitive and perceptual abilities of infants using a variety of methods, including eye-tracking. They have published several studies on infant perception of social cues and facial expressions.

The Developmental Neuroscience Lab at the University of California, Berkeley: This lab uses a variety of methods, including eye-tracking, to investigate the neural and cognitive mechanisms underlying developmental processes in infants and young children.

I hope this helps! Please note that this is not an exhaustive list, and there are many other research groups out there conducting similar work.

If you know specific search terms you're looking for, you could begin by searching Web of Science for the specific combinations you're looking for, and then following up on the specific groups that are returned by such a search.
You may need to use some creativity in narrowing down the search criteria to avoid getting 5000 hits that you need to sift through, but there are a large number of possible "narrowing" options on Web of Science (year of publication, location, sorting by citations, etc.) that can help you.
Alternatively, sites like academia.edu or even a more generic engine like Scholar Google may be able to assist you in this process.

I agree. Only that Scholar Google is not one of the best options given all the noise that it pro cues. Sometimes Scopus works much better than WOS

I found one possibly relevant result searching Mendeley for your topic of interest. The advantage of using Mendeley over Google is that you can limit your search to researcher profiles. On-site people search is a little broken sometimes, so you might have better luck actually doing a Google search but limiting it to Mendeley profiles like this: https://encrypted.google.com/search?q=eyetracking+site%3Amendeley.com%2Fprofiles%2F


text:
Better chances for math grad school: graduate in three years or four?
I finished freshman year of college and I am transferring from a liberal arts college to a university to study a more rigorous math curriculum. I got accepted at UC berkeley (but as a junior transfer, so I can only be in college for 2 more years). I'm going to hear back from stanford, cornell, and colubmia next week. I would choose stanford over berkeley if I got in, but I'm not sure about the other schools since berkeley is a top ranked math school.
-My question is, if I get into columbia or cornell, should I go and be in college for 3 years, or go to berkeley for only 2 years? Is it better to stay in college for 3 years to be more competitive?
Background: I took  real analysis, topology, complex analysis, algebra 2, linear programming, cryptography. I'm self studying algebraic topology and homological algebra, but recently I'm studying probabilistic methods and graph theory to prepare for an REU this summer. I'm interested in anything algebra related.
Why I am worried: I looked over mathematicsgre.com, a forum where grad school applicants post their stats and results. People who get into top math schools usually have taken 10+ grad level classes and did research, REUs, and (probably) have  great letters of rec. If I go to berkeley, I only have 2 years, actually less since I would apply to grad school as a senior so I only have full 1 year and a few months.

Wouldn't your studies at the other schools also be limited to 2 years? Or, why do you get an extra year at Stanford/Cornell that you don't get at Berkeley?

I have enough credits to be considered as a junior at Berkeley, so I only have enough financial aid for 2 years. I guess my studies could be limited to 2 years at other places, but I'm not quite sure (I have to see what credits transfer, as well as the financial aid package.) I could get up to 3 years at other colleges but it has to be 2 at berkeley.

Find some required class that you need to graduate, and then don't take it until you're ready.

What jumps out at me in your question is the assumption that because of your advanced standing you can only stay at Berkeley for two years and thus only spend three years in college altogether.  Though I do not have any direct experience with this (i.e., financial aid at state universities in California), I find that quite surprising.  Berkeley is an elite institution, and presumably they don't let just anyone transfer in.  The fact that you have two years' worth of university credit after one year in college is to your credit and probably factored into their decision to accept you.  So they turn around and penalize you by only offering you two years of financial aid?  That doesn't make much sense.
I would at least make a phone call and, if necessary, schedule an in-person appointment with a financial aid officer.  My first guess, honestly, is that you may not be understanding the situation correctly.  If you are, you need to explain why the junior standing could stop you from making best use of the amazing resources that UC Berkeley has to offer and could make you less competitive in your later academic plans.  I would expect them to be sympathetic to that.
On the other hand, I find your discussion of what it takes to get into a top mathematics program (here and in the other question you asked) a bit reductive.  It is not simply a matter of taking the most graduate courses, doing multiple REUs (in my opinion as someone who was involved in graduate admissions in my math department, one REU has the same effect as multiple REUs unless you do some truly notable research in one of the REUs, which is unusual; also, doing multiple REUs makes it natural for you to get more than one recommendation letter from an REU director, and this is a mistake: most REU letters sound the same no matter who is writing them or is being written about), and so forth: the goal that you rather want to pursue is to show mastery of mathematics and show the potential and the interest in doing mathematical research.  You can show this by taking 5 graduate courses rather than 10.  (Ten courses sounds almost ridiculously high, by the way: I took 9 trimester graduate courses -- so the equivalent of 6 semester courses -- over the last two years of my undergraduate program.  I got into all the top mathematics departments.  If I had taken a few courses fewer I don't think the outcome would have changed.)
In fact, the list of math courses that you've already taken compares well with what very strong undergraduates take up through the end of their second year in top mathematics programs in the US.  If you did really well with them, then I think you would be ready to take graduate courses (what other undergraduate courses would you take?) in your next year and thus as far as I can see you could graduate in three years and still be competitive for a top program.  But do you really have to?  If you are serious about studying mathematics, then you have the entire rest of your life to do that.  I would recommend a more balanced undergraduate experience that is not 100% calculated to optimize the graduate program you can get into and which lasts for the traditional four years: there are other interesting courses to take as an undergraduate which you will never take again, and there are other things to do with one's undergraduate life aside from coursework.  Don't get shortchanged on your undergraduate experience.

To echo Pete, I took 10 graduate courses and three years of research, but did not get into the top universities for graduate schools for math. It matters who is writing your letters, how much they know about you, and what your math GRE scores are. Pete's right though with balancing things out. Have a nice undergraduate experience and not worry about grad school now. You may burn yourself out.

@TK: It also matters a lot what your undergrad institution is.  At a top department like Berkeley, their "honors" undergrad courses will be more similar to basic graduate courses at a place like UGA than to undergrad courses for majors at UGA.  Rattling off a list of courses does not tell this story: the difference between two courses called "real analysis" could be all the difference between getting into a top program or not.  P.S.: You did okay.

Another point to make is not getting into a top grad program is not the end of the world. :-)

Well, the term "top grad program" is nebulous.  Sometimes it is used to mean "Berkeley, Harvard, Princeton" -- the ones which always come out at the very top of the list, in various orderings.  (Although this year's USNews puts MIT at the top!)  In terms of admissions goals, I would say that shooting for a top 20 program is more qualitatively meaningful -- so, as I said, TK did okay -- and that it is worth thinking twice about accepting an offer outside of the top 75 or so.

Thanks for the answer! The reason I said I have only 2 years in Berkeley is that my financial aid is limited to 2 years, and they expect me to graduate on time. I'll call them and ask, but I don't think I can get an appeal.


text:
How can I cite the same reference in successive sentences?
I've just read a paper that cited the same reference in two successive sentences:

This is the first sentence (xxxx 2013). This is the second sentence
  (xxxx 2013).

Up until now, I would have cited the reference just once, like this:

This is the first sentence. This is the second sentence (xxxx 2013).

Which method is correct?

"This is the first sentence (xxxx 2013). This is the second sentence."

Wouldn't the term ibid apply? "This is the first sentence (xxxx 2013). This is the second sentence (ibid)." Or is ibid used only in end-notes and footnotes?

Ibid. is only used in certain styles of footnotes and endnotes. It is not used when directly citing works in the text (in MLA style, for instance).

I have used loc. cit. in situations like these.

In such situations, if it can be done, I'd just qoute the source rather than cite to it. Depends on what's easier.

In general terms, the reference should be made where the cited information occurs. If you cite in the second it is not clear from where the information in the first originates. A similar problem occurs if you cite an entire paragraph by adding a reference at the end of a paragraph ass "(Xxxx, 2013)" (I am fully aware that this is the norm in some fields).
Citing the same reference in two sentences is clearly wrong. The solution as I see it is to write the sentences so that it is clear they belong together. There are several ways to do this. One way is to avoid the passive, parenthetical, reference and use the active reference where only the year is in parenthesis. As an example, you can start the first sentence by stating "Xxx (2013) states ..." and then in the second say "They furthermore  ...". In this example we provide a bridge between the two sentences so that it is very clear it is the same reference that applies. Instead of "They" you can also use "Xxxx".
There are clearly numerous ways to bridge sentences so the form depends on what you need to say. As a result I would recommend putting the reference in the first sentence, not the second.

Nice answer. Though I could imagine a situation where it is not "clearly wrong" to cite the same paper in two adjoining sentences because, e.g., you cite the same paper in two clusters of references like: yadda yadda yadda (xx1 2011; xx2 2011; xx3 2013). yadda yadda yadda (xx1 2011).

+1 - As a general point, many citation questions seem to assume that the text cannot be changed, only the citations, when the citation issue could be more easily addressed by editing the text itself. Compare http://rasmussen.libanswers.com/a.php?qid=107534

@OswaldVeblen: I think that assumption is simply based on the issue that changing the text may fix the citation, but break something else at the same time. Maybe the text ended up exactly like it is because every synonymous version would not sound good in the complete context (reptitions of words etc.). Note that incidentally, in the example you linked to, the "good" version is also the longest one - just by one line, but one line can make a page count difference, and also, lines add up -, and space in a paper can be (depending on the field) a very sparse resource to save at all cost.

@O.R.Mapper: every synonymous version? We're talking about a natural language here. Sometimes - in fact, often - the best way to fix a problematic paragraph is to delete it and write it again.  Putting in citations as you go, and planning ahead for them, is key to good writing. "Avant donc que d’écrire, apprenez à penser." (Boileau 1674)

@OswaldVeblen: Well, sure - and, as you go ahead in a systematical way, you usually try to consider all possible ways to express the intended statements. Some of those synonymous ways have to be ruled out because they don't fit in the context, and others remain. And sometimes, only one such version remains. The point is that citations do not need to "flow" grammatically in the sentence, and thus are still the most flexible part of the text to change, even when the sentences themselves are already restricted to a particular version based on the surrounding text or by the layout.

Neither is correct, it is a matter of style.
Refer to the style guide of the journal, publishing house or conference that you're writing for.

If the style guide does not provide specific instruction for that matter, what then? Neither is correct or neither is incorrect?

Where style guides are silent, it is normally a matter for academic judgement.  I would suggest that you either cite by sentence or by paragraph; rather than citing "all sentences prior to this citation."  I come from a footnoting field though, your field will vary.

I disagree. In the second example the first sentence isn't properly cited (given only the info provided).

@KennyPeanuts: the question of what counts as "proper" citation varies greatly depending on the field of study. In my field, it would be unusual to cite the same source more than once in a paragraph.

This is exactly what the abbreviation "Ibid." is used for:

This is the first sentence (Xxxx, 2013). This is the second sentence (Ibid.).

It derives from the latin word "ibidem", which means "in the same place".
Source: https://en.wikipedia.org/wiki/Ibid.
Edit: Disclaimer
Following the comment discussion below this answer, I would like to state clearly that the usage of "Ibid." is highly dependent on the field of study and the general citation style you are using. If you have never encountered this abbreviation before in your field of study, you should probably not start using it.

In the Wiki page you linked: was cited in the preceding endnote or footnote. Are you sure your answer is correct?

Quoting from a bit further downstream of the article: "Ibid. may also be used in the Harvard (name-date) system for in-text references where there has been a close previous citation from the same source material.". So, since the OP seems to be using Harvard-style references, I guess the answer is yes.

The style manual determines whether "ibid." can be used. APA parenthetical citations look identical, but do not allow "ibid." http://blog.apastyle.org/apastyle/2014/07/does-apa-style-use-ibid.html

As Oswald Veblen correctly points out, whether you actually do want to use "Ibid." depends on your academic discipline. Different academic fields use very different citation styles, and one should certainly stick with those conventions intrinsic to one's field of study. However, without knowing the field the OP is coming from, it's hard to give a definite answer. I was just stating that it is possible to use "Ibid." in such a case.

I've yet to see ibid. in Computer Science papers. I'd say your answer is misleading without some proper disclaimer.

Following Blaisorblades valid argument, I've added a disclaimer to my answer.

APA - Documentation does not need to be repeated for every idea within a single paragraph.  For example, if you retrieved information for three consecutive sentences from the same source, you can put the information after the third sentence.

What I would do in this case depends on whether you're citing two different claims/results or just two pieces of text within that paper related to the same claim/idea/point.

If it's two different results, definitely cite them separately, regardless of whether the citations are closeby or not; and I would make an effort to indicate, with each citation, the exact location of the specific claim/point, so it would be clear to the reader that these are two distinct claims. (If you're using LaTeX, it would look something like\cite[\S 1.2]{ThatXXXPaper} and \cite[Appendix B]{ThatXXXPaper}.)
If it's the same result/claim/point, and you're just citing the continuation of the text, take the advice in other answers, i.e.:

It may depend on the stylistic conventions in your field
It may depend on the stylistic conventions of the conference/journal to which you're submitting the paper, or your university's regulations if it's a thesis
You might want to use "ibid." (ibidem) instead of repeating the citation
You might be able to cite just once at the end of a paragraph (assuming that doesn't create ambiguity)
You might want to avoid the second citation by appropriate rephrasing as  @PeterJanson suggests.

I think if you are writing something that refers to several sources repeatedly maybe you should use a different referencing system. Maybe use superscript numbers like the Vancouver referencing system.
I assume this is a problem more likely to be faced when writing a cohort / review paper.


text:
Construct a network of citations
I had the thought that I should run a computation on my document to see things like: How many times I cite each source, how many times I cite each author etc.
Then I thought that isn't enough.
What if I cite A_foo, B_foo and C_foo, but both A_foo and B_foo cite C_foo.
That is worth knowing.
A graph of citations between the texts that I am citing will revel interesting information.
For example nodes with out any parent, will generally indicate new and original ideas. While they may draw on other things, those things are distant enough from  my work that I am not citing them.
Nodes that have no links to any others, show that I am bringing in an idea from another subfield, perhaps.
This graph should be computable, I know google scholar maintains a list of almost everything I've cited, and for each thing lists who has cited them.
Has this been experimented with? Are there existing tools for the job?
If there isn't I might mess around with some python and put something together.
My references are all in bibtex.
I would like to construct a graph of the citations.

Possible Duplicate: http://academia.stackexchange.com/questions/13329/map-tree-of-citations-references?rq=1

Strangely enough, this question was asked on SO a while back with no real good answers. There was a link to this question, which links to PaperScope, which looks promising, although there hasn't been activity on the project since April 2013. I've never used it that tool.
Microsoft's Visual Explorerisn't exactly what you're looking for, but it's related. Hubmed used to have a graph view but I can't find it now, maybe you can.
The problem will be that even going just two nodes out - your citations, and your citations' citations - will include thousands of papers from hundreds of authors. As discussed in the aforelinked SO and SU questions, simply choosing an appropriate way to display the data will be a non-trivial challenge, and making useful inferences from the results will probably be even more difficult. Still, it may be interesting. Good luck!

In terms of collecting data from google scholar it does not appear to be very easy - no API last I knew. I've seen other examples where researchers say they download articles from Web of Science and then create the network (see an example graph here).

I don't have access to Web of Science though, so it is unclear what download the articles means exactly (if it involves cleaning out citations from PDFs then that sounds like a difficult task, although from the description it may come as more structured than that).

"The problem will be that even going just two nodes out - your citations, and your citations' citations - will include thousands of papers from hundreds of authors"
I only want my citation's citation's If they are also by direct citations.
This cuts the nodes right down.